Data warehousing tools vs analytics these are top differences



Role of Tools in
Data warehousing

The mainframe is a legacy operating system. Billions and trillions of bytes of data we already added in. But, we are not getting refined data from it. So nowadays archiving all the data to DW to make decisions.

Data warehousing in Mainframe

In the mainframe environment, the big database is DB2. How to get refined data? it is a big challenge.

So the strategic data we arrive through Data warehousing and its Tools. I found one good community, you can 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.

Top tools in Data Warehousing

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

Data warehousing Flow

Top component in any data warehouse

  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.

Next Step After Data Warehousing

The data stored in data warehouse utilized by companies is not up to the level.

The Gartner Top List of Analytics Tools

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

The Bottom Line

Analytics

Data warehousing still useful for batch model projects. The analytics tools so useful when the data is streaming.

Types of SQL Queries in Data 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

Data Flow in Real-time Analysis

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.