From a4fcfa1a344c2a45ca9abd133abecb6a058469b9 Mon Sep 17 00:00:00 2001
From: "sumit shinde ( Roni )" <shindesumit217@gmail.com>
Date: Tue, 11 Jun 2024 21:49:08 +0530
Subject: [PATCH 01/13] blog : Using Node-RED as an ETL tool

---
 .../etl-with-node-red-chart-based-on-age.png  | Bin 0 -> 25369 bytes
 ...th-node-red-chart-based-on-job-profile.png | Bin 0 -> 67476 bytes
 .../2024/06/using-node-red-as-an-etl-tool.md  | 224 ++++++++++++++++++
 3 files changed, 224 insertions(+)
 create mode 100644 src/blog/2024/06/images/etl-with-node-red-chart-based-on-age.png
 create mode 100644 src/blog/2024/06/images/etl-with-node-red-chart-based-on-job-profile.png
 create mode 100644 src/blog/2024/06/using-node-red-as-an-etl-tool.md

diff --git a/src/blog/2024/06/images/etl-with-node-red-chart-based-on-age.png b/src/blog/2024/06/images/etl-with-node-red-chart-based-on-age.png
new file mode 100644
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diff --git a/src/blog/2024/06/using-node-red-as-an-etl-tool.md b/src/blog/2024/06/using-node-red-as-an-etl-tool.md
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+---
+title: Using Node-RED as an ETL Tool
+subtitle: A Step-by-Step Guide on How to Use Node-RED as an ETL Tool
+description: Learn how to utilize Node-RED as an ETL (Extract, Transform, Load) tool, and explore its advantages and disadvantages in this context.
+date: 2024-06-05
+authors:["Sumit Shinde"]
+image: 
+tags:
+  - etl
+  - extract
+  - transform
+  - load
+  - analysis
+  - business intelligence
+---
+
+ETL is essential for data integration and analysis, playing a crucial role in many business contexts by helping to gain detailed business insights. Node-RED, known for its ease in creating IoT applications, has also been recognized for its potential in ETL tasks. When IBM published its blog on using Node-RED for ETL, the topic garnered significant attention and sparked discussions on whether it could be effectively utilized for ETL. In this guide, we'll explore how to use Node-RED for ETL, discussing both its strengths and weaknesses along the way.
+
+<!--more-->
+
+## What is ETL
+
+[ETL](https://en.wikipedia.org/wiki/Extract,_transform,_load) (Extract, Transform, Load) is a fundamental process in data integration and data warehousing. It encompasses three main stages: extracting data from various sources, transforming it into a format suitable for analysis, and loading it into a target database or data warehouse. This process allows businesses to consolidate data from multiple sources, ensuring it is clean, consistent, and ready for generating insights and making informed decisions.
+
+## Node-RED as ETL a tool
+
+Node-RED is specifically used for building various types of IoT applications. During the development of such applications, Node-RED is sometimes used as an ETL (Extract, Transform, Load) tool without even realizing it. For instance, when monitoring sensors across a facility, the process involves extracting data from sensors, transforming (cleaning and formatting) sensor data, and loading it into a database. There are thousands of other examples like this. This demonstrates that Node-RED has already been adopted as an ETL tool for certain scenarios.
+
+### Extracting
+
+Node-RED can extract data from various sources, including APIs, databases, local filesystems, and IoT devices using built-in nodes and community-contributed nodes. For example, the HTTP request node can be used to pull data from web services, while nodes for MySQL, MongoDB, and PostgreSQL can extract data from databases. Nodes for MQTT and Kafka can fetch data from message brokers. Additionally, there are cloud nodes such as AWS, Google Cloud Platform (GCP), and IBM Watson that allow extraction of data from cloud storage services.
+
+### Transforming
+
+Transformation in Node-RED involves converting data into the desired format, applying business rules, and cleaning it. This can be done using function nodes for custom JavaScript code, along with nodes like JSON, split, join, change CSV, and XML for parsing and formatting.
+
+### Loading
+
+The final step in the ETL process is loading the transformed data into a target system, such as a database or data warehouse. Node-RED supports various output nodes to send data to different destinations. For instance, you can use the MySQL node to insert data into a MySQL database, or the HTTP request node to send data to a web service endpoint.
+
+### Visualizing and Analyzing
+
+In addition to the core ETL (Extract, Transform, Load) process, visualizing and analyzing data is a crucial step for gaining insights. While visualization isn't part of the ETL acronym itself, it plays a significant role in understanding and interpreting transformed data. In Node-RED, you can easily visualize data using Node-RED Dashboard 2.0 charts.
+
+Node-RED Dashboard 2.0 provides several chart options, and more are expected in the future. For more information and to explore available chart types, refer to the [Node-RED Dashboard 2.0 charts documentation](https://dashboard.flowfuse.com/nodes/widgets/ui-chart.html).
+
+*Note: We have mentioned only a few nodes that can be used in the ETL process, but there are thousands of other nodes that you can explore yourself by visiting the [Node-RED Nodes Library](https://flows.nodered.org/search?type=node)*
+
+## Building a Simple ETL Project Using Node-RED
+
+Let's walk through a simple project where we use Node-RED as an ETL tool to extract sample customer data from an API, transform it, and then load cleaned data into a MongoDB database and process data into a local file.
+
+*Note: The goal of this project is to understand how to utilize Node-RED as an ETL tool. We assume that the reader has a basic knowledge of Node-RED.*
+
+## Prerequisite 
+
+Before proceeding further make sure you have installed the following nodes.
+
+1. [node-red-contrib-mongodb4](https://flows.nodered.org/node/node-red-contrib-mongodb4)
+2. [@flowfuse/node-red-dashboard](https://dashboard.flowfuse.com/)
+
+## Extracting Data
+
+1. Drag an Inject node onto the canvas.
+2. Drag an HTTP Request node onto the canvas, double-click on it, and set the URL to `https://api.slingacademy.com/v1/sample-data/files/customers.json`.
+3. Drag a Link Out node onto the canvas.
+4. Connect the Inject node's output to the input of the HTTP Request node, and the HTTP Request node's output to the input of the Link Out node.
+
+Now, if you add a Debug node after the HTTP Request node, you will see an array of objects containing customer data in the Debug panel. Each object has properties such as `first_name`, `last_name`, `email`, and others.
+
+Next, we'll perform a transformation to replace the existing properties `first_name` and `last_name` with a single property `full_name` that concatenates them. We'll cover this transformation in the next section.
+
+## Transforming Data
+
+### Data Cleaning
+
+1. Drag a Link In node onto the canvas, double-click on it, and select the name of the Link Out node added in the above section.
+2. Drag a Split node onto the canvas.
+3. Drag a Change node onto the canvas and Set `msg.payload.full_name` to `payload.first_name & " " & payload.last_name` as the JSONata expression, then delete unnecessary properties like `first_name` and `last_name`.
+4. Drag a Join node onto the canvas.
+
+until now we have created a flow that will create a new property in customer data objects called full_name and then it will delete the unnecessary properties. in summary, we have cleaned the data according to our needs.
+
+### Customer Data Deduplication
+
+1. Drag a Change node onto the canvas, and set `msg.data` to `msg.payload` and `msg.payload` to `[]`.
+2. Drag a MongoDB4 node onto the canvas, and configure it with your correct details. If you haven't used MongoDB with Node-RED, please refer to the [Node-RED MongoDB documentation](https://flows.nodered.org/node/node-red-contrib-mongodb4). Enter `find` into the Operation field.
+3. Drag a Function node onto the canvas and insert the following code into it:
+
+```javascript
+// Check if previously stored customer data is present, and ensure it's an array
+let previously_stored_customer_data = Array.isArray(msg.payload) ? msg.payload : (msg.payload ? [msg.payload] : []);
+
+// Ensure latest customer data is an array
+let latest_customer_data = Array.isArray(msg.data) ? Array.from(msg.data) : (msg.data ? [msg.data] : []);
+
+// Check if both previously stored and latest customer data are not empty
+if (latest_customer_data.length !== 0 && previously_stored_customer_data.length !== 0) {
+    // Extract emails from previously stored customer data
+    let emailArray1 = previously_stored_customer_data.map(customer => customer.email);
+
+    // Filter out customers that are already in previously stored data
+    let filtered_customer_data = latest_customer_data.filter(customer => !emailArray1.includes(customer.email));
+
+ msg.payload = filtered_customer_data; 
+    return msg;
+} else {
+    // Case where there's no previously stored data (first ETL cycle after deploying flow)
+ msg.payload = latest_customer_data;
+    return msg;
+}
+```
+
+4. Drag a Link-out node onto the canvas.
+
+Now, we have added some more nodes that perform data deduplication. This means we will create a new array containing unique data that is not already stored in the database to avoid data duplications.
+
+## Data Processing
+
+1. Drag a Function node onto the canvas and insert the following code into it:
+
+```javascript
+let data = msg.payload;
+
+// Initialize counters
+let ageRanges = {
+    "0-20": 0,
+    "21-30": 0,
+    "31-40": 0,
+    "41+": 0
+};
+
+let genderTotals = {
+    male: 0,
+    female: 0,
+    other: 0  // Default to 'other' if gender is not specified
+};
+
+let jobStatusTotals = {};
+
+// Process each document in the data array
+data.forEach(doc => {
+    // Calculate age range
+    let age = parseInt(doc.age);
+    let ageRange;
+    if (age <= 20) {
+        ageRange = "0-20";
+ } else if (age <= 30) {
+        ageRange = "21-30";
+ } else if (age <= 40) {
+        ageRange = "31-40";
+ } else {
+        ageRange = "41+";
+ }
+ ageRanges[ageRange]++; // Increment age range count
+
+    // Count gender
+    let gender = doc.gender || 'other'; // Default to 'other' if gender is not specified
+ genderTotals[gender]++;
+
+    // Count job status
+    let job = doc.job || 'Unknown'; // Default to 'Unknown' if job is not specified
+    if (jobStatusTotals[job]) {
+ jobStatusTotals[job]++;
+ } else {
+ jobStatusTotals[job] = 1;
+ }
+});
+
+// Prepare output payload
+msg.payload = {
+    "Age Ranges": ageRanges,
+    "genderTotals": genderTotals,
+    "JobStatus Totals": jobStatusTotals
+}; 
+
+return msg;
+```
+2. Drag another Link-out node onto the canvas.
+
+Finally, in this phase of transformation, we are tallying the total number of users based on age, gender, and job title. This allows us to quickly analyze and understand our user demographics based on these details so that later we can visualize this data easily.
+
+## Loading data
+
+1. Drag a Link-in node onto the canvas, double-click on it, and select the name of the Link-out node connected to the data deduplication function. Then, drag another Link-in node and connect it to the Link-out node connected to the data processing function node.
+
+2. Drag a MongoDB4 node onto the canvas and configure it correctly. Select the correct collection and enter `insertMany` into the Operation field.
+
+3. Drag a File Write node onto the canvas, set the path, and select the action to "override".
+
+!["Screenshot of dashboard visualizing process customer data based on job profile"](./images/etl-with-node-red-chart-based-on-job-profile.png "Screenshot of dashboard visualizing process customer data based on job profile"){data-zoomable}
+
+!["Screenshot of dashboard visualizing process customer data based on age group"](./images/etl-with-node-red-chart-based-on-age.png "Screenshot of dashboard visualizing process customer data based on age group"){data-zoomable}
+
+
+Following is the complete flow of the project along with dashboard visualization.
+
+{% renderFlow %, 400}
+[{"id":"e85cb581eb249115","type":"group","z":"977143edb097b685","style":{"stroke":"#b2b3bd","stroke-opacity":"1","fill":"#f2f3fb","fill-opacity":"0.5","label":true,"label-position":"nw","color":"#32333b"},"nodes":["218b698ddba2210b","638eddcd2b5f645e","9773152e6a0f2287","fd68d5c864a0e7c9"],"x":114,"y":159,"w":532,"h":182},{"id":"218b698ddba2210b","type":"http request","z":"977143edb097b685","g":"e85cb581eb249115","name":"","method":"GET","ret":"obj","paytoqs":"ignore","url":"https://api.slingacademy.com/v1/sample-data/files/customers.json","tls":"","persist":false,"proxy":"","insecureHTTPParser":false,"authType":"","senderr":false,"headers":[],"x":430,"y":300,"wires":[["fd68d5c864a0e7c9"]]},{"id":"638eddcd2b5f645e","type":"inject","z":"977143edb097b685","g":"e85cb581eb249115","name":"Extract Data","props":[{"p":"payload"},{"p":"topic","vt":"str"}],"repeat":"","crontab":"","once":false,"onceDelay":0.1,"topic":"","payload":"","payloadType":"date","x":230,"y":300,"wires":[["218b698ddba2210b"]]},{"id":"9773152e6a0f2287","type":"comment","z":"977143edb097b685","g":"e85cb581eb249115","name":"Extracting customer data from api","info":"","x":330,"y":200,"wires":[]},{"id":"fd68d5c864a0e7c9","type":"link out","z":"977143edb097b685","g":"e85cb581eb249115","name":"link out 1","mode":"link","links":["0e0129a95e24355c"],"x":605,"y":300,"wires":[]},{"id":"e0adf6e180ec1a7c","type":"group","z":"977143edb097b685","style":{"stroke":"#b2b3bd","stroke-opacity":"1","fill":"#f2f3fb","fill-opacity":"0.5","label":true,"label-position":"nw","color":"#32333b"},"nodes":["ace58c34d1566f51","d2a48c9825b224b4","9cd9d88bb507cc49","ef5e8501b76cfec8","d3a842120a526a7f","6f472c26ef54f974","a88a07f39e0e941d","0e0129a95e24355c","c7150b3b68fc34a0","3f3ad8e528f5800b","dd3fb62233d5477e"],"x":104,"y":379,"w":1342,"h":202},{"id":"ace58c34d1566f51","type":"split","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"","splt":"\\n","spltType":"str","arraySplt":1,"arraySpltType":"len","stream":false,"addname":"","x":250,"y":500,"wires":[["d2a48c9825b224b4"]]},{"id":"d2a48c9825b224b4","type":"change","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"","rules":[{"t":"set","p":"payload.full_name","pt":"msg","to":"payload.first_name & \" \" & payload.last_name","tot":"jsonata"},{"t":"delete","p":"payload.first_name","pt":"msg"},{"t":"delete","p":"payload.last_name","pt":"msg"}],"action":"","property":"","from":"","to":"","reg":false,"x":460,"y":500,"wires":[["ef5e8501b76cfec8"]]},{"id":"9cd9d88bb507cc49","type":"function","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"data processing","func":"let data = msg.payload\n\nlet ageRanges = {\n    \"0-20\": 0,\n    \"21-30\": 0,\n    \"31-40\": 0,\n    \"41+\": 0\n};\n\nlet genderTotals = {\n    male: 0,\n    female: 0,\n    other: 0  // Add other genders as needed\n};\n\nlet jobStatusTotals = {};\n\n// Iterate through each document to calculate counts\ndata.forEach(doc => {\n    // Calculate age range\n    let age = parseInt(doc.age);\n    let ageRange;\n    if (age <= 20) {\n        ageRange = \"0-20\";\n    } else if (age <= 30) {\n        ageRange = \"21-30\";\n    } else if (age <= 40) {\n        ageRange = \"31-40\";\n    } else {\n        ageRange = \"41+\";\n    }\n    // Increment age range count\n    ageRanges[ageRange]++;\n\n    // Increment gender count\n    if (doc.gender === 'male') {\n        genderTotals.male++;\n    } else if (doc.gender === 'female') {\n        genderTotals.female++;\n    } else {\n        genderTotals.other++;\n    }\n\n    // Increment job status count\n    if (jobStatusTotals[doc.job]) {\n        jobStatusTotals[doc.job]++;\n    } else {\n        jobStatusTotals[doc.job] = 1;\n    }\n});\n\n\nmsg.payload = {\n    \"ageRanges\": ageRanges,\n    \"genderTotals\": genderTotals,\n    \"jobStatusTotals\": jobStatusTotals\n}; \n\n// Return the aggregated counts object\nreturn msg","outputs":1,"timeout":0,"noerr":0,"initialize":"","finalize":"","libs":[],"x":980,"y":540,"wires":[["3f3ad8e528f5800b"]]},{"id":"ef5e8501b76cfec8","type":"join","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"","mode":"custom","build":"array","property":"payload","propertyType":"msg","key":"topic","joiner":"\\n","joinerType":"str","accumulate":false,"timeout":"","count":"","reduceRight":false,"reduceExp":"","reduceInit":"","reduceInitType":"","reduceFixup":"","x":690,"y":500,"wires":[["9cd9d88bb507cc49","6f472c26ef54f974"]]},{"id":"d3a842120a526a7f","type":"comment","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"Tranforming customer data","info":"","x":330,"y":420,"wires":[]},{"id":"6f472c26ef54f974","type":"change","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"set payload","rules":[{"t":"set","p":"data","pt":"msg","to":"payload","tot":"msg"},{"t":"set","p":"payload","pt":"msg","to":"[]","tot":"json"}],"action":"","property":"","from":"","to":"","reg":false,"x":970,"y":420,"wires":[["a88a07f39e0e941d"]]},{"id":"a88a07f39e0e941d","type":"mongodb4","z":"977143edb097b685","g":"e0adf6e180ec1a7c","clientNode":"a0c7c3410ffb0e89","mode":"collection","collection":"customer_data","operation":"find","output":"toArray","maxTimeMS":"0","handleDocId":false,"name":"","x":1130,"y":420,"wires":[["dd3fb62233d5477e"]]},{"id":"0e0129a95e24355c","type":"link in","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"link in 1","links":["fd68d5c864a0e7c9"],"x":145,"y":500,"wires":[["ace58c34d1566f51"]]},{"id":"c7150b3b68fc34a0","type":"link out","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"link out 2","mode":"link","links":["78176aee11393705"],"x":1405,"y":420,"wires":[]},{"id":"3f3ad8e528f5800b","type":"link out","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"link out 3","mode":"link","links":["ecfa23782d33d813"],"x":1115,"y":540,"wires":[]},{"id":"dd3fb62233d5477e","type":"function","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"set payload","func":"let previously_stored_customer_data = Array.isArray(msg.payload) ? msg.payload : (msg.payload ? 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msg\n}","outputs":1,"timeout":"","noerr":0,"initialize":"","finalize":"","libs":[],"x":1290,"y":420,"wires":[["c7150b3b68fc34a0"]]},{"id":"a0c7c3410ffb0e89","type":"mongodb4-client","name":"","protocol":"mongodb+srv","hostname":"","port":"","dbName":"mydb","appName":"","authSource":"","authMechanism":"DEFAULT","tls":false,"tlsCAFile":"","tlsCertificateKeyFile":"","tlsInsecure":false,"connectTimeoutMS":"30000","socketTimeoutMS":"0","minPoolSize":"0","maxPoolSize":"100","maxIdleTimeMS":"0","uri":"mongodb+srv://cluster0.uxibwot.mongodb.net","advanced":"{}","uriTabActive":"tab-uri-advanced"},{"id":"ac5e2cabb6251314","type":"group","z":"977143edb097b685","name":"","style":{"label":true},"nodes":["d6a4a19c23a0f42b","78176aee11393705","ecfa23782d33d813","dab90d6302b1c885","bc4afbc715e3b64d","d781431fed20d650","f19a1157e25b47df"],"x":114,"y":619,"w":592,"h":222},{"id":"d6a4a19c23a0f42b","type":"mongodb4","z":"977143edb097b685","g":"ac5e2cabb6251314","clientNode":"a0c7c3410ffb0e89","mode":"collection","collection":"customer_data","operation":"insertMany","output":"toArray","maxTimeMS":"0","handleDocId":false,"name":"","x":370,"y":720,"wires":[["bc4afbc715e3b64d"]]},{"id":"78176aee11393705","type":"link 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+{% endrenderFlow %}
+
+## What are the limitations of using ETL as an ETL tool
+
+While Node-RED is versatile, there are some limitations to consider:
+
+- **Scalability:** Node-RED might struggle with extremely large datasets compared to dedicated ETL tools.
+- **Performance:** For very complex transformations or high-volume data, performance might not match that of specialized ETL solutions.
+- **Advanced Features:** Some advanced ETL features, like automated schema detection and sophisticated error handling, might require additional customization or external modules.
+- **Data Governance:** Node-RED does not inherently provide robust data governance and lineage tracking which are often essential in enterprise ETL tools.
+
+## Benefits of using Node-RED as an ETL tool.
+
+Despite its limitations, Node-RED offers several benefits:
+
+1. **Ease of Use:** Its visual programming interface makes it accessible to non-developers.
+2. **Flexibility:** A wide range of nodes and the ability to write custom JavaScript allow for flexible data processing.
+3. **Integration:** Strong support for IoT and real-time data processing, making it ideal for integrating diverse data sources.
+4. **Cost-Effective:** Being open-source, Node-RED can be a cost-effective alternative to expensive ETL tools.
+5. **Community Support:** A large community provides a wealth of nodes, examples, and support.
+
+## Additional resources
+
+- [Implementing ETL flows with Node-RED](https://developer.ibm.com/articles/implementing-etl-flows-with-node-red/): Read this IBM blog where a team member explains how they replaced a dedicated ETL tool with Node-RED.
+- [Node-RED Forum Thread](https://discourse.nodered.org/t/etl-pipelines-with-node-red/): Explore this Node-RED forum thread where community members discussed how to utilize Node-RED as an ETL tool, including solutions for handling large datasets.
\ No newline at end of file

From c94a79eb86feed8b096a09df2f0867eb56086206 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]"
 <41898282+github-actions[bot]@users.noreply.github.com>
Date: Tue, 11 Jun 2024 16:21:42 +0000
Subject: [PATCH 02/13] Optimised images with calibre/image-actions

---
 .../etl-with-node-red-chart-based-on-age.png  | Bin 25369 -> 23986 bytes
 ...th-node-red-chart-based-on-job-profile.png | Bin 67476 -> 61432 bytes
 2 files changed, 0 insertions(+), 0 deletions(-)

diff --git a/src/blog/2024/06/images/etl-with-node-red-chart-based-on-age.png b/src/blog/2024/06/images/etl-with-node-red-chart-based-on-age.png
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From 94eaa1fa6fa42a4583930b4bb1d893527377bb29 Mon Sep 17 00:00:00 2001
From: "sumit shinde ( Roni )"
 <110285294+sumitshinde-84@users.noreply.github.com>
Date: Tue, 11 Jun 2024 21:51:58 +0530
Subject: [PATCH 03/13] Update using-node-red-as-an-etl-tool.md

---
 src/blog/2024/06/using-node-red-as-an-etl-tool.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/blog/2024/06/using-node-red-as-an-etl-tool.md b/src/blog/2024/06/using-node-red-as-an-etl-tool.md
index 310b750320..4e4b100091 100644
--- a/src/blog/2024/06/using-node-red-as-an-etl-tool.md
+++ b/src/blog/2024/06/using-node-red-as-an-etl-tool.md
@@ -3,8 +3,8 @@ title: Using Node-RED as an ETL Tool
 subtitle: A Step-by-Step Guide on How to Use Node-RED as an ETL Tool
 description: Learn how to utilize Node-RED as an ETL (Extract, Transform, Load) tool, and explore its advantages and disadvantages in this context.
 date: 2024-06-05
-authors:["Sumit Shinde"]
-image: 
+authors: ["sumit-shinde"]
+image:
 tags:
   - etl
   - extract
@@ -221,4 +221,4 @@ Despite its limitations, Node-RED offers several benefits:
 ## Additional resources
 
 - [Implementing ETL flows with Node-RED](https://developer.ibm.com/articles/implementing-etl-flows-with-node-red/): Read this IBM blog where a team member explains how they replaced a dedicated ETL tool with Node-RED.
-- [Node-RED Forum Thread](https://discourse.nodered.org/t/etl-pipelines-with-node-red/): Explore this Node-RED forum thread where community members discussed how to utilize Node-RED as an ETL tool, including solutions for handling large datasets.
\ No newline at end of file
+- [Node-RED Forum Thread](https://discourse.nodered.org/t/etl-pipelines-with-node-red/): Explore this Node-RED forum thread where community members discussed how to utilize Node-RED as an ETL tool, including solutions for handling large datasets.

From bed67501c7c337ae8c320eb7bebe7d3e9afb2608 Mon Sep 17 00:00:00 2001
From: "sumit shinde ( Roni )"
 <110285294+sumitshinde-84@users.noreply.github.com>
Date: Tue, 11 Jun 2024 21:55:11 +0530
Subject: [PATCH 04/13] Update using-node-red-as-an-etl-tool.md

---
 src/blog/2024/06/using-node-red-as-an-etl-tool.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/blog/2024/06/using-node-red-as-an-etl-tool.md b/src/blog/2024/06/using-node-red-as-an-etl-tool.md
index 4e4b100091..0ada8c022a 100644
--- a/src/blog/2024/06/using-node-red-as-an-etl-tool.md
+++ b/src/blog/2024/06/using-node-red-as-an-etl-tool.md
@@ -195,7 +195,7 @@ Finally, in this phase of transformation, we are tallying the total number of us
 
 Following is the complete flow of the project along with dashboard visualization.
 
-{% renderFlow %, 400}
+{% renderFlow %}
 [{"id":"e85cb581eb249115","type":"group","z":"977143edb097b685","style":{"stroke":"#b2b3bd","stroke-opacity":"1","fill":"#f2f3fb","fill-opacity":"0.5","label":true,"label-position":"nw","color":"#32333b"},"nodes":["218b698ddba2210b","638eddcd2b5f645e","9773152e6a0f2287","fd68d5c864a0e7c9"],"x":114,"y":159,"w":532,"h":182},{"id":"218b698ddba2210b","type":"http request","z":"977143edb097b685","g":"e85cb581eb249115","name":"","method":"GET","ret":"obj","paytoqs":"ignore","url":"https://api.slingacademy.com/v1/sample-data/files/customers.json","tls":"","persist":false,"proxy":"","insecureHTTPParser":false,"authType":"","senderr":false,"headers":[],"x":430,"y":300,"wires":[["fd68d5c864a0e7c9"]]},{"id":"638eddcd2b5f645e","type":"inject","z":"977143edb097b685","g":"e85cb581eb249115","name":"Extract Data","props":[{"p":"payload"},{"p":"topic","vt":"str"}],"repeat":"","crontab":"","once":false,"onceDelay":0.1,"topic":"","payload":"","payloadType":"date","x":230,"y":300,"wires":[["218b698ddba2210b"]]},{"id":"9773152e6a0f2287","type":"comment","z":"977143edb097b685","g":"e85cb581eb249115","name":"Extracting customer data from api","info":"","x":330,"y":200,"wires":[]},{"id":"fd68d5c864a0e7c9","type":"link out","z":"977143edb097b685","g":"e85cb581eb249115","name":"link out 1","mode":"link","links":["0e0129a95e24355c"],"x":605,"y":300,"wires":[]},{"id":"e0adf6e180ec1a7c","type":"group","z":"977143edb097b685","style":{"stroke":"#b2b3bd","stroke-opacity":"1","fill":"#f2f3fb","fill-opacity":"0.5","label":true,"label-position":"nw","color":"#32333b"},"nodes":["ace58c34d1566f51","d2a48c9825b224b4","9cd9d88bb507cc49","ef5e8501b76cfec8","d3a842120a526a7f","6f472c26ef54f974","a88a07f39e0e941d","0e0129a95e24355c","c7150b3b68fc34a0","3f3ad8e528f5800b","dd3fb62233d5477e"],"x":104,"y":379,"w":1342,"h":202},{"id":"ace58c34d1566f51","type":"split","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"","splt":"\\n","spltType":"str","arraySplt":1,"arraySpltType":"len","stream":false,"addname":"","x":250,"y":500,"wires":[["d2a48c9825b224b4"]]},{"id":"d2a48c9825b224b4","type":"change","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"","rules":[{"t":"set","p":"payload.full_name","pt":"msg","to":"payload.first_name & \" \" & payload.last_name","tot":"jsonata"},{"t":"delete","p":"payload.first_name","pt":"msg"},{"t":"delete","p":"payload.last_name","pt":"msg"}],"action":"","property":"","from":"","to":"","reg":false,"x":460,"y":500,"wires":[["ef5e8501b76cfec8"]]},{"id":"9cd9d88bb507cc49","type":"function","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"data processing","func":"let data = msg.payload\n\nlet ageRanges = {\n    \"0-20\": 0,\n    \"21-30\": 0,\n    \"31-40\": 0,\n    \"41+\": 0\n};\n\nlet genderTotals = {\n    male: 0,\n    female: 0,\n    other: 0  // Add other genders as needed\n};\n\nlet jobStatusTotals = {};\n\n// Iterate through each document to calculate counts\ndata.forEach(doc => {\n    // Calculate age range\n    let age = parseInt(doc.age);\n    let ageRange;\n    if (age <= 20) {\n        ageRange = \"0-20\";\n    } else if (age <= 30) {\n        ageRange = \"21-30\";\n    } else if (age <= 40) {\n        ageRange = \"31-40\";\n    } else {\n        ageRange = \"41+\";\n    }\n    // Increment age range count\n    ageRanges[ageRange]++;\n\n    // Increment gender count\n    if (doc.gender === 'male') {\n        genderTotals.male++;\n    } else if (doc.gender === 'female') {\n        genderTotals.female++;\n    } else {\n        genderTotals.other++;\n    }\n\n    // Increment job status count\n    if (jobStatusTotals[doc.job]) {\n        jobStatusTotals[doc.job]++;\n    } else {\n        jobStatusTotals[doc.job] = 1;\n    }\n});\n\n\nmsg.payload = {\n    \"ageRanges\": ageRanges,\n    \"genderTotals\": genderTotals,\n    \"jobStatusTotals\": jobStatusTotals\n}; \n\n// Return the aggregated counts object\nreturn msg","outputs":1,"timeout":0,"noerr":0,"initialize":"","finalize":"","libs":[],"x":980,"y":540,"wires":[["3f3ad8e528f5800b"]]},{"id":"ef5e8501b76cfec8","type":"join","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"","mode":"custom","build":"array","property":"payload","propertyType":"msg","key":"topic","joiner":"\\n","joinerType":"str","accumulate":false,"timeout":"","count":"","reduceRight":false,"reduceExp":"","reduceInit":"","reduceInitType":"","reduceFixup":"","x":690,"y":500,"wires":[["9cd9d88bb507cc49","6f472c26ef54f974"]]},{"id":"d3a842120a526a7f","type":"comment","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"Tranforming customer data","info":"","x":330,"y":420,"wires":[]},{"id":"6f472c26ef54f974","type":"change","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"set payload","rules":[{"t":"set","p":"data","pt":"msg","to":"payload","tot":"msg"},{"t":"set","p":"payload","pt":"msg","to":"[]","tot":"json"}],"action":"","property":"","from":"","to":"","reg":false,"x":970,"y":420,"wires":[["a88a07f39e0e941d"]]},{"id":"a88a07f39e0e941d","type":"mongodb4","z":"977143edb097b685","g":"e0adf6e180ec1a7c","clientNode":"a0c7c3410ffb0e89","mode":"collection","collection":"customer_data","operation":"find","output":"toArray","maxTimeMS":"0","handleDocId":false,"name":"","x":1130,"y":420,"wires":[["dd3fb62233d5477e"]]},{"id":"0e0129a95e24355c","type":"link in","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"link in 1","links":["fd68d5c864a0e7c9"],"x":145,"y":500,"wires":[["ace58c34d1566f51"]]},{"id":"c7150b3b68fc34a0","type":"link out","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"link out 2","mode":"link","links":["78176aee11393705"],"x":1405,"y":420,"wires":[]},{"id":"3f3ad8e528f5800b","type":"link out","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"link out 3","mode":"link","links":["ecfa23782d33d813"],"x":1115,"y":540,"wires":[]},{"id":"dd3fb62233d5477e","type":"function","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"set payload","func":"let previously_stored_customer_data = Array.isArray(msg.payload) ? msg.payload : (msg.payload ? [msg.payload] : []);\nlet latest_customer_data = Array.isArray(msg.data) ? Array.from(msg.data) : (msg.data ? [msg.data] : []);\n\nif (latest_customer_data.length !== 0 && previously_stored_customer_data.length !== 0) {\n    let fullNameArray1 = previously_stored_customer_data.map(customer => customer.full_name);\n    let filtered_customer_data = latest_customer_data.filter(customer => !fullNameArray1.includes(customer.full_name));\n    msg.payload = [filtered_customer_data];\n    return msg\n} else {\n    // Case where there's no previously stored data\n    msg.payload = [latest_customer_data];\n    return msg\n}","outputs":1,"timeout":"","noerr":0,"initialize":"","finalize":"","libs":[],"x":1290,"y":420,"wires":[["c7150b3b68fc34a0"]]},{"id":"a0c7c3410ffb0e89","type":"mongodb4-client","name":"","protocol":"mongodb+srv","hostname":"","port":"","dbName":"mydb","appName":"","authSource":"","authMechanism":"DEFAULT","tls":false,"tlsCAFile":"","tlsCertificateKeyFile":"","tlsInsecure":false,"connectTimeoutMS":"30000","socketTimeoutMS":"0","minPoolSize":"0","maxPoolSize":"100","maxIdleTimeMS":"0","uri":"mongodb+srv://cluster0.uxibwot.mongodb.net","advanced":"{}","uriTabActive":"tab-uri-advanced"},{"id":"ac5e2cabb6251314","type":"group","z":"977143edb097b685","name":"","style":{"label":true},"nodes":["d6a4a19c23a0f42b","78176aee11393705","ecfa23782d33d813","dab90d6302b1c885","bc4afbc715e3b64d","d781431fed20d650","f19a1157e25b47df"],"x":114,"y":619,"w":592,"h":222},{"id":"d6a4a19c23a0f42b","type":"mongodb4","z":"977143edb097b685","g":"ac5e2cabb6251314","clientNode":"a0c7c3410ffb0e89","mode":"collection","collection":"customer_data","operation":"insertMany","output":"toArray","maxTimeMS":"0","handleDocId":false,"name":"","x":370,"y":720,"wires":[["bc4afbc715e3b64d"]]},{"id":"78176aee11393705","type":"link 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From ed57625aa5dc2d5026f242f632eb94c9d03309af Mon Sep 17 00:00:00 2001
From: "sumit shinde ( Roni )" <shindesumit217@gmail.com>
Date: Wed, 12 Jun 2024 12:47:18 +0530
Subject: [PATCH 05/13] update blog

---
 ...red-chart-customer-data-on-debug-panel.png | Bin 0 -> 31332 bytes
 .../2024/06/using-node-red-as-an-etl-tool.md  |  93 ++++++++++--------
 2 files changed, 50 insertions(+), 43 deletions(-)
 create mode 100644 src/blog/2024/06/images/etl-with-node-red-chart-customer-data-on-debug-panel.png

diff --git a/src/blog/2024/06/images/etl-with-node-red-chart-customer-data-on-debug-panel.png b/src/blog/2024/06/images/etl-with-node-red-chart-customer-data-on-debug-panel.png
new file mode 100644
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diff --git a/src/blog/2024/06/using-node-red-as-an-etl-tool.md b/src/blog/2024/06/using-node-red-as-an-etl-tool.md
index 0ada8c022a..bf670a64ef 100644
--- a/src/blog/2024/06/using-node-red-as-an-etl-tool.md
+++ b/src/blog/2024/06/using-node-red-as-an-etl-tool.md
@@ -1,6 +1,6 @@
 ---
-title: Using Node-RED as an ETL Tool
-subtitle: A Step-by-Step Guide on How to Use Node-RED as an ETL Tool
+title: Using Node-RED as an ETL Tool.
+subtitle: A Step-by-Step Guide on How to Use Node-RED as an ETL Tool.
 description: Learn how to utilize Node-RED as an ETL (Extract, Transform, Load) tool, and explore its advantages and disadvantages in this context.
 date: 2024-06-05
 authors: ["sumit-shinde"]
@@ -20,41 +20,49 @@ ETL is essential for data integration and analysis, playing a crucial role in ma
 
 ## What is ETL
 
-[ETL](https://en.wikipedia.org/wiki/Extract,_transform,_load) (Extract, Transform, Load) is a fundamental process in data integration and data warehousing. It encompasses three main stages: extracting data from various sources, transforming it into a format suitable for analysis, and loading it into a target database or data warehouse. This process allows businesses to consolidate data from multiple sources, ensuring it is clean, consistent, and ready for generating insights and making informed decisions.
+[ETL](https://en.wikipedia.org/wiki/Extract,_transform,_load) (Extract, Transform, Load) is a fundamental process in managing data efficiently. It consists of three main stages: extraction, transformation, and loading. In the extraction phase, data is gathered from various sources such as databases, APIs, or files. Then, in the transformation stage, the data is converted into a format that is suitable for analysis by cleaning, filtering, and structuring it. Finally, in the loading stage, the transformed data is stored in a target database or data warehouse for further analysis and decision-making. ETL plays a significant role in ensuring that data is organized, consistent, and readily available for generating insights and making informed decisions in businesses and organizations.
 
-## Node-RED as ETL a tool
+## Node-RED as an ETL tool
 
-Node-RED is specifically used for building various types of IoT applications. During the development of such applications, Node-RED is sometimes used as an ETL (Extract, Transform, Load) tool without even realizing it. For instance, when monitoring sensors across a facility, the process involves extracting data from sensors, transforming (cleaning and formatting) sensor data, and loading it into a database. There are thousands of other examples like this. This demonstrates that Node-RED has already been adopted as an ETL tool for certain scenarios.
+Node-RED is specifically used for building various types of IoT applications. During the development of such applications, Node-RED is sometimes used as an ETL tool without even realizing it. For instance, when monitoring sensors across a facility, the process involves extracting data from sensors, transforming (cleaning and formatting) sensor data, and loading it into a database. There are thousands of other such examples. This demonstrates that Node-RED has already been adopted as an ETL tool at certain level.
+
+*Note: We have mentioned only a few nodes that can be used in the ETL process, but there are thousands of other nodes that you can explore yourself by visiting the [Node-RED Nodes Library](https://flows.nodered.org/search?type=node)*
 
 ### Extracting
 
-Node-RED can extract data from various sources, including APIs, databases, local filesystems, and IoT devices using built-in nodes and community-contributed nodes. For example, the HTTP request node can be used to pull data from web services, while nodes for MySQL, MongoDB, and PostgreSQL can extract data from databases. Nodes for MQTT and Kafka can fetch data from message brokers. Additionally, there are cloud nodes such as AWS, Google Cloud Platform (GCP), and IBM Watson that allow extraction of data from cloud storage services.
+Node-RED can extract data from various sources, including APIs, databases, local filesystems, and IoT devices using built-in nodes and community-contributed nodes. For example, the HTTP request node can be used to pull data from web services, while nodes for MySQL, MongoDB, and PostgreSQL can extract data from databases. Nodes for MQTT and Kafka can fetch data from message brokers. Additionally, there are diffrent cloud nodes for cloud platform such as AWS, GCP, and IBM Watson that allow extraction of data from cloud storage services.
 
 ### Transforming
 
-Transformation in Node-RED involves converting data into the desired format, applying business rules, and cleaning it. This can be done using function nodes for custom JavaScript code, along with nodes like JSON, split, join, change CSV, and XML for parsing and formatting.
+Transforming data involves cleaning, structuring, and applying business rules. This can be done using function nodes with custom JavaScript, along with nodes like JSON, split, join, change, CSV, and XML for parsing and formatting.
 
 ### Loading
 
-The final step in the ETL process is loading the transformed data into a target system, such as a database or data warehouse. Node-RED supports various output nodes to send data to different destinations. For instance, you can use the MySQL node to insert data into a MySQL database, or the HTTP request node to send data to a web service endpoint.
+The final step in the ETL process is loading the transformed data into a target system, such as a database or data warehouse. Node-RED supports various output nodes to send data to different destinations. For instance, you can use the MySQL node to insert data into a MySQL database, or the HTTP request node to send data to a web service endpoint or cloud nodes to send data to cloud storage services.
 
 ### Visualizing and Analyzing
 
-In addition to the core ETL (Extract, Transform, Load) process, visualizing and analyzing data is a crucial step for gaining insights. While visualization isn't part of the ETL acronym itself, it plays a significant role in understanding and interpreting transformed data. In Node-RED, you can easily visualize data using Node-RED Dashboard 2.0 charts.
+In addition to the core ETL process, visualizing and analyzing data is a crucial step for gaining insights. While visualization isn't part of the ETL acronym itself, it plays a significant role in understanding and interpreting transformed data. In Node-RED, you can easily visualize data using Node-RED Dashboard 2.0 charts.
 
 Node-RED Dashboard 2.0 provides several chart options, and more are expected in the future. For more information and to explore available chart types, refer to the [Node-RED Dashboard 2.0 charts documentation](https://dashboard.flowfuse.com/nodes/widgets/ui-chart.html).
 
-*Note: We have mentioned only a few nodes that can be used in the ETL process, but there are thousands of other nodes that you can explore yourself by visiting the [Node-RED Nodes Library](https://flows.nodered.org/search?type=node)*
+## Benefits of using Node-RED as an ETL tool.
+
+1. **Ease of Use:** Its visual programming interface makes it accessible to non-developers.
+2. **Flexibility:** A wide range of nodes and the ability to write custom JavaScript allow for flexible data processing.
+3. **Integration:** Strong support for IoT and real-time data processing, making it ideal for integrating diverse data sources.
+4. **Cost-Effective:** Being open-source, Node-RED can be a cost-effective alternative to expensive ETL tools.
+5. **Community Support:** A large community provides a wealth of nodes, examples, and support.
 
 ## Building a Simple ETL Project Using Node-RED
 
-Let's walk through a simple project where we use Node-RED as an ETL tool to extract sample customer data from an API, transform it, and then load cleaned data into a MongoDB database and process data into a local file.
+Let's walk through a simple project where we will use Node-RED as an ETL tool to extract sample customer data from an API, transform it, and then load cleaned data into a MongoDB database and process data into a local file.
 
 *Note: The goal of this project is to understand how to utilize Node-RED as an ETL tool. We assume that the reader has a basic knowledge of Node-RED.*
 
 ## Prerequisite 
 
-Before proceeding further make sure you have installed the following nodes.
+Before proceeding further make sure you have installed the following nodes:
 
 1. [node-red-contrib-mongodb4](https://flows.nodered.org/node/node-red-contrib-mongodb4)
 2. [@flowfuse/node-red-dashboard](https://dashboard.flowfuse.com/)
@@ -63,29 +71,32 @@ Before proceeding further make sure you have installed the following nodes.
 
 1. Drag an Inject node onto the canvas.
 2. Drag an HTTP Request node onto the canvas, double-click on it, and set the URL to `https://api.slingacademy.com/v1/sample-data/files/customers.json`.
-3. Drag a Link Out node onto the canvas.
-4. Connect the Inject node's output to the input of the HTTP Request node, and the HTTP Request node's output to the input of the Link Out node.
+3. Drag a Link-out node onto the canvas.
+4. Connect the Inject node's output to the input of the HTTP Request node, and the HTTP Request node's output to the input of the Link-out node.
 
 Now, if you add a Debug node after the HTTP Request node, you will see an array of objects containing customer data in the Debug panel. Each object has properties such as `first_name`, `last_name`, `email`, and others.
 
-Next, we'll perform a transformation to replace the existing properties `first_name` and `last_name` with a single property `full_name` that concatenates them. We'll cover this transformation in the next section.
+!["Screenshot of debug panel displaying printed customer data"](./images/etl-with-node-red-chart-customer-data-on-debug-panel.png "Screenshot of debug panel displaying printed customer data"){data-zoomable}
+
+In next section, we'll perform a transformation to replace the existing properties `first_name` and `last_name` with a single property `full_name` that concatenates them.
 
 ## Transforming Data
 
 ### Data Cleaning
 
-1. Drag a Link In node onto the canvas, double-click on it, and select the name of the Link Out node added in the above section.
+1. Drag a Link-in node onto the canvas, double-click on it, and select the name of the Link-out node added in the above section.
 2. Drag a Split node onto the canvas.
-3. Drag a Change node onto the canvas and Set `msg.payload.full_name` to `payload.first_name & " " & payload.last_name` as the JSONata expression, then delete unnecessary properties like `first_name` and `last_name`.
+3. Drag a Change node onto the canvas and Set `msg.payload.full_name` to `payload.first_name & " " & payload.last_name` as the JSONata expression, this JSONata expression will concat the firstname and lastname properties, then delete unnecessary properties like `first_name` and `last_name`.
 4. Drag a Join node onto the canvas.
+5. Connect the output of Link-in node to the input of Split node, then split node output to the input of join node.
 
-until now we have created a flow that will create a new property in customer data objects called full_name and then it will delete the unnecessary properties. in summary, we have cleaned the data according to our needs.
+Up to this point, we've established a flow that creates a new property in customer data objects called 'full_name' and subsequently removes any unnecessary properties. In summary, we've cleaned the data according to our needs.
 
 ### Customer Data Deduplication
 
 1. Drag a Change node onto the canvas, and set `msg.data` to `msg.payload` and `msg.payload` to `[]`.
 2. Drag a MongoDB4 node onto the canvas, and configure it with your correct details. If you haven't used MongoDB with Node-RED, please refer to the [Node-RED MongoDB documentation](https://flows.nodered.org/node/node-red-contrib-mongodb4). Enter `find` into the Operation field.
-3. Drag a Function node onto the canvas and insert the following code into it:
+3. Drag a Function node onto the canvas and insert the following code into it and give it name "Data deplication":
 
 ```javascript
 // Check if previously stored customer data is present, and ensure it's an array
@@ -112,12 +123,13 @@ if (latest_customer_data.length !== 0 && previously_stored_customer_data.length
 ```
 
 4. Drag a Link-out node onto the canvas.
+5. Connect the output of Join node to the input of change node and output of change node to the input of MongoDB4 node and then MongoDB4 output to the input of function node named "Data deplication" and output of that function node to the input of Link-out node.
 
-Now, we have added some more nodes that perform data deduplication. This means we will create a new array containing unique data that is not already stored in the database to avoid data duplications.
+Now, we have added additional nodes that perform data deduplication. This means we will create a new array containing unique data that is not already stored in the database to avoid data duplications. For filtering that data, we are using JavaScript with a function node.
 
 ## Data Processing
 
-1. Drag a Function node onto the canvas and insert the following code into it:
+1. Drag a Function node onto the canvas and insert the following code into it and name this node "Data processing":
 
 ```javascript
 let data = msg.payload;
@@ -177,48 +189,43 @@ msg.payload = {
 return msg;
 ```
 2. Drag another Link-out node onto the canvas.
+3. Connect the Join node's output to the input of function node named "Data processing", and output of that function node to the input of Link-out node.
 
-Finally, in this phase of transformation, we are tallying the total number of users based on age, gender, and job title. This allows us to quickly analyze and understand our user demographics based on these details so that later we can visualize this data easily.
+In this phase of transformation, we are tallying the total number of users based on age, gender, and job title. This allows us to quickly analyze and understand our user demographics based on these details so that later we can visualize this data easily.
 
 ## Loading data
 
-1. Drag a Link-in node onto the canvas, double-click on it, and select the name of the Link-out node connected to the data deduplication function. Then, drag another Link-in node and connect it to the Link-out node connected to the data processing function node.
-
+1. Drag a Link-in node onto the canvas, double-click on it, and select the name of the Link-out node connected to the "Data deduplication" function. Then, drag another Link-in node and connect it to the Link-out node which is connected to the "Data processing" function node.
 2. Drag a MongoDB4 node onto the canvas and configure it correctly. Select the correct collection and enter `insertMany` into the Operation field.
-
 3. Drag a File Write node onto the canvas, set the path, and select the action to "override".
+4. Connect the first Link-in node's output to the input of MongoDB4 node and then second Link-in node's output to the input of File write node.
 
-!["Screenshot of dashboard visualizing process customer data based on job profile"](./images/etl-with-node-red-chart-based-on-job-profile.png "Screenshot of dashboard visualizing process customer data based on job profile"){data-zoomable}
-
-!["Screenshot of dashboard visualizing process customer data based on age group"](./images/etl-with-node-red-chart-based-on-age.png "Screenshot of dashboard visualizing process customer data based on age group"){data-zoomable}
+Now, finally, in this loading phase, we are storing the cleaned, formatted, and unique customer data into the database. The processed data, such as total users by age, gender, and job profiles, is stored in the local file
 
+## Deploying the flow
 
 Following is the complete flow of the project along with dashboard visualization.
 
 {% renderFlow %}
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 {% endrenderFlow %}
 
-## What are the limitations of using ETL as an ETL tool
+1. To Deploy the flow, click on the top-right Deploy button
+2. To visit the dashboard, Go to Dashboard 2.0 sidebar and click on the top-right "Open dashboard" button.
 
-While Node-RED is versatile, there are some limitations to consider:
+!["Screenshot of dashboard visualizing process customer data based on job profile"](./images/etl-with-node-red-chart-based-on-job-profile.png "Screenshot of dashboard visualizing process customer data based on job profile"){data-zoomable}
 
-- **Scalability:** Node-RED might struggle with extremely large datasets compared to dedicated ETL tools.
-- **Performance:** For very complex transformations or high-volume data, performance might not match that of specialized ETL solutions.
-- **Advanced Features:** Some advanced ETL features, like automated schema detection and sophisticated error handling, might require additional customization or external modules.
-- **Data Governance:** Node-RED does not inherently provide robust data governance and lineage tracking which are often essential in enterprise ETL tools.
+!["Screenshot of dashboard visualizing process customer data based on age group"](./images/etl-with-node-red-chart-based-on-age.png "Screenshot of dashboard visualizing process customer data based on age group"){data-zoomable}
 
-## Benefits of using Node-RED as an ETL tool.
+## What are the limitations of using Node-RED as an ETL tool
 
-Despite its limitations, Node-RED offers several benefits:
+While Node-RED is versatile, there are some limitations to consider while using it as ETL tool:
 
-1. **Ease of Use:** Its visual programming interface makes it accessible to non-developers.
-2. **Flexibility:** A wide range of nodes and the ability to write custom JavaScript allow for flexible data processing.
-3. **Integration:** Strong support for IoT and real-time data processing, making it ideal for integrating diverse data sources.
-4. **Cost-Effective:** Being open-source, Node-RED can be a cost-effective alternative to expensive ETL tools.
-5. **Community Support:** A large community provides a wealth of nodes, examples, and support.
+- **Scalability:** Node-RED might struggle with extremely large datasets compared to dedicated ETL tools.
+- **Advanced Features:** Some advanced ETL features, like automated schema detection and sophisticated error handling, might require additional customization or external modules.
+- **Data Governance:** Node-RED does not inherently provide robust data governance and lineage tracking which are often essential in enterprise ETL tools.
 
 ## Additional resources
 
 - [Implementing ETL flows with Node-RED](https://developer.ibm.com/articles/implementing-etl-flows-with-node-red/): Read this IBM blog where a team member explains how they replaced a dedicated ETL tool with Node-RED.
-- [Node-RED Forum Thread](https://discourse.nodered.org/t/etl-pipelines-with-node-red/): Explore this Node-RED forum thread where community members discussed how to utilize Node-RED as an ETL tool, including solutions for handling large datasets.
+- [Node-RED Forum Thread](https://discourse.nodered.org/t/etl-pipelines-with-node-red/): Explore this Node-RED forum thread where community members discussed how to utilize Node-RED as an ETL tool, including solutions for handling large datasets.
\ No newline at end of file

From cec219a48f4d4a93ce68c4d0490901948fc07f97 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]"
 <41898282+github-actions[bot]@users.noreply.github.com>
Date: Wed, 12 Jun 2024 07:19:46 +0000
Subject: [PATCH 06/13] Optimised images with calibre/image-actions

---
 ...red-chart-customer-data-on-debug-panel.png | Bin 31332 -> 16162 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/src/blog/2024/06/images/etl-with-node-red-chart-customer-data-on-debug-panel.png b/src/blog/2024/06/images/etl-with-node-red-chart-customer-data-on-debug-panel.png
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From 11abae9c71bf68a8ca40db8a0bce03eea174c1eb Mon Sep 17 00:00:00 2001
From: "sumit shinde ( Roni )"
 <110285294+sumitshinde-84@users.noreply.github.com>
Date: Thu, 13 Jun 2024 00:34:37 +0530
Subject: [PATCH 07/13] Update using-node-red-as-an-etl-tool.md

---
 src/blog/2024/06/using-node-red-as-an-etl-tool.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/blog/2024/06/using-node-red-as-an-etl-tool.md b/src/blog/2024/06/using-node-red-as-an-etl-tool.md
index bf670a64ef..358116b6e3 100644
--- a/src/blog/2024/06/using-node-red-as-an-etl-tool.md
+++ b/src/blog/2024/06/using-node-red-as-an-etl-tool.md
@@ -95,7 +95,7 @@ Up to this point, we've established a flow that creates a new property in custom
 ### Customer Data Deduplication
 
 1. Drag a Change node onto the canvas, and set `msg.data` to `msg.payload` and `msg.payload` to `[]`.
-2. Drag a MongoDB4 node onto the canvas, and configure it with your correct details. If you haven't used MongoDB with Node-RED, please refer to the [Node-RED MongoDB documentation](https://flows.nodered.org/node/node-red-contrib-mongodb4). Enter `find` into the Operation field.
+2. Drag a MongoDB4 node onto the canvas, and configure it with your correct details. If you haven't used MongoDB with Node-RED, please refer to the [Using MongoDB with Node-RED](/blog/2024/04/using-mongodb-with-node-red/). Enter `find` into the Operation field.
 3. Drag a Function node onto the canvas and insert the following code into it and give it name "Data deplication":
 
 ```javascript
@@ -207,7 +207,7 @@ Now, finally, in this loading phase, we are storing the cleaned, formatted, and
 Following is the complete flow of the project along with dashboard visualization.
 
 {% renderFlow %}
-[{"id":"e85cb581eb249115","type":"group","z":"977143edb097b685","style":{"stroke":"#b2b3bd","stroke-opacity":"1","fill":"#f2f3fb","fill-opacity":"0.5","label":true,"label-position":"nw","color":"#32333b"},"nodes":["218b698ddba2210b","638eddcd2b5f645e","9773152e6a0f2287","fd68d5c864a0e7c9"],"x":114,"y":159,"w":532,"h":182},{"id":"218b698ddba2210b","type":"http request","z":"977143edb097b685","g":"e85cb581eb249115","name":"","method":"GET","ret":"obj","paytoqs":"ignore","url":"https://api.slingacademy.com/v1/sample-data/files/customers.json","tls":"","persist":false,"proxy":"","insecureHTTPParser":false,"authType":"","senderr":false,"headers":[],"x":430,"y":300,"wires":[["fd68d5c864a0e7c9"]]},{"id":"638eddcd2b5f645e","type":"inject","z":"977143edb097b685","g":"e85cb581eb249115","name":"Extract Data","props":[{"p":"payload"},{"p":"topic","vt":"str"}],"repeat":"","crontab":"","once":false,"onceDelay":0.1,"topic":"","payload":"","payloadType":"date","x":230,"y":300,"wires":[["218b698ddba2210b"]]},{"id":"9773152e6a0f2287","type":"comment","z":"977143edb097b685","g":"e85cb581eb249115","name":"Extracting customer data from api","info":"","x":330,"y":200,"wires":[]},{"id":"fd68d5c864a0e7c9","type":"Link-out","z":"977143edb097b685","g":"e85cb581eb249115","name":"Link-out 1","mode":"link","links":["0e0129a95e24355c"],"x":605,"y":300,"wires":[]},{"id":"e0adf6e180ec1a7c","type":"group","z":"977143edb097b685","style":{"stroke":"#b2b3bd","stroke-opacity":"1","fill":"#f2f3fb","fill-opacity":"0.5","label":true,"label-position":"nw","color":"#32333b"},"nodes":["ace58c34d1566f51","d2a48c9825b224b4","9cd9d88bb507cc49","ef5e8501b76cfec8","d3a842120a526a7f","6f472c26ef54f974","a88a07f39e0e941d","0e0129a95e24355c","c7150b3b68fc34a0","3f3ad8e528f5800b","dd3fb62233d5477e"],"x":104,"y":379,"w":1342,"h":202},{"id":"ace58c34d1566f51","type":"split","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"","splt":"\\n","spltType":"str","arraySplt":1,"arraySpltType":"len","stream":false,"addname":"","x":250,"y":500,"wires":[["d2a48c9825b224b4"]]},{"id":"d2a48c9825b224b4","type":"change","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"","rules":[{"t":"set","p":"payload.full_name","pt":"msg","to":"payload.first_name & \" \" & payload.last_name","tot":"jsonata"},{"t":"delete","p":"payload.first_name","pt":"msg"},{"t":"delete","p":"payload.last_name","pt":"msg"}],"action":"","property":"","from":"","to":"","reg":false,"x":460,"y":500,"wires":[["ef5e8501b76cfec8"]]},{"id":"9cd9d88bb507cc49","type":"function","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"data processing","func":"let data = msg.payload\n\nlet ageRanges = {\n    \"0-20\": 0,\n    \"21-30\": 0,\n    \"31-40\": 0,\n    \"41+\": 0\n};\n\nlet genderTotals = {\n    male: 0,\n    female: 0,\n    other: 0  // Add other genders as needed\n};\n\nlet jobStatusTotals = {};\n\n// Iterate through each document to calculate counts\ndata.forEach(doc => {\n    // Calculate age range\n    let age = parseInt(doc.age);\n    let ageRange;\n    if (age <= 20) {\n        ageRange = \"0-20\";\n    } else if (age <= 30) {\n        ageRange = \"21-30\";\n    } else if (age <= 40) {\n        ageRange = \"31-40\";\n    } else {\n        ageRange = \"41+\";\n    }\n    // Increment age range count\n    ageRanges[ageRange]++;\n\n    // Increment gender count\n    if (doc.gender === 'male') {\n        genderTotals.male++;\n    } else if (doc.gender === 'female') {\n        genderTotals.female++;\n    } else {\n        genderTotals.other++;\n    }\n\n    // Increment job status count\n    if (jobStatusTotals[doc.job]) {\n        jobStatusTotals[doc.job]++;\n    } else {\n        jobStatusTotals[doc.job] = 1;\n    }\n});\n\n\nmsg.payload = {\n    \"ageRanges\": ageRanges,\n    \"genderTotals\": genderTotals,\n    \"jobStatusTotals\": jobStatusTotals\n}; \n\n// Return the aggregated counts object\nreturn msg","outputs":1,"timeout":0,"noerr":0,"initialize":"","finalize":"","libs":[],"x":980,"y":540,"wires":[["3f3ad8e528f5800b"]]},{"id":"ef5e8501b76cfec8","type":"join","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"","mode":"custom","build":"array","property":"payload","propertyType":"msg","key":"topic","joiner":"\\n","joinerType":"str","accumulate":false,"timeout":"","count":"","reduceRight":false,"reduceExp":"","reduceInit":"","reduceInitType":"","reduceFixup":"","x":690,"y":500,"wires":[["9cd9d88bb507cc49","6f472c26ef54f974"]]},{"id":"d3a842120a526a7f","type":"comment","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"Tranforming customer data","info":"","x":330,"y":420,"wires":[]},{"id":"6f472c26ef54f974","type":"change","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"set payload","rules":[{"t":"set","p":"data","pt":"msg","to":"payload","tot":"msg"},{"t":"set","p":"payload","pt":"msg","to":"[]","tot":"json"}],"action":"","property":"","from":"","to":"","reg":false,"x":970,"y":420,"wires":[["a88a07f39e0e941d"]]},{"id":"a88a07f39e0e941d","type":"mongodb4","z":"977143edb097b685","g":"e0adf6e180ec1a7c","clientNode":"a0c7c3410ffb0e89","mode":"collection","collection":"customer_data","operation":"find","output":"toArray","maxTimeMS":"0","handleDocId":false,"name":"","x":1130,"y":420,"wires":[["dd3fb62233d5477e"]]},{"id":"0e0129a95e24355c","type":"Link-in","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"Link-in 1","links":["fd68d5c864a0e7c9"],"x":145,"y":500,"wires":[["ace58c34d1566f51"]]},{"id":"c7150b3b68fc34a0","type":"Link-out","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"Link-out 2","mode":"link","links":["78176aee11393705"],"x":1405,"y":420,"wires":[]},{"id":"3f3ad8e528f5800b","type":"Link-out","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"Link-out 3","mode":"link","links":["ecfa23782d33d813"],"x":1115,"y":540,"wires":[]},{"id":"dd3fb62233d5477e","type":"function","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"set payload","func":"let previously_stored_customer_data = Array.isArray(msg.payload) ? msg.payload : (msg.payload ? [msg.payload] : []);\nlet latest_customer_data = Array.isArray(msg.data) ? Array.from(msg.data) : (msg.data ? [msg.data] : []);\n\nif (latest_customer_data.length !== 0 && previously_stored_customer_data.length !== 0) {\n    let fullNameArray1 = previously_stored_customer_data.map(customer => customer.full_name);\n    let filtered_customer_data = latest_customer_data.filter(customer => !fullNameArray1.includes(customer.full_name));\n    msg.payload = [filtered_customer_data];\n    return msg\n} else {\n    // Case where there's no previously stored data\n    msg.payload = [latest_customer_data];\n    return msg\n}","outputs":1,"timeout":"","noerr":0,"initialize":"","finalize":"","libs":[],"x":1290,"y":420,"wires":[["c7150b3b68fc34a0"]]},{"id":"a0c7c3410ffb0e89","type":"mongodb4-client","name":"","protocol":"mongodb+srv","hostname":"","port":"","dbName":"mydb","appName":"","authSource":"","authMechanism":"DEFAULT","tls":false,"tlsCAFile":"","tlsCertificateKeyFile":"","tlsInsecure":false,"connectTimeoutMS":"30000","socketTimeoutMS":"0","minPoolSize":"0","maxPoolSize":"100","maxIdleTimeMS":"0","uri":"mongodb+srv://cluster0.uxibwot.mongodb.net","advanced":"{}","uriTabActive":"tab-uri-advanced"},{"id":"ac5e2cabb6251314","type":"group","z":"977143edb097b685","name":"","style":{"label":true},"nodes":["d6a4a19c23a0f42b","78176aee11393705","ecfa23782d33d813","dab90d6302b1c885","bc4afbc715e3b64d","d781431fed20d650","f19a1157e25b47df"],"x":114,"y":619,"w":592,"h":222},{"id":"d6a4a19c23a0f42b","type":"mongodb4","z":"977143edb097b685","g":"ac5e2cabb6251314","clientNode":"a0c7c3410ffb0e89","mode":"collection","collection":"customer_data","operation":"insertMany","output":"toArray","maxTimeMS":"0","handleDocId":false,"name":"","x":370,"y":720,"wires":[["bc4afbc715e3b64d"]]},{"id":"78176aee11393705","type":"Link-in","z":"977143edb097b685","g":"ac5e2cabb6251314","name":"Link-in 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[msg.payload] : []);\nlet latest_customer_data = Array.isArray(msg.data) ? Array.from(msg.data) : (msg.data ? 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1","func":"let selectedDataForChart = global.get(\"selectedDataForChart\")\n// Assuming msg.payload is an object\nlet data = msg.payload[selectedDataForChart]\nlet result = [];\n\n// Loop through each key in the payload object\nObject.keys(data).forEach(key => {\n    result.push({ category: key, value: data[key] });\n});\n\nmsg.payload = result\nreturn msg;","outputs":1,"timeout":0,"noerr":0,"initialize":"","finalize":"","libs":[],"x":1240,"y":980,"wires":[["9b534cdc46dfc52a"]]},{"id":"ed6fc24b155aef33","type":"ui-dropdown","z":"977143edb097b685","g":"520f961e35d10da4","group":"1859e113313a5279","name":"","label":"Select Option:","tooltip":"","order":1,"width":0,"height":0,"passthru":false,"multiple":false,"options":[{"label":"Based on age group","value":"ageRanges","type":"str"},{"label":"Based on gender group","value":"genderTotals","type":"str"},{"label":"Based on job profile","value":"jobStatusTotals","type":"str"}],"payload":"","topic":"topic","topicType":"msg","className":"","x":220,"y":980,"wires":[["b64ddca6aab82cb2"]]},{"id":"af4bc1c2a6e6d020","type":"file in","z":"977143edb097b685","g":"520f961e35d10da4","name":"","filename":"./customer_data_analytics","filenameType":"str","format":"lines","chunk":false,"sendError":false,"encoding":"none","allProps":false,"x":810,"y":980,"wires":[["572c04ca900af9d5"]]},{"id":"572c04ca900af9d5","type":"json","z":"977143edb097b685","g":"520f961e35d10da4","name":"","property":"payload","action":"","pretty":false,"x":1050,"y":980,"wires":[["52c6d1c11c84571c"]]},{"id":"b64ddca6aab82cb2","type":"change","z":"977143edb097b685","g":"520f961e35d10da4","name":"store selected data in global context","rules":[{"t":"set","p":"selectedDataForChart","pt":"global","to":"payload","tot":"msg"}],"action":"","property":"","from":"","to":"","reg":false,"x":500,"y":980,"wires":[["af4bc1c2a6e6d020"]]},{"id":"a135a8ed2c447561","type":"comment","z":"977143edb097b685","g":"520f961e35d10da4","name":"Visualizing data on charts","info":"","x":350,"y":920,"wires":[]},{"id":"1859e113313a5279","type":"ui-group","name":"chart","page":"","width":"13","height":"1","order":-1,"showTitle":true,"className":"","visible":"true","disabled":"false"}]
 {% endrenderFlow %}
 
 1. To Deploy the flow, click on the top-right Deploy button
@@ -228,4 +228,4 @@ While Node-RED is versatile, there are some limitations to consider while using
 ## Additional resources
 
 - [Implementing ETL flows with Node-RED](https://developer.ibm.com/articles/implementing-etl-flows-with-node-red/): Read this IBM blog where a team member explains how they replaced a dedicated ETL tool with Node-RED.
-- [Node-RED Forum Thread](https://discourse.nodered.org/t/etl-pipelines-with-node-red/): Explore this Node-RED forum thread where community members discussed how to utilize Node-RED as an ETL tool, including solutions for handling large datasets.
\ No newline at end of file
+- [Node-RED Forum Thread](https://discourse.nodered.org/t/etl-pipelines-with-node-red/): Explore this Node-RED forum thread where community members discussed how to utilize Node-RED as an ETL tool, including solutions for handling large datasets.

From 63c33f6cb3cfda8606c8932ffc66e4543f237e45 Mon Sep 17 00:00:00 2001
From: "sumit shinde ( Roni )"
 <110285294+sumitshinde-84@users.noreply.github.com>
Date: Thu, 13 Jun 2024 20:58:54 +0530
Subject: [PATCH 08/13] Update using-node-red-as-an-etl-tool.md

---
 .../2024/06/using-node-red-as-an-etl-tool.md  | 20 +++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/src/blog/2024/06/using-node-red-as-an-etl-tool.md b/src/blog/2024/06/using-node-red-as-an-etl-tool.md
index 358116b6e3..3941a5ea2a 100644
--- a/src/blog/2024/06/using-node-red-as-an-etl-tool.md
+++ b/src/blog/2024/06/using-node-red-as-an-etl-tool.md
@@ -14,7 +14,7 @@ tags:
   - business intelligence
 ---
 
-ETL is essential for data integration and analysis, playing a crucial role in many business contexts by helping to gain detailed business insights. Node-RED, known for its ease in creating IoT applications, has also been recognized for its potential in ETL tasks. When IBM published its blog on using Node-RED for ETL, the topic garnered significant attention and sparked discussions on whether it could be effectively utilized for ETL. In this guide, we'll explore how to use Node-RED for ETL, discussing both its strengths and weaknesses along the way.
+ETL (Extract, Transform, Load) is essential for integrating and analyzing data, helping businesses unlock detailed insights. You might already know Node-RED for its user-friendly approach to creating IoT applications, but did you know it can also be a powerful tool for ETL tasks? When IBM published a blog about using Node-RED for ETL, it caught a lot of attention and got people talking about its potential in this space. In this guide, we'll walk you through how to use Node-RED for ETL, sharing its strengths and weaknesses along the way.
 
 <!--more-->
 
@@ -24,13 +24,13 @@ ETL is essential for data integration and analysis, playing a crucial role in ma
 
 ## Node-RED as an ETL tool
 
-Node-RED is specifically used for building various types of IoT applications. During the development of such applications, Node-RED is sometimes used as an ETL tool without even realizing it. For instance, when monitoring sensors across a facility, the process involves extracting data from sensors, transforming (cleaning and formatting) sensor data, and loading it into a database. There are thousands of other such examples. This demonstrates that Node-RED has already been adopted as an ETL tool at certain level.
+Node-RED is specifically used for building various types of IoT applications. During the development of such applications, Node-RED is sometimes used as an ETL tool without even realizing it. For instance, when monitoring sensors across a facility, the process involves extracting data from sensors, transforming (cleaning and formatting) sensor data, and loading it into a database. There are thousands of other such examples. This demonstrates that Node-RED has already been adopted as an ETL tool at a certain level.
 
 *Note: We have mentioned only a few nodes that can be used in the ETL process, but there are thousands of other nodes that you can explore yourself by visiting the [Node-RED Nodes Library](https://flows.nodered.org/search?type=node)*
 
 ### Extracting
 
-Node-RED can extract data from various sources, including APIs, databases, local filesystems, and IoT devices using built-in nodes and community-contributed nodes. For example, the HTTP request node can be used to pull data from web services, while nodes for MySQL, MongoDB, and PostgreSQL can extract data from databases. Nodes for MQTT and Kafka can fetch data from message brokers. Additionally, there are diffrent cloud nodes for cloud platform such as AWS, GCP, and IBM Watson that allow extraction of data from cloud storage services.
+Node-RED can extract data from various sources, including APIs, databases, local filesystems, and IoT devices using built-in nodes and community-contributed nodes. For example, the HTTP request node can be used to pull data from web services, while nodes for MySQL, MongoDB, and PostgreSQL can extract data from databases. Nodes for MQTT and Kafka can fetch data from message brokers. Additionally, there are different cloud nodes for cloud platforms such as AWS, GCP, and IBM Watson that allow extraction of data from cloud storage services.
 
 ### Transforming
 
@@ -72,7 +72,7 @@ Before proceeding further make sure you have installed the following nodes:
 1. Drag an Inject node onto the canvas.
 2. Drag an HTTP Request node onto the canvas, double-click on it, and set the URL to `https://api.slingacademy.com/v1/sample-data/files/customers.json`.
 3. Drag a Link-out node onto the canvas.
-4. Connect the Inject node's output to the input of the HTTP Request node, and the HTTP Request node's output to the input of the Link-out node.
+4. Connect the Inject node's output to the input of the HTTP Request node and the HTTP Request node's output to the input of the Link-out node.
 
 Now, if you add a Debug node after the HTTP Request node, you will see an array of objects containing customer data in the Debug panel. Each object has properties such as `first_name`, `last_name`, `email`, and others.
 
@@ -88,7 +88,7 @@ In next section, we'll perform a transformation to replace the existing properti
 2. Drag a Split node onto the canvas.
 3. Drag a Change node onto the canvas and Set `msg.payload.full_name` to `payload.first_name & " " & payload.last_name` as the JSONata expression, this JSONata expression will concat the firstname and lastname properties, then delete unnecessary properties like `first_name` and `last_name`.
 4. Drag a Join node onto the canvas.
-5. Connect the output of Link-in node to the input of Split node, then split node output to the input of join node.
+5. Connect the output of the Link-in node to the input of the Split node, then the Split node output to the input of the Join node.
 
 Up to this point, we've established a flow that creates a new property in customer data objects called 'full_name' and subsequently removes any unnecessary properties. In summary, we've cleaned the data according to our needs.
 
@@ -96,7 +96,7 @@ Up to this point, we've established a flow that creates a new property in custom
 
 1. Drag a Change node onto the canvas, and set `msg.data` to `msg.payload` and `msg.payload` to `[]`.
 2. Drag a MongoDB4 node onto the canvas, and configure it with your correct details. If you haven't used MongoDB with Node-RED, please refer to the [Using MongoDB with Node-RED](/blog/2024/04/using-mongodb-with-node-red/). Enter `find` into the Operation field.
-3. Drag a Function node onto the canvas and insert the following code into it and give it name "Data deplication":
+3. Drag a Function node onto the canvas and insert the following code into it and give it the name "Data deduplication":
 
 ```javascript
 // Check if previously stored customer data is present, and ensure it's an array
@@ -123,13 +123,13 @@ if (latest_customer_data.length !== 0 && previously_stored_customer_data.length
 ```
 
 4. Drag a Link-out node onto the canvas.
-5. Connect the output of Join node to the input of change node and output of change node to the input of MongoDB4 node and then MongoDB4 output to the input of function node named "Data deplication" and output of that function node to the input of Link-out node.
+5. Connect the output of the Join node to the input of change node and the output of the Change node to the input of the MongoDB4 node and then MongoDB4 output to the input of the function node named "Data deplication" and output of that function node to the input of Link-out node.
 
 Now, we have added additional nodes that perform data deduplication. This means we will create a new array containing unique data that is not already stored in the database to avoid data duplications. For filtering that data, we are using JavaScript with a function node.
 
 ## Data Processing
 
-1. Drag a Function node onto the canvas and insert the following code into it and name this node "Data processing":
+1. Drag a Function node onto the canvas insert the following code into it and name this node "Data processing":
 
 ```javascript
 let data = msg.payload;
@@ -211,7 +211,7 @@ Following is the complete flow of the project along with dashboard visualization
 {% endrenderFlow %}
 
 1. To Deploy the flow, click on the top-right Deploy button
-2. To visit the dashboard, Go to Dashboard 2.0 sidebar and click on the top-right "Open dashboard" button.
+2. To visit the dashboard, Go to the Dashboard 2.0 sidebar and click on the top-right "Open dashboard" button.
 
 !["Screenshot of dashboard visualizing process customer data based on job profile"](./images/etl-with-node-red-chart-based-on-job-profile.png "Screenshot of dashboard visualizing process customer data based on job profile"){data-zoomable}
 
@@ -219,7 +219,7 @@ Following is the complete flow of the project along with dashboard visualization
 
 ## What are the limitations of using Node-RED as an ETL tool
 
-While Node-RED is versatile, there are some limitations to consider while using it as ETL tool:
+While Node-RED is versatile, there are some limitations to consider while using it as an ETL tool:
 
 - **Scalability:** Node-RED might struggle with extremely large datasets compared to dedicated ETL tools.
 - **Advanced Features:** Some advanced ETL features, like automated schema detection and sophisticated error handling, might require additional customization or external modules.

From 395f514a15ce43f230622b32459ed45a46bd98c7 Mon Sep 17 00:00:00 2001
From: "sumit shinde ( Roni )"
 <110285294+sumitshinde-84@users.noreply.github.com>
Date: Thu, 13 Jun 2024 21:12:38 +0530
Subject: [PATCH 09/13] Update using-node-red-as-an-etl-tool.md

---
 src/blog/2024/06/using-node-red-as-an-etl-tool.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/blog/2024/06/using-node-red-as-an-etl-tool.md b/src/blog/2024/06/using-node-red-as-an-etl-tool.md
index 3941a5ea2a..b564b168f4 100644
--- a/src/blog/2024/06/using-node-red-as-an-etl-tool.md
+++ b/src/blog/2024/06/using-node-red-as-an-etl-tool.md
@@ -207,7 +207,7 @@ Now, finally, in this loading phase, we are storing the cleaned, formatted, and
 Following is the complete flow of the project along with dashboard visualization.
 
 {% renderFlow %}
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\n\n// Return the aggregated counts object\nreturn msg","outputs":1,"timeout":0,"noerr":0,"initialize":"","finalize":"","libs":[],"x":1120,"y":500,"wires":[["3f3ad8e528f5800b"]]},{"id":"ef5e8501b76cfec8","type":"join","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"","mode":"custom","build":"array","property":"payload","propertyType":"msg","key":"topic","joiner":"\\n","joinerType":"str","accumulate":false,"timeout":"","count":"","reduceRight":false,"reduceExp":"","reduceInit":"","reduceInitType":"","reduceFixup":"","x":830,"y":460,"wires":[["9cd9d88bb507cc49","6f472c26ef54f974"]]},{"id":"d3a842120a526a7f","type":"comment","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"Tranforming customer data","info":"","x":470,"y":380,"wires":[]},{"id":"6f472c26ef54f974","type":"change","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"set payload","rules":[{"t":"set","p":"data","pt":"msg","to":"payload","tot":"msg"},{"t":"set","p":"payload","pt":"msg","to":"[]","tot":"json"}],"action":"","property":"","from":"","to":"","reg":false,"x":1110,"y":380,"wires":[["a88a07f39e0e941d"]]},{"id":"a88a07f39e0e941d","type":"mongodb4","z":"977143edb097b685","g":"e0adf6e180ec1a7c","clientNode":"a0c7c3410ffb0e89","mode":"collection","collection":"customer_data","operation":"find","output":"toArray","maxTimeMS":"0","handleDocId":false,"name":"","x":1270,"y":380,"wires":[["dd3fb62233d5477e"]]},{"id":"0e0129a95e24355c","type":"link in","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"link in 1","links":["fd68d5c864a0e7c9"],"x":285,"y":460,"wires":[["ace58c34d1566f51"]]},{"id":"c7150b3b68fc34a0","type":"link out","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"link out 2","mode":"link","links":["78176aee11393705"],"x":1545,"y":380,"wires":[]},{"id":"3f3ad8e528f5800b","type":"link out","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"link out 3","mode":"link","links":["ecfa23782d33d813"],"x":1255,"y":500,"wires":[]},{"id":"dd3fb62233d5477e","type":"function","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"set payload","func":"let previously_stored_customer_data = Array.isArray(msg.payload) ? msg.payload : (msg.payload ? [msg.payload] : []);\nlet latest_customer_data = Array.isArray(msg.data) ? Array.from(msg.data) : (msg.data ? [msg.data] : []);\n\nif (latest_customer_data.length !== 0 && previously_stored_customer_data.length !== 0) {\n    let fullNameArray1 = previously_stored_customer_data.map(customer => customer.full_name);\n    let filtered_customer_data = latest_customer_data.filter(customer => !fullNameArray1.includes(customer.full_name));\n    msg.payload = [filtered_customer_data];\n    return msg\n} else {\n    // Case where there's no previously stored data\n    msg.payload = [latest_customer_data];\n    return msg\n}","outputs":1,"timeout":"","noerr":0,"initialize":"","finalize":"","libs":[],"x":1430,"y":380,"wires":[["c7150b3b68fc34a0"]]},{"id":"a0c7c3410ffb0e89","type":"mongodb4-client","name":"MongoDB 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in","z":"977143edb097b685","g":"ac5e2cabb6251314","name":"link in 2","links":["c7150b3b68fc34a0"],"x":295,"y":680,"wires":[["d6a4a19c23a0f42b"]]},{"id":"ecfa23782d33d813","type":"link in","z":"977143edb097b685","g":"ac5e2cabb6251314","name":"link in 3","links":["3f3ad8e528f5800b"],"x":295,"y":760,"wires":[["dab90d6302b1c885"]]},{"id":"dab90d6302b1c885","type":"file","z":"977143edb097b685","g":"ac5e2cabb6251314","name":"","filename":"./customer_data_analytics","filenameType":"str","appendNewline":true,"createDir":false,"overwriteFile":"true","encoding":"none","x":530,"y":760,"wires":[["d781431fed20d650"]]},{"id":"bc4afbc715e3b64d","type":"debug","z":"977143edb097b685","g":"ac5e2cabb6251314","name":"debug 1","active":true,"tosidebar":true,"console":false,"tostatus":false,"complete":"false","statusVal":"","statusType":"auto","x":720,"y":680,"wires":[]},{"id":"d781431fed20d650","type":"debug","z":"977143edb097b685","g":"ac5e2cabb6251314","name":"debug 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database","info":"","x":520,"y":620,"wires":[]},{"id":"520f961e35d10da4","type":"group","z":"977143edb097b685","style":{"stroke":"#b2b3bd","stroke-opacity":"1","fill":"#f2f3fb","fill-opacity":"0.5","label":true,"label-position":"nw","color":"#32333b"},"nodes":["9b534cdc46dfc52a","52c6d1c11c84571c","ed6fc24b155aef33","af4bc1c2a6e6d020","572c04ca900af9d5","b64ddca6aab82cb2","a135a8ed2c447561"],"x":254,"y":839,"w":1372,"h":142},{"id":"9b534cdc46dfc52a","type":"ui-chart","z":"977143edb097b685","g":"520f961e35d10da4","group":"1859e113313a5279","name":"","label":"chart","order":2,"chartType":"bar","category":"category","categoryType":"property","xAxisProperty":"","xAxisPropertyType":"msg","xAxisType":"category","yAxisProperty":"value","ymin":"","ymax":"","action":"replace","pointShape":"circle","pointRadius":4,"showLegend":false,"removeOlder":1,"removeOlderUnit":"3600","removeOlderPoints":"","colors":["#1f77b4","#aec7e8","#ff7f0e","#2ca02c","#98df8a","#d62728","#ff9896","#9467bd","#c5b0d5"],"width":"13","height":8,"className":"","x":1550,"y":940,"wires":[[]]},{"id":"52c6d1c11c84571c","type":"function","z":"977143edb097b685","g":"520f961e35d10da4","name":"function 1","func":"let selectedDataForChart = global.get(\"selectedDataForChart\")\n// Assuming msg.payload is an object\nlet data = msg.payload[selectedDataForChart]\nlet result = [];\n\n// Loop through each key in the payload object\nObject.keys(data).forEach(key => {\n    result.push({ category: key, value: data[key] });\n});\n\nmsg.payload = result\nreturn msg;","outputs":1,"timeout":0,"noerr":0,"initialize":"","finalize":"","libs":[],"x":1380,"y":940,"wires":[["9b534cdc46dfc52a"]]},{"id":"ed6fc24b155aef33","type":"ui-dropdown","z":"977143edb097b685","g":"520f961e35d10da4","group":"1859e113313a5279","name":"","label":"Select Option:","tooltip":"","order":1,"width":0,"height":0,"passthru":false,"multiple":false,"options":[{"label":"Based on age group","value":"ageRanges","type":"str"},{"label":"Based on gender group","value":"genderTotals","type":"str"},{"label":"Based on job profile","value":"jobStatusTotals","type":"str"}],"payload":"","topic":"topic","topicType":"msg","className":"","x":360,"y":940,"wires":[["b64ddca6aab82cb2"]]},{"id":"af4bc1c2a6e6d020","type":"file in","z":"977143edb097b685","g":"520f961e35d10da4","name":"","filename":"./customer_data_analytics","filenameType":"str","format":"lines","chunk":false,"sendError":false,"encoding":"none","allProps":false,"x":950,"y":940,"wires":[["572c04ca900af9d5"]]},{"id":"572c04ca900af9d5","type":"json","z":"977143edb097b685","g":"520f961e35d10da4","name":"","property":"payload","action":"","pretty":false,"x":1190,"y":940,"wires":[["52c6d1c11c84571c"]]},{"id":"b64ddca6aab82cb2","type":"change","z":"977143edb097b685","g":"520f961e35d10da4","name":"store selected data in global context","rules":[{"t":"set","p":"selectedDataForChart","pt":"global","to":"payload","tot":"msg"}],"action":"","property":"","from":"","to":"","reg":false,"x":640,"y":940,"wires":[["af4bc1c2a6e6d020"]]},{"id":"a135a8ed2c447561","type":"comment","z":"977143edb097b685","g":"520f961e35d10da4","name":"Visualizing data on charts","info":"","x":490,"y":880,"wires":[]},{"id":"1859e113313a5279","type":"ui-group","name":"chart","page":"f10b4d0259e43aeb","width":"13","height":"1","order":-1,"showTitle":true,"className":"","visible":"true","disabled":"false"},{"id":"f10b4d0259e43aeb","type":"ui-page","name":"home","ui":"cb79bc4520925e32","path":"/","icon":"","layout":"grid","theme":"2c5d702b11de7dd1","order":1,"className":"","visible":true,"disabled":"false"},{"id":"cb79bc4520925e32","type":"ui-base","name":"My UI","path":"/dashboard","includeClientData":true,"acceptsClientConfig":["ui-notification","ui-control"],"showPathInSidebar":false},{"id":"2c5d702b11de7dd1","type":"ui-theme","name":"Theme Name","colors":{"surface":"#ffffff","primary":"#0094ce","bgPage":"#eeeeee","groupBg":"#ffffff","groupOutline":"#cccccc"}}]
 {% endrenderFlow %}
 
 1. To Deploy the flow, click on the top-right Deploy button

From 929249b61e508bbba40ba1f44eaa3d36b674c017 Mon Sep 17 00:00:00 2001
From: "sumit shinde ( Roni )"
 <110285294+sumitshinde-84@users.noreply.github.com>
Date: Fri, 14 Jun 2024 15:44:10 +0530
Subject: [PATCH 10/13] Update using-node-red-as-an-etl-tool.md

---
 .../2024/06/using-node-red-as-an-etl-tool.md   | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/src/blog/2024/06/using-node-red-as-an-etl-tool.md b/src/blog/2024/06/using-node-red-as-an-etl-tool.md
index b564b168f4..45d61c0e74 100644
--- a/src/blog/2024/06/using-node-red-as-an-etl-tool.md
+++ b/src/blog/2024/06/using-node-red-as-an-etl-tool.md
@@ -14,7 +14,7 @@ tags:
   - business intelligence
 ---
 
-ETL (Extract, Transform, Load) is essential for integrating and analyzing data, helping businesses unlock detailed insights. You might already know Node-RED for its user-friendly approach to creating IoT applications, but did you know it can also be a powerful tool for ETL tasks? When IBM published a blog about using Node-RED for ETL, it caught a lot of attention and got people talking about its potential in this space. In this guide, we'll walk you through how to use Node-RED for ETL, sharing its strengths and weaknesses along the way.
+ETL (Extract, Transform, Load) is essential for integrating and analyzing data, helping businesses unlock detailed insights. You already know Node-RED for its user-friendly approach to creating IoT applications, but did you know it can also be a powerful tool for ETL tasks? When IBM published a blog about using Node-RED for ETL, it caught a lot of attention and got people talking about its potential in this space. In this guide, we'll walk you through how to use Node-RED for ETL, sharing its strengths and weaknesses along the way.
 
 <!--more-->
 
@@ -24,17 +24,17 @@ ETL (Extract, Transform, Load) is essential for integrating and analyzing data,
 
 ## Node-RED as an ETL tool
 
-Node-RED is specifically used for building various types of IoT applications. During the development of such applications, Node-RED is sometimes used as an ETL tool without even realizing it. For instance, when monitoring sensors across a facility, the process involves extracting data from sensors, transforming (cleaning and formatting) sensor data, and loading it into a database. There are thousands of other such examples. This demonstrates that Node-RED has already been adopted as an ETL tool at a certain level.
+Node-RED is specifically used for building various types of IoT applications. During the development of such applications, we sometimes used Node-RED as an ETL is tool without even realizing it. For instance, when monitoring sensors across a facility, the process involves extracting data from sensors, transforming (cleaning and formatting) sensor data, and loading it into a database. There are thousands of other such examples. This demonstrates that Node-RED has already been adopted as an ETL tool at a certain level.
 
 *Note: We have mentioned only a few nodes that can be used in the ETL process, but there are thousands of other nodes that you can explore yourself by visiting the [Node-RED Nodes Library](https://flows.nodered.org/search?type=node)*
 
 ### Extracting
 
-Node-RED can extract data from various sources, including APIs, databases, local filesystems, and IoT devices using built-in nodes and community-contributed nodes. For example, the HTTP request node can be used to pull data from web services, while nodes for MySQL, MongoDB, and PostgreSQL can extract data from databases. Nodes for MQTT and Kafka can fetch data from message brokers. Additionally, there are different cloud nodes for cloud platforms such as AWS, GCP, and IBM Watson that allow extraction of data from cloud storage services.
+Node-RED can extract data from various sources, including APIs, databases, local filesystems, and IoT devices using built-in nodes and community-contributed nodes. For example, the HTTP request node can be used to pull data from web services, while nodes for MySQL, MongoDB, and PostgreSQL can extract data from databases. Nodes for MQTT and Kafka can fetch data from message brokers. File nodes enable extraction of data from local filesystems, while different cloud nodes for platforms like AWS, GCP, and IBM Watson allow extraction of data from cloud storage services. Moreover, Node-RED running on edge devices can extract data from sensors connected directly to them.
 
 ### Transforming
 
-Transforming data involves cleaning, structuring, and applying business rules. This can be done using function nodes with custom JavaScript, along with nodes like JSON, split, join, change, CSV, and XML for parsing and formatting.
+Transforming data involves cleaning, structuring, and applying business rules. This can be done using function nodes with custom JavaScript, along with nodes like JSON, Split, Join, Change, CSV, and XML for parsing and formatting.
 
 ### Loading
 
@@ -50,7 +50,7 @@ Node-RED Dashboard 2.0 provides several chart options, and more are expected in
 
 1. **Ease of Use:** Its visual programming interface makes it accessible to non-developers.
 2. **Flexibility:** A wide range of nodes and the ability to write custom JavaScript allow for flexible data processing.
-3. **Integration:** Strong support for IoT and real-time data processing, making it ideal for integrating diverse data sources.
+3. **Integration:** Node-RED excels in integrating IoT devices and handling real-time data, making it well-suited for combining diverse data sources into unified workflows.
 4. **Cost-Effective:** Being open-source, Node-RED can be a cost-effective alternative to expensive ETL tools.
 5. **Community Support:** A large community provides a wealth of nodes, examples, and support.
 
@@ -71,7 +71,7 @@ Before proceeding further make sure you have installed the following nodes:
 
 1. Drag an Inject node onto the canvas.
 2. Drag an HTTP Request node onto the canvas, double-click on it, and set the URL to `https://api.slingacademy.com/v1/sample-data/files/customers.json`.
-3. Drag a Link-out node onto the canvas.
+3. Drag a Link-out node onto the canvas, I am using Link nodes to separate flow groups for different ETL phases to enhance organization, although you can directly connect nodes or maintain a single flow group.
 4. Connect the Inject node's output to the input of the HTTP Request node and the HTTP Request node's output to the input of the Link-out node.
 
 Now, if you add a Debug node after the HTTP Request node, you will see an array of objects containing customer data in the Debug panel. Each object has properties such as `first_name`, `last_name`, `email`, and others.
@@ -221,9 +221,9 @@ Following is the complete flow of the project along with dashboard visualization
 
 While Node-RED is versatile, there are some limitations to consider while using it as an ETL tool:
 
-- **Scalability:** Node-RED might struggle with extremely large datasets compared to dedicated ETL tools.
-- **Advanced Features:** Some advanced ETL features, like automated schema detection and sophisticated error handling, might require additional customization or external modules.
-- **Data Governance:** Node-RED does not inherently provide robust data governance and lineage tracking which are often essential in enterprise ETL tools.
+- Advanced Features: Some advanced ETL features, like automated schema detection and sophisticated error handling, might require additional customization or external modules.
+- Data Governance: Node-RED does not inherently provide robust data governance and lineage tracking, which are often essential in enterprise ETL tools.
+- Scalability: While Node-RED can handle many tasks effectively, it might not offer the same level of optimization for extremely large datasets compared to dedicated ETL tools.
 
 ## Additional resources
 

From fe981fb254156771a56f212efb52cf8acc97d5cc Mon Sep 17 00:00:00 2001
From: "sumit shinde ( Roni )"
 <110285294+sumitshinde-84@users.noreply.github.com>
Date: Fri, 14 Jun 2024 15:47:52 +0530
Subject: [PATCH 11/13] Update using-node-red-as-an-etl-tool.md

---
 src/blog/2024/06/using-node-red-as-an-etl-tool.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/blog/2024/06/using-node-red-as-an-etl-tool.md b/src/blog/2024/06/using-node-red-as-an-etl-tool.md
index 45d61c0e74..d72f602096 100644
--- a/src/blog/2024/06/using-node-red-as-an-etl-tool.md
+++ b/src/blog/2024/06/using-node-red-as-an-etl-tool.md
@@ -204,7 +204,7 @@ Now, finally, in this loading phase, we are storing the cleaned, formatted, and
 
 ## Deploying the flow
 
-Following is the complete flow of the project along with dashboard visualization.
+Below is the complete flow of the project, including dashboard visualization. For database configuration, I have utilized environment variables. Please ensure to set the variables with your database configuration before deploying the flow.
 
 {% renderFlow %}
 [{"id":"e85cb581eb249115","type":"group","z":"977143edb097b685","style":{"stroke":"#b2b3bd","stroke-opacity":"1","fill":"#f2f3fb","fill-opacity":"0.5","label":true,"label-position":"nw","color":"#32333b"},"nodes":["218b698ddba2210b","638eddcd2b5f645e","9773152e6a0f2287","fd68d5c864a0e7c9"],"x":254,"y":119,"w":532,"h":182},{"id":"218b698ddba2210b","type":"http request","z":"977143edb097b685","g":"e85cb581eb249115","name":"","method":"GET","ret":"obj","paytoqs":"ignore","url":"https://api.slingacademy.com/v1/sample-data/files/customers.json","tls":"","persist":false,"proxy":"","insecureHTTPParser":false,"authType":"","senderr":false,"headers":[],"x":570,"y":260,"wires":[["fd68d5c864a0e7c9"]]},{"id":"638eddcd2b5f645e","type":"inject","z":"977143edb097b685","g":"e85cb581eb249115","name":"Extract Data","props":[{"p":"payload"},{"p":"topic","vt":"str"}],"repeat":"","crontab":"","once":false,"onceDelay":0.1,"topic":"","payload":"","payloadType":"date","x":370,"y":260,"wires":[["218b698ddba2210b"]]},{"id":"9773152e6a0f2287","type":"comment","z":"977143edb097b685","g":"e85cb581eb249115","name":"Extracting customer data from api","info":"","x":470,"y":160,"wires":[]},{"id":"fd68d5c864a0e7c9","type":"link out","z":"977143edb097b685","g":"e85cb581eb249115","name":"link out 1","mode":"link","links":["0e0129a95e24355c"],"x":745,"y":260,"wires":[]},{"id":"e0adf6e180ec1a7c","type":"group","z":"977143edb097b685","style":{"stroke":"#b2b3bd","stroke-opacity":"1","fill":"#f2f3fb","fill-opacity":"0.5","label":true,"label-position":"nw","color":"#32333b"},"nodes":["ace58c34d1566f51","d2a48c9825b224b4","9cd9d88bb507cc49","ef5e8501b76cfec8","d3a842120a526a7f","6f472c26ef54f974","a88a07f39e0e941d","0e0129a95e24355c","c7150b3b68fc34a0","3f3ad8e528f5800b","dd3fb62233d5477e"],"x":244,"y":339,"w":1342,"h":202},{"id":"ace58c34d1566f51","type":"split","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"","splt":"\\n","spltType":"str","arraySplt":1,"arraySpltType":"len","stream":false,"addname":"","x":390,"y":460,"wires":[["d2a48c9825b224b4"]]},{"id":"d2a48c9825b224b4","type":"change","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"","rules":[{"t":"set","p":"payload.full_name","pt":"msg","to":"payload.first_name & \" \" & payload.last_name","tot":"jsonata"},{"t":"delete","p":"payload.first_name","pt":"msg"},{"t":"delete","p":"payload.last_name","pt":"msg"}],"action":"","property":"","from":"","to":"","reg":false,"x":600,"y":460,"wires":[["ef5e8501b76cfec8"]]},{"id":"9cd9d88bb507cc49","type":"function","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"data processing","func":"let data = msg.payload\n\nlet ageRanges = {\n    \"0-20\": 0,\n    \"21-30\": 0,\n    \"31-40\": 0,\n    \"41+\": 0\n};\n\nlet genderTotals = {\n    male: 0,\n    female: 0,\n    other: 0  // Add other genders as needed\n};\n\nlet jobStatusTotals = {};\n\n// Iterate through each document to calculate counts\ndata.forEach(doc => {\n    // Calculate age range\n    let age = parseInt(doc.age);\n    let ageRange;\n    if (age <= 20) {\n        ageRange = \"0-20\";\n    } else if (age <= 30) {\n        ageRange = \"21-30\";\n    } else if (age <= 40) {\n        ageRange = \"31-40\";\n    } else {\n        ageRange = \"41+\";\n    }\n    // Increment age range count\n    ageRanges[ageRange]++;\n\n    // Increment gender count\n    if (doc.gender === 'male') {\n        genderTotals.male++;\n    } else if (doc.gender === 'female') {\n        genderTotals.female++;\n    } else {\n        genderTotals.other++;\n    }\n\n    // Increment job status count\n    if (jobStatusTotals[doc.job]) {\n        jobStatusTotals[doc.job]++;\n    } else {\n        jobStatusTotals[doc.job] = 1;\n    }\n});\n\n\nmsg.payload = {\n    \"ageRanges\": ageRanges,\n    \"genderTotals\": genderTotals,\n    \"jobStatusTotals\": jobStatusTotals\n}; \n\n// Return the aggregated counts object\nreturn msg","outputs":1,"timeout":0,"noerr":0,"initialize":"","finalize":"","libs":[],"x":1120,"y":500,"wires":[["3f3ad8e528f5800b"]]},{"id":"ef5e8501b76cfec8","type":"join","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"","mode":"custom","build":"array","property":"payload","propertyType":"msg","key":"topic","joiner":"\\n","joinerType":"str","accumulate":false,"timeout":"","count":"","reduceRight":false,"reduceExp":"","reduceInit":"","reduceInitType":"","reduceFixup":"","x":830,"y":460,"wires":[["9cd9d88bb507cc49","6f472c26ef54f974"]]},{"id":"d3a842120a526a7f","type":"comment","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"Tranforming customer data","info":"","x":470,"y":380,"wires":[]},{"id":"6f472c26ef54f974","type":"change","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"set payload","rules":[{"t":"set","p":"data","pt":"msg","to":"payload","tot":"msg"},{"t":"set","p":"payload","pt":"msg","to":"[]","tot":"json"}],"action":"","property":"","from":"","to":"","reg":false,"x":1110,"y":380,"wires":[["a88a07f39e0e941d"]]},{"id":"a88a07f39e0e941d","type":"mongodb4","z":"977143edb097b685","g":"e0adf6e180ec1a7c","clientNode":"a0c7c3410ffb0e89","mode":"collection","collection":"customer_data","operation":"find","output":"toArray","maxTimeMS":"0","handleDocId":false,"name":"","x":1270,"y":380,"wires":[["dd3fb62233d5477e"]]},{"id":"0e0129a95e24355c","type":"link in","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"link in 1","links":["fd68d5c864a0e7c9"],"x":285,"y":460,"wires":[["ace58c34d1566f51"]]},{"id":"c7150b3b68fc34a0","type":"link out","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"link out 2","mode":"link","links":["78176aee11393705"],"x":1545,"y":380,"wires":[]},{"id":"3f3ad8e528f5800b","type":"link out","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"link out 3","mode":"link","links":["ecfa23782d33d813"],"x":1255,"y":500,"wires":[]},{"id":"dd3fb62233d5477e","type":"function","z":"977143edb097b685","g":"e0adf6e180ec1a7c","name":"set payload","func":"let previously_stored_customer_data = Array.isArray(msg.payload) ? msg.payload : (msg.payload ? [msg.payload] : []);\nlet latest_customer_data = Array.isArray(msg.data) ? Array.from(msg.data) : (msg.data ? [msg.data] : []);\n\nif (latest_customer_data.length !== 0 && previously_stored_customer_data.length !== 0) {\n    let fullNameArray1 = previously_stored_customer_data.map(customer => customer.full_name);\n    let filtered_customer_data = latest_customer_data.filter(customer => !fullNameArray1.includes(customer.full_name));\n    msg.payload = [filtered_customer_data];\n    return msg\n} else {\n    // Case where there's no previously stored data\n    msg.payload = [latest_customer_data];\n    return msg\n}","outputs":1,"timeout":"","noerr":0,"initialize":"","finalize":"","libs":[],"x":1430,"y":380,"wires":[["c7150b3b68fc34a0"]]},{"id":"a0c7c3410ffb0e89","type":"mongodb4-client","name":"MongoDB Config","protocol":"mongodb+srv","hostname":"","port":"","dbName":"${DATABASE}","appName":"","authSource":"","authMechanism":"DEFAULT","tls":false,"tlsCAFile":"","tlsCertificateKeyFile":"","tlsInsecure":false,"connectTimeoutMS":"30000","socketTimeoutMS":"0","minPoolSize":"0","maxPoolSize":"100","maxIdleTimeMS":"0","uri":"${URI}","advanced":"{}","uriTabActive":"tab-uri-advanced"},{"id":"ac5e2cabb6251314","type":"group","z":"977143edb097b685","name":"","style":{"label":true},"nodes":["d6a4a19c23a0f42b","78176aee11393705","ecfa23782d33d813","dab90d6302b1c885","bc4afbc715e3b64d","d781431fed20d650","f19a1157e25b47df"],"x":254,"y":579,"w":592,"h":222},{"id":"d6a4a19c23a0f42b","type":"mongodb4","z":"977143edb097b685","g":"ac5e2cabb6251314","clientNode":"a0c7c3410ffb0e89","mode":"collection","collection":"customer_data","operation":"insertMany","output":"toArray","maxTimeMS":"0","handleDocId":false,"name":"","x":510,"y":680,"wires":[["bc4afbc715e3b64d"]]},{"id":"78176aee11393705","type":"link 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database","info":"","x":520,"y":620,"wires":[]},{"id":"520f961e35d10da4","type":"group","z":"977143edb097b685","style":{"stroke":"#b2b3bd","stroke-opacity":"1","fill":"#f2f3fb","fill-opacity":"0.5","label":true,"label-position":"nw","color":"#32333b"},"nodes":["9b534cdc46dfc52a","52c6d1c11c84571c","ed6fc24b155aef33","af4bc1c2a6e6d020","572c04ca900af9d5","b64ddca6aab82cb2","a135a8ed2c447561"],"x":254,"y":839,"w":1372,"h":142},{"id":"9b534cdc46dfc52a","type":"ui-chart","z":"977143edb097b685","g":"520f961e35d10da4","group":"1859e113313a5279","name":"","label":"chart","order":2,"chartType":"bar","category":"category","categoryType":"property","xAxisProperty":"","xAxisPropertyType":"msg","xAxisType":"category","yAxisProperty":"value","ymin":"","ymax":"","action":"replace","pointShape":"circle","pointRadius":4,"showLegend":false,"removeOlder":1,"removeOlderUnit":"3600","removeOlderPoints":"","colors":["#1f77b4","#aec7e8","#ff7f0e","#2ca02c","#98df8a","#d62728","#ff9896","#9467bd","#c5b0d5"],"width":"13","height":8,"className":"","x":1550,"y":940,"wires":[[]]},{"id":"52c6d1c11c84571c","type":"function","z":"977143edb097b685","g":"520f961e35d10da4","name":"function 1","func":"let selectedDataForChart = global.get(\"selectedDataForChart\")\n// Assuming msg.payload is an object\nlet data = msg.payload[selectedDataForChart]\nlet result = [];\n\n// Loop through each key in the payload object\nObject.keys(data).forEach(key => {\n    result.push({ category: key, value: data[key] });\n});\n\nmsg.payload = result\nreturn msg;","outputs":1,"timeout":0,"noerr":0,"initialize":"","finalize":"","libs":[],"x":1380,"y":940,"wires":[["9b534cdc46dfc52a"]]},{"id":"ed6fc24b155aef33","type":"ui-dropdown","z":"977143edb097b685","g":"520f961e35d10da4","group":"1859e113313a5279","name":"","label":"Select Option:","tooltip":"","order":1,"width":0,"height":0,"passthru":false,"multiple":false,"options":[{"label":"Based on age group","value":"ageRanges","type":"str"},{"label":"Based on gender group","value":"genderTotals","type":"str"},{"label":"Based on job profile","value":"jobStatusTotals","type":"str"}],"payload":"","topic":"topic","topicType":"msg","className":"","x":360,"y":940,"wires":[["b64ddca6aab82cb2"]]},{"id":"af4bc1c2a6e6d020","type":"file in","z":"977143edb097b685","g":"520f961e35d10da4","name":"","filename":"./customer_data_analytics","filenameType":"str","format":"lines","chunk":false,"sendError":false,"encoding":"none","allProps":false,"x":950,"y":940,"wires":[["572c04ca900af9d5"]]},{"id":"572c04ca900af9d5","type":"json","z":"977143edb097b685","g":"520f961e35d10da4","name":"","property":"payload","action":"","pretty":false,"x":1190,"y":940,"wires":[["52c6d1c11c84571c"]]},{"id":"b64ddca6aab82cb2","type":"change","z":"977143edb097b685","g":"520f961e35d10da4","name":"store selected data in global context","rules":[{"t":"set","p":"selectedDataForChart","pt":"global","to":"payload","tot":"msg"}],"action":"","property":"","from":"","to":"","reg":false,"x":640,"y":940,"wires":[["af4bc1c2a6e6d020"]]},{"id":"a135a8ed2c447561","type":"comment","z":"977143edb097b685","g":"520f961e35d10da4","name":"Visualizing data on charts","info":"","x":490,"y":880,"wires":[]},{"id":"1859e113313a5279","type":"ui-group","name":"chart","page":"f10b4d0259e43aeb","width":"13","height":"1","order":-1,"showTitle":true,"className":"","visible":"true","disabled":"false"},{"id":"f10b4d0259e43aeb","type":"ui-page","name":"home","ui":"cb79bc4520925e32","path":"/","icon":"","layout":"grid","theme":"2c5d702b11de7dd1","order":1,"className":"","visible":true,"disabled":"false"},{"id":"cb79bc4520925e32","type":"ui-base","name":"My UI","path":"/dashboard","includeClientData":true,"acceptsClientConfig":["ui-notification","ui-control"],"showPathInSidebar":false},{"id":"2c5d702b11de7dd1","type":"ui-theme","name":"Theme Name","colors":{"surface":"#ffffff","primary":"#0094ce","bgPage":"#eeeeee","groupBg":"#ffffff","groupOutline":"#cccccc"}}]
@@ -223,7 +223,7 @@ While Node-RED is versatile, there are some limitations to consider while using
 
 - Advanced Features: Some advanced ETL features, like automated schema detection and sophisticated error handling, might require additional customization or external modules.
 - Data Governance: Node-RED does not inherently provide robust data governance and lineage tracking, which are often essential in enterprise ETL tools.
-- Scalability: While Node-RED can handle many tasks effectively, it might not offer the same level of optimization for extremely large datasets compared to dedicated ETL tools.
+- Scalability: While Node-RED can handle many heavy tasks, but it may not offer the same level of optimization for processing extremely large datasets compared to dedicated ETL tools.
 
 ## Additional resources
 

From 71ff22da93cda7d3e7c56c06c69b617f54a00d01 Mon Sep 17 00:00:00 2001
From: "sumit shinde ( Roni )"
 <110285294+sumitshinde-84@users.noreply.github.com>
Date: Mon, 17 Jun 2024 11:16:35 +0530
Subject: [PATCH 12/13] Apply suggestions from code review

Co-authored-by: Zeger-Jan van de Weg <ZJvandeWeg@users.noreply.github.com>
---
 src/blog/2024/06/using-node-red-as-an-etl-tool.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/blog/2024/06/using-node-red-as-an-etl-tool.md b/src/blog/2024/06/using-node-red-as-an-etl-tool.md
index d72f602096..4ca442585a 100644
--- a/src/blog/2024/06/using-node-red-as-an-etl-tool.md
+++ b/src/blog/2024/06/using-node-red-as-an-etl-tool.md
@@ -1,7 +1,7 @@
 ---
-title: Using Node-RED as an ETL Tool.
-subtitle: A Step-by-Step Guide on How to Use Node-RED as an ETL Tool.
-description: Learn how to utilize Node-RED as an ETL (Extract, Transform, Load) tool, and explore its advantages and disadvantages in this context.
+title: Using Node-RED as an ETL Tool
+subtitle: A Step-by-Step Guide to use Use Node-RED for your industrial ETL Tool.
+description: Learn how to utilize Node-RED as an ETL (Extract, Transform, Load) tool for your data industrial data ops.
 date: 2024-06-05
 authors: ["sumit-shinde"]
 image:
@@ -24,7 +24,7 @@ ETL (Extract, Transform, Load) is essential for integrating and analyzing data,
 
 ## Node-RED as an ETL tool
 
-Node-RED is specifically used for building various types of IoT applications. During the development of such applications, we sometimes used Node-RED as an ETL is tool without even realizing it. For instance, when monitoring sensors across a facility, the process involves extracting data from sensors, transforming (cleaning and formatting) sensor data, and loading it into a database. There are thousands of other such examples. This demonstrates that Node-RED has already been adopted as an ETL tool at a certain level.
+Node-RED is specifically used for building various types of IoT applications. Data ops workload are part of these use-cases, and are often deployed without thinking of them as DataOps. For instance, when monitoring sensors across a facility, the process involves extracting data from sensors, transforming (cleaning and formatting) sensor data, and loading it into a database. There are thousands of other such examples.
 
 *Note: We have mentioned only a few nodes that can be used in the ETL process, but there are thousands of other nodes that you can explore yourself by visiting the [Node-RED Nodes Library](https://flows.nodered.org/search?type=node)*
 

From 75b3f8122102d6bc9f973fe5bad60c6b9a614274 Mon Sep 17 00:00:00 2001
From: "sumit shinde ( Roni )"
 <110285294+sumitshinde-84@users.noreply.github.com>
Date: Mon, 17 Jun 2024 13:40:11 +0530
Subject: [PATCH 13/13] Update using-node-red-as-an-etl-tool.md

---
 src/blog/2024/06/using-node-red-as-an-etl-tool.md | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/src/blog/2024/06/using-node-red-as-an-etl-tool.md b/src/blog/2024/06/using-node-red-as-an-etl-tool.md
index 4ca442585a..b6888e58ff 100644
--- a/src/blog/2024/06/using-node-red-as-an-etl-tool.md
+++ b/src/blog/2024/06/using-node-red-as-an-etl-tool.md
@@ -14,13 +14,15 @@ tags:
   - business intelligence
 ---
 
-ETL (Extract, Transform, Load) is essential for integrating and analyzing data, helping businesses unlock detailed insights. You already know Node-RED for its user-friendly approach to creating IoT applications, but did you know it can also be a powerful tool for ETL tasks? When IBM published a blog about using Node-RED for ETL, it caught a lot of attention and got people talking about its potential in this space. In this guide, we'll walk you through how to use Node-RED for ETL, sharing its strengths and weaknesses along the way.
+ETL is the process data-driven organizations use to gather data from diverse sources and merge it to support discovery, reporting, analysis, and decision-making. While Node-RED is widely known as a low-code visual programming tool for building IoT applications, it also has significant potential for ETL tasks. This potential was highlighted when IBM published a blog about using Node-RED for ETL that sparked interest and discussion in the community.
 
 <!--more-->
 
+In this guide, we'll walk you through how to use Node-RED as an ETL tool for your data industrial data ops, its strengths and weaknesses along the way.
+
 ## What is ETL
 
-[ETL](https://en.wikipedia.org/wiki/Extract,_transform,_load) (Extract, Transform, Load) is a fundamental process in managing data efficiently. It consists of three main stages: extraction, transformation, and loading. In the extraction phase, data is gathered from various sources such as databases, APIs, or files. Then, in the transformation stage, the data is converted into a format that is suitable for analysis by cleaning, filtering, and structuring it. Finally, in the loading stage, the transformed data is stored in a target database or data warehouse for further analysis and decision-making. ETL plays a significant role in ensuring that data is organized, consistent, and readily available for generating insights and making informed decisions in businesses and organizations.
+[ETL](https://en.wikipedia.org/wiki/Extract,_transform,_load) (Extract, Transform, Load) is a fundamental process in managing data efficiently. It consists of three main stages: extraction, transformation, and loading. In the extraction phase, data is gathered from various sources such as SCADA systems, IoT devices, industrial sensors, APIs and more. In the transformation stage, the data is converted into a format that is suitable for analysis by cleaning, filtering, and structuring it. For example, this may involve converting raw sensor data into operational metrics. Aggregating time-series data over intervals (e.g., hourly, daily) to analyze trends and patterns in process performance. Filtering and analyzing event logs are for monitoring system performance and identifying potential issues. Finally, in the loading stage, the transformed data is stored in a target database or data warehouse for further analysis and decision-making. ETL plays a significant role in ensuring that data is organized, consistent, and readily available for generating insights and making informed decisions in businesses and organizations.
 
 ## Node-RED as an ETL tool
 
@@ -30,11 +32,13 @@ Node-RED is specifically used for building various types of IoT applications. Da
 
 ### Extracting
 
-Node-RED can extract data from various sources, including APIs, databases, local filesystems, and IoT devices using built-in nodes and community-contributed nodes. For example, the HTTP request node can be used to pull data from web services, while nodes for MySQL, MongoDB, and PostgreSQL can extract data from databases. Nodes for MQTT and Kafka can fetch data from message brokers. File nodes enable extraction of data from local filesystems, while different cloud nodes for platforms like AWS, GCP, and IBM Watson allow extraction of data from cloud storage services. Moreover, Node-RED running on edge devices can extract data from sensors connected directly to them.
+Node-RED can extract data from various sources, including APIs, databases, local filesystems, and IoT devices using built-in nodes and community-contributed nodes. For example, the HTTP request node can be used to pull data from web services, while nodes for MySQL, MongoDB, and PostgreSQL can extract data from databases. Nodes for MQTT and Kafka can fetch data from message brokers. File nodes enable extraction of data from local filesystems, while different cloud nodes for platforms like AWS, GCP, and IBM Watson allow extraction of data from cloud storage services. Nodes for OPC-UA, Modbus, EtherNet/IP can be used to extract data from SCADA systems, PLCs, RTUs, and more. Moreover, Node-RED running on edge devices can extract directly extract data from devices.
 
 ### Transforming
 
-Transforming data involves cleaning, structuring, and applying business rules. This can be done using function nodes with custom JavaScript, along with nodes like JSON, Split, Join, Change, CSV, and XML for parsing and formatting.
+Transforming data involves cleaning, structuring, and applying business rules. This can be done using various nodes in Node-RED, including function nodes with custom JavaScript, along with nodes like JSON for parsing or stringifying JSON data, CSV for parsing CSV data into JSON or stringifying JSON into CSV, Split for breaking data into parts, Join for combining data and calculating total of data, Switch for conditional routing, Change for mapping and filtering data, Sort for ordering data, and XML for parsing and formatting.
+
+For example, imagine you have extracted sensor data objects from factory floor devices. Using Node-RED, you can convert that object into JSON using JSON nodes, route it based on device IDs with Switch nodes, filter out irrelevant sensor readings with Change nodes, apply custom business rules with Function nodes, sort it chronologically with Sort nodes and format it into XML with XML nodes for analysis and reporting.
 
 ### Loading