Difference betweenDevOps and DataOps

This is one of the most confusing topics now a days as data is the most useable element of organizations now a day.

I won’t be going into details as we have discussed each as a separate topic but will only try to elaborate the difference between both.

Below is the high-level difference between both the practices.

• DevOps focuses on development by software developers, quality, and operations whereas DataOps focuses on source and target integrations, data transformation from data engineers, data quality and security.
• DevOps makes sure there is no hindrance in environment for HIGH QUALITY development cycle whereas DataOps makes sure, end users get HIGH QUALITY data, trusted data, ready to use data and actionable KPIs.
• DevOps make sure, all IT team members, operational team members, business team members are aligned to SPEED-UP the process after every sprint where DataOps makes sure, IT team members, development team members and business team members roles and responsibilities are aligned for FASTER collaborative work across teams.
• DevOps make sure, automation of all the servers management, versions control, configurations, testing, release management, etc. are AUTOMATED whereas DataOps makes sure, data acquisition, integration, metadata management, self-service, data governance, cross cloud, etc. are all in place and working in AUTOMATED fashion.
• DevOps works with stakeholders for real-time feedback after every sprint for FEEDBACK-ORIENTED optimization whereas DataOps works with stakeholders to get regular insights from new data flowing-in in real-time or in batch for RESULT-ORIENTED optimization.
• DevOps goes for regular implementations for QUICK recovery to cater failures in agile mode whereas DataOps goes for regular implementations to QUICKLY fix data quality and minimum metadata discovering in agile mode.

Below are the workflows for both DevOps and DataOps.

• DevOps focuses on whatever developers builds 🡪 codes 🡪 unit test 🡪 performance 🡪 SIT 🡪 UAT 🡪 PROD deployment 🡪 business value.
• DataOps focuses on data i.e., data pipelining 🡪 transformations 🡪 🡪 data look and feel 🡪 business value.