Master Data Warehouse Concepts - Step by Step from Scratch (Part 3) | Inf Sid | Skillshare

Master Data Warehouse Concepts - Step by Step from Scratch (Part 3)

Inf Sid, ETL/Data Architect

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14 Lessons (55m)
    • 1. Importance of Database Indexes in Data Warehouse

    • 2. BitMap Indexes

    • 3. BTree Indexes

    • 4. BTree Vs BitMap

    • 5. Data Integration and ETL

    • 6. What is ETL?

    • 7. Common Questions on DI and ETL

    • 8. ETL Defined

    • 9. ELT Defined

    • 10. ETL Vs ELT Differences

    • 11. ETL Buy Vs Build

    • 12. Different ETL Tools

    • 13. Full and Incr Extraction

    • 14. Differences bwtween Full Load and Incremental Loads


About This Class

This is the third part in the series of Master Data Warehouse Concepts - Step by Step from Scratch

In this course, you would be learning all the concepts and terminologies related to the Data warehouse , such as the OLTP, OLAP, Dimensions, Facts and much more, along with other concepts related to it such as what is meant by Start Schema, Snow flake Schema, other options available and their differences.
It also explains how the data is managed with in the Data Warehouse and explains the process of reading and writing data onto the Warehouse. Later in the course you would also learn the basics of Data Modelling and how to start with it logically and physically. You would also learn all the concepts related to Facts, Dimensions, Aggregations and commonly used techniques of ETL.
Upon completion of this course, you would have a clear idea about, all the concepts related to the Data Warehouse, that should be sufficient to help you start off with the next step of becoming an ETL developer or Administering the Data warehouse environment with the help of various tools.
All the Best and Happy Learning !





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Inf Sid

ETL/Data Architect

Business Intelligence Consultant and Trainer with 14+ years of extensive work experience on various client engagements. Delivered many large data warehousing projects and trained numerous professionals on business intelligence technologies. Extensively worked on all facets of data warehousing including requirement gathering, gap analysis, database design, data integration, data modeling, enterprise reporting, data analytics, data quality, data visualization, OLAP.

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