DataOps

Way to DataOps

DataOps focuses on the end-to-end delivery of data. In the digital era, companies need to harness their data to derive competitive advantage. In addition, companies across all industries need to comply with new data privacy regulations. The need for DataOps can be summarised as follows:

• More data is available than ever before
• More users want access to more data in more combinations

DataOps

When development and operations don’t work in concert, it becomes hard to ship and maintain quality software at speed. This led to the need for DevOps. 
DataOps is similar to DevOps, but centered around the strategic use of data, as opposed to shipping software. DataOps is an automated process oriented methodology used by Big Data teams to improve the quality and reduce the cycle time of Data Analytics. It applies to the entire data life cycle from data preparation to reporting.
It includes automating different stages of the work flow including BI, Data Science and Analytics.DataOps speeds up the production of applications running on Big Data processing frameworks. 

Components 

DataOps include the following components:

• Data Engineering
• Data Integration
• Data security
• Data Quality

DevOps and DataOps

• DevOps is the collaboration between Developers, Operations and QA Engineers across the entire Application Delivery pipeline, from Design and Coding, to Testing and Production Support. While DataOps is a Data Management method that emphasizes communication, collaboration, integration and automation of processes between Data Engineers, Data Scientists and other Data professionals.
• DevOps mission is to enable Developers and Managers to handle modern web based Application development and deployment. DataOps enables data professionals to optimize modern web based data storage and analytics.
• DevOps focuses on continuous delivery by leveraging on demand IT resources and by automating Testing and Deployment. While DataOps tries to bring the same improvements to  Data Analytics.

Steps to implement DataOps

The following are the 7 steps to implement DataOps:

1. Add Data and logic tests
2. Use a version control system 
3. Branch and Merge Codebase
4. Use multiple environments 
5. Reuse and containerize
6. Parameterize processing 
7. Orchestrate data pipelines 

The above details are based on the learnings gathered from different Internet sources. 

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