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Enterprise Data Management (Data Warehouse/ ETL)

 A data warehouse, also known as an enterprise data warehouse (EDW), is a system that stores and analyzes data from multiple sources.

Data warehouses are used for reporting and data analysis, and are considered a core component of business intelligence.

Case study: 

Trends, an affordable fashion & lifestyle retailer which operates more than 2,300 stores in over 1,100 towns and cities across India.

They might have goods stored in different warehouse, They might have enterprise application to track the goods in warehouse and analyze them for improving their business.


A data warehouse has four main components: refer above diagram
  1. Central database:
  2. ETL (extract, transform, load) tools
  3. Metadata (Data sources)
  4. Access tools 
Some examples of data warehouses include:
  1. Snowflake
  2. Google BigQuery
  3. Amazon Redshift
  4. Azure Synapse Analytics
  5. IBM Db2 Warehouse
  6. Firebolt 

Benefits of Data Warehouse:
  1. Understand business trends and make better forecasting decisions.
  2. Data Warehouses are designed to perform well enormous amounts of data.
  3. The structure of data warehouses is more accessible for end-users to navigate, understand, and query.
  4. Queries that would be complex in many normalized databases could be easier to build and maintain in data warehouses.
  5. Data warehousing is an efficient method to manage demand for lots of information from lots of users.
  6. Data warehousing provide the capabilities to analyze a large amount of historical data.

Enterprise Data Management ( EDM ) governance and control:

Enterprise data management (EDM) is the process of defining, integrating, and retrieving data for internal applications and external communication. 

EDM also involves managing the people who access the data, ensuring that they have the right information and follow the organization's standards for storing data. 

EDM focuses on creating accurate, consistent, and transparent content. 


EDM Architecture:

Data Management Maturity Model:


Extract, transform, and load (ETL) is the process of combining data from multiple sources into a large, central repository called a data warehouse. ETL uses a set of business rules to clean and organize raw data and prepare it for storage, data analytics, and machine learning (ML).
Here are some advantages of ETL:
  1. Data governance: ETL fosters data democracy, which broadens data accessibility and makes it available to all the stakeholders to analyze it and use it for business
  2. Reduce delivery time: ETL can reduce delivery time
  3. Reduce unnecessary expenses: ETL can reduce unnecessary expenses
  4. Automate complex processes: ETL can automate complex processes
  5. Validate data before migration: ETL can validate data before migration 
Amazon Redshift:





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