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Implementing Slow Changing Dimensions in a Data Warehouse using Hive and Spark

Hive Project- Understand the various types of SCDs and implement these slowly changing dimesnsion in Hadoop Hive and Spark.
What are the prerequisites for this project?
  • It is expected that students have a fair knowledge of Big Data and Hadoop.
  • Installation Cloudera Quickstart VM.

What will you learn

  • What is slow changing dimension (scd)
  • Types of slow changing dimension
  • Updates and transactions in Hive
  • Implementing SCD 2 & 3 in Hive
  • Implementing SCD 2 & 3 in Spark

Project Description

One of the broadest use of Hadoop today is building data warehousing platform off a data lake. And in building a data warehouse, the traditions left us by Kimball and Inmon is still very much in play.

Why not every one of the legacy rules should be implemented as as-is in the big data platform, the issue of slow-changing dimensions is still a front-burner.

The slow changing dimension of warehouse dimension that is said to rarely change. However, when they change, there should be a systematic approach to capturing that change. Examples of SCDs are customer and products information.

In this hive project, we will look at the various types of SCDs and learn to implements SCDs in Hive and Spark.



Big Data & Enterprise Software Engineer

I am passionate about software development, databases, data analysis and the android platform. My native language is java but no one has stopped me so far from learning and using angular and node.js. Data and data analysis is thrilling and so are my experiences with SQL on Oracle, Microsoft SQL Server, Postgres and MyS see more...

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