Skip to content

Latest commit

 

History

History
204 lines (164 loc) · 6.81 KB

README.md

File metadata and controls

204 lines (164 loc) · 6.81 KB
Raw


DE ZoomCamp

Data Engineering Zoomcamp 2024

Free Data Engineering course!
Structure · Deadlines · Modules · Requirements · License

This repository contains my scripts, answers & solutions to the Data Engineering Zoomcamp 2024.

📁 Structure 📁

This is the general top level directory structure of this repository:

├───.github
│   ├─── ...
├───01-docker-terraform
│   ├─── ...
├───02-workflow-orchestration
│   ├─── ...
├───02-workshop-1-data-ingestion
│   ├─── ...
├───03-data-warehouse
│   ├─── ...
├───04-analytics-engineering
│   ├─── ...
├───05-batch
│   ├─── ...
├───05-workshop-2-stream-processing
│   ├─── ...
├───06-streaming
│   ├─── ...
├───docs
│   ├─── ...
├───images
│   ├─── ...
├───notes
│   ├─── ...
└───scripts
│   ├─── ...

⏳ Deadlines ⏳

ID Module Progress Deadline
1 Introduction & Prerequisites ✅ 29/01/2024
2 Workflow Orchestration ✅ 05/02/2024
3 DLT Workshop ✅ 15/02/2024
4 Data Warehouse ✅ 12/02/2024
5 Analytics Engineering ❌ 22/02/2024
6 Batch Processing ❌ 04/03/2024
7 Streaming ❌ 15/03/2024
8 RisingWave Workshop ❌ 18/03/2024
9 Project (Attempt 1) ❌ 01/04/2024
10 Project Evaluation (Attempt 1) ❌ 08/04/2024
11 Project (Attempt 2) ❌ 15/04/2024
12 Project Evaluation (Attempt 2) ❌ 29/04/2024

🚀 Modules 🚀

Module 1: Containerization and Infrastructure as Code

  • Course overview
  • Introduction to GCP
  • Docker and docker-compose
  • Running Postgres locally with Docker
  • Setting up infrastructure on GCP with Terraform
  • Preparing the environment for the course
  • Homework

Module 2: Workflow Orchestration

  • Data Lake
  • Workflow orchestration
  • Workflow orchestration with Mage
  • Homework

Workshop 1: Data Ingestion

  • Reading from apis
  • Building scalable pipelines
  • Normalising data
  • Incremental loading
  • Homework

Module 3: Data Warehouse

  • Data Warehouse
  • BigQuery
  • Partitioning and clustering
  • BigQuery best practices
  • Internals of BigQuery
  • BigQuery Machine Learning

Module 4: Analytics engineering

  • Basics of analytics engineering
  • dbt (data build tool)
  • BigQuery and dbt
  • Postgres and dbt
  • dbt models
  • Testing and documenting
  • Deployment to the cloud and locally
  • Visualizing the data with google data studio and metabase

Module 5: Batch processing

  • Batch processing
  • What is Spark
  • Spark Dataframes
  • Spark SQL
  • Internals: GroupBy and joins

Module 6: Streaming

  • Introduction to Kafka
  • Schemas (avro)
  • Kafka Streams
  • Kafka Connect and KSQL

Workshop 2: Stream Processing with SQL

Project

Putting everything learned to practice.

  • Week 1 and 2: Working on the project
  • Week 3: Reviewing peers
DE ZoomCamp

(back to top)

🛠️ Technologies 🛠️

  • Python: Versatile and widely-used programming language for web development, data science, and automation.
  • PostgreSQL: Open-source relational database management system known for its extensibility.
  • Docker: Platform for developing, shipping, and running applications in containers.
  • Mage: Workflow Orchestrator that builds real-time and batch pipelines to transform data.
  • BigQuery: Fully-managed, serverless data warehouse on Google Cloud Platform (GCP).
  • GCP: Google Cloud Platform, a suite of cloud computing services.
  • dbt: Analytics engineering tool for transforming data in your warehouse.
  • Apache Spark: Open-source distributed computing system for big data processing.
  • dltHub: An open source Python library that makes data loading easy.
  • RisingWave: Stream processing platform that utilizes SQL to enhance data analysis
  • Apache Kafka: Distributed event streaming platform for building real-time data pipelines.

(back to top)

📜 License 📜

MIT License

Copyright (c) 2024 Jonathan Areas

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

(back to top)