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.