DataOps - Devops for Big Data and Analytics

DataOps - Devops for Big Data and Analytics

Big Data Needs DevOps and Agile Practices as DataOps Principles for Continuous Delivery and Integration Pipeline with Different Tools

Dataops – Data Operations for Analytics

DataOps is a Data Operation, and it is the latest Agile operations method from the collective of IT and Big Data professional. It works on Data Management practices and processes which improves the accuracy of analytics, speed, automation including data access, integration, and management. It also helps in managing data with goals for that data. DataOps combines Agile Development, DevOps and Statistical Process controls and applies them to Data Analytics.

How did Dataops Principles Implement?

DataOps is a Combination of Data + Operations, as supporting an iterative lifecycle for data flow –

  • Build
  • Execute
  • Operate
  • Protect

Build – Build is a design topology of repeatable data flow pipelines, flexible using configuration tools rather than hard coding. Cross-functional teams build adaptable, repeatable data flow topologies.

Execute – On Edge system run pipelines and also run a pipeline in Autoscaling On-premises Cluster or Cloud-environment. Across Multiple Cloud and On-premises.

Operate – Continuous Monitoring manages data flow performance. Monitor Pipelines, gather metrics, fulfil SLA’s.

Protect – Data protection done by DataOps tools integrated with unauthorized access, data stores, authorized systems, and authentication. Handles sensitive data, provide metadata to governance systems.

insights data visualization

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