Physical logical design til

README.md

@@ -56,6 +56,15 @@ This repository serves as a collection of things learned by people working in AG
 
 - [Basic SQLite3](sqlite3/basic-sqlite3.md)
 
+### Data Warehouse
+
+- [Logical Design for Data Warehouse](data-warehouse/logical-design.md)
+- [Physical Design for Data Warehouse](data-warehouse/physical-design.md)
+
+### ETL
+
+- [Basic ETL](etl/basic-etl.md)
+
 ## Contributing
 
 If you want to share your learnings for today, please check out CONTRIBUTING.md

data-warehouse/logical-design.md

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+# Data Warehouse: Logical Design
+
+**Date: June 29, 2016**
+
+### Logical Design
+
+Being able to identify logical relationships between objects.
+
+#### Factors to consider
+
+- data content
+- relationships of data
+- data warehouse environment
+- data transformation requirement
+- frequency of refresh
+
+#### Components
+
+- entity
+- attribute
+- relationship
+
+#### Output
+
+- should present entity and attributes as fact tables and dimensions
+- should be able to have a model of data from source to subjective information
+
+### References
+- [Oracle9i Data Warehousing Guide](https://docs.oracle.com/cd/B10501_01/server.920/a96520/toc.htm)
+- [Intricity101 videos](https://www.youtube.com/user/Intricity101/videos)
+
+### Author
+
+Almer Mendoza

data-warehouse/physical-design.md

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+# Data Warehouse: Physical Design
+
+**Date: June 29, 2016**
+
+### Physical Design
+
+Taking ways and effectivity of storage into consideration.
+
+#### Structures
+
+###### Tablespaces
+
+Tablespaces are container of Physical Design structures.
+
+###### Tables and partitioned tables
+
+Tables and Partioned Tables are container of raw data. They are the basic unit of storage.
+
+###### Data Segment Compression
+
+Ensures that speed and time spent on execution queries must increase and decrease, respectively.
+
+###### Views
+
+Visualizes data using tables.
+
+###### Integrity Constraints
+
+Adds rules on data manipulation to avoid invalid information.
+
+###### Dimensions
+
+Schema object defining relationships between fields.
+
+###### Materialized Views
+
+Does advance calculations and creates summaries to avoid expensive aggregate operations.
+
+###### Indexes and Partitioned Indexes
+
+Use of indexes to further partition table. Usuaully uses binary digit to signify on what category (or if it is part of the table category).
+
+### References
+- [Oracle9i Data Warehousing Guide](https://docs.oracle.com/cd/B10501_01/server.920/a96520/toc.htm)
+- [Intricity101 videos](https://www.youtube.com/user/Intricity101/videos)
+
+### Author
+
+Almer Mendoza

etl/basic-etl.md

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+# ETL - Basic
+
+**Date: (June 29, 2016)**
+
+ETL or *Extract - Transform - Load* is a process done in data warehousing. It is used when:
+- You want to aggregate data from different sources into one collection (the warehouse)
+- You want to select and arrange only the pertinent information for any kind of analytic, and provide his/her own user view of the data (Data mart)
+- You want to clean the data and make meaningful sense out of it.
+
+ETL can be broken down to three major steps:
+
+1. Extract
+    - The part where you gather data from different data sources (csv files, databases, etc...). Data can also come from different data warehouses.
+    - It should be designed to avoid negative effects on source system such as its performance, response time, or any kind of data locking.
+
+2. Transform
+    - Making the extracted data usable.
+    - This includes mapping the data, matching rows, enhancing data, summarizing data, etc.
+    - Transformation also includes standardizing data (such as currency and time formats) and handling encoding
+
+3. Load
+    - Fetches prepared data and storing them to the data warehouse and database, or data mart.
+
+Source: [ETL Tutorial | Extract Transform and Load](https://www.youtube.com/watch?v=WZw0OTgCBOY)