Big Data Concepts and Tools - Big Data and Data Warehousing

4 important questions on Big Data Concepts and Tools - Big Data and Data Warehousing

What is actionable data?

It is often the result of extensive data analytics and other data processing; in short it is an analytics result that provides enough data for managers and organizations to make an informed decision.

What are the challenges facing data warehousing and Big Data? Are we witnessing the end of the data warehousing era? Why or Why not?

Challenges facing DWH and Big Data: It is the variety and velocy of data that forced IT to develop "Big Data" not the volume.
Hadoop and RDBMS are not interchangeable, the differences overwhelm the similarities.

What are the use cases for data warehousing and RDBMS?

Data ware house performance: standard, but Hadoop is gaining on performance
Integrating data that provides business value: Nowhere else in the entire IT data center is data collected, cleaned and integrated as it is in the data warehouse.
Interactive BI tools: force of tools that which business users enable o demand more reports than IT has staffing to provide. Users start asking more strategic questions in stead of trivial question. Power users!

In what scenario's can Hadoop and RDBMS coexist?

Use Hadoop for storing and archiving multistructured data: connector RDMS<>Hadoop e.g. Aster-Hadoop connector
Use Hadoop for filtering, transforming, and/or consolidating multistructured data: Aster-Hadoop connector
Use Hadoop to analyze large volumes of multistructured data and publish the analytical results: result can be posted back to the RDMS
Use a RDBMS that provides MapReduce capabilities as an investigative computing platform: mixture of SQL and MapReduce analytic functions
Use a front-end query to access and analyze data: data stored in both

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