DATA WAREHOUSE

DATA WAREHOUSE
Not same as database but stores copy of OLTP database
Stores data from other sources (IOT, Analytics, Metadata)
Relational database designed for Analytical workload
Collects and aggregates data from many sources
Handles big data well and intends to solve big data problems
OLAP systems, separate from Operational system
Data is usually structured in form of Dimensional model
ACID requirement is less strict than a database systems
Enable Business Intelligence which provides business insights
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Data Warehouse Example Use-Cases
Social Media Platform
Collect data related to users, group, locations, etc. This is stored in a Data Warehouse and drives insights for ads campaigns.
Banking
Analyze spending patterns between accounts using data warehouses to see spending patterns and fraud detection.
Governments
To detect tax fraud they store and analyze tax payments within the data warehouses.
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Characteristic of Data Warehouse
Subject-Oriented
Focused on a subject (Customer, marketing, sales).
Doesn’t focus on operational day to day.
Simple concise view around a specific subject
Optimise to answer analytical questions
Exclude unhelpful data to support decision process
Integrated
Hold data from all different sources and entire organisations
Well defined naming convention, measure & attributes
Extract data is uniformly transformed
Time-variant
Data is organised in time-periods
Allows historical analysis
Contains element of time either implicitly or explicitly
Once data is stored it cant be modified*
Non-volatile
Data is permanent, doesn’t get deleted
Data is read-only
Only loading and accessing data is allowed
Data keeps growing
Data is refreshed at scheduled time
Does not require transaction process and concurrency control mechanism
Delete, update, inserts ate omitted
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Mas Ghaly, a Data Engineer with experience at a well-regarded company SuperApp (YC W18), will share his valuable insights from his course and the field.
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