Data in the Warehouse and Data in Hadoop

In our experience, traditional warehouses are mostly ideal for analyzing structured data from various systems and producing insights with known and relatively stable measurements. On the other hand, we feel a Hadoop-based platform is well suited to deal with semi structured and unstructured  data,  as  well  as  when  a  data  discovery  process  is  needed. That  isn’t  to  say  that Hadoop can’t be used for structured data that is readily available in a raw format. 

Processing Structured Data is something that your traditional database is already very good  at.  After  all,  structured  data,  by  definition  is  easy  to  enter,  store,  query  and  analyze.  It conforms nicely to a fixed schema model of neat columns and rows that can be manipulated by Structured Query Language (SQL) to establish relationships. As such, using Hadoop to process semi-structured and unstructured data is raw, complex,  and  pours  in  from  multiple  sources  such  as  emails,  text  documents,  videos,  photos, social media posts, Twitter feeds, sensors and click streams. 

Storing,  managing  and  analyzing  massive  volumes  of  semi-structured  and unstructured data is what Hadoop was purpose-built to do. Hadoop as a Service provides a scalable solution to meet ever-increasing data storage and processing demands that the data warehouse can no longer handle. With its unlimited scale and on-demand  access  to  compute  and  storage capacity.  Hadoop  as  a  Service  is  the  perfect  match for big data processing. 

Keeping  costs down is a concern for every business And  traditional  relational  databases  are  certainly  cost  effective.  If  you  are  considering adding Hadoop  to  your  data  warehouse,  it’s  important  to  make  sure  that  your  company’s  big  data demands are genuine and that the potential benefits to be realized  from implementing Hadoop will outweigh the costs.

Shorter time-to-insight necessitates interactive querying via the analysis of smaller data sets  in  near  or  real-time.  And  that’s  a task  that  the  data  warehouse  has  been  well  equipped  to handle. However, thanks to a powerful Hadoop processing engine called Spark, Hive,Hbase ..