Thursday, July 19, 2018

5 Pillars of Data Management for Data Analytics

Basic Data Management Principles for Data Analytics

Data is the lifeblood for Big data analytics and all the AI/ML solutions built on top.

Here are 5 basic data management principles that must never be broken.



1. Secure Data at Rest

  • Most of the data is stored in storage systems which must be secured.
  • All data in storage must be encrypted  


2. Fast & Secure Data Access 

  • Fast access to data from databases, storage systems. This implies using fast storage servers and FC SAN networks.  
  • Strong access control & authentication is essential


3. Manage Networks for Data in Transit

  • This involves building fast networks - a 40Gb Ethernet for compute clusters and 100Gb FC SAN networks
  • Fast SD-WAN technologies ensure that globally distributed data can be used for data analytics.


4. Secure IoT Data Stream

  • IoT endpoints are often in remote locations and have to be secured.
  • Corrupt data from IoT will break Analytics.
  • Having Intelligent Edge helps in preprocessing IoT data - for data quality & security


5. Rock Solid Data backup and recovery

  • Accidents & Disasters do happen. Protect from data loss & data unavailability with a rock solid data backup solutions.
  • Robust disaster recovery solutions can give zero RTO/RPO.


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