How Data Warehousing and Analytics Linked together

Here’s a quick comparison between Data Warehousing and Analytics.

Data Warehousing Tools

In the mainframe environment, the top database is DB2. How to get refined data? it is a big challenge. Check here for fundamentals.

If we see the global markets, the business scenario is changing now. So business owners need strategic data for making decisions. Below are the tools to help work on Data Warehousing(DW).

  • Oracle BI tools
  • IBM BI tools
  • Microsoft BI tools

Data warehousing Top Components

  1. production data sources- Data generated in production boxes
  2. data extraction and conversion- Extract the data from production boxes and convert into a suitable format.
  3. the data warehouse database management system – Management of Data warehouse performance.
  4. data warehouse administration- Administration of the data warehouse.
  5. business intelligence (BI) tools– BI tools like Tableau, QlikView etc.

SQL Queries in Warehousing

  • very large fact tables often running into billions of rows
  • a large number of dimension tables
  • a large number of joins
  • large aggregation queries
  • large amounts of data ingested into the warehouse


The data stored in warehouse is not strategic level. The level of granularity is less. Future prediction is the big plus in Analytics. This you achieve through numerous computer models and statistical methods.

Top Analytics Tools

  1. AWS Analytics
  2. Cloudera
  3. Microsoft Analytics
  4. Splunk
  5. Teradata

The Bottom Line


Analytics play prime role in predictions and analysis of stream data. In the case of DW the data is not current.

Analytics Flow Chart

Photo credit: Srini

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Author: Srini

Experienced software developer. Skills in Development, Coding, Testing and Debugging. Good Data analytic skills (Data Warehousing and BI). Also skills in Mainframe.