“Companies are marketing DataOps products & services, & organizations are adopting DataOps to improve the efficiency, quality & cycle time of their data analytics”.
DataOps is a new approach to the end-to-end data lifecycle, which applies new processes and methodologies to data analytics. 1) 4 key software components of a DataOps Platform: A) data pipeline orchestration. DataOps needs a directed graph-based workflow that contains all the data access, integration, model & visualization steps in the data analytic production process. B) automated testing & production quality alerts. Automatically tests and monitors the production quality of all data & artifacts in the data analytic production process as well as testing the code changes during the deployment process. C) deployment automation and development sandbox creation. DataOps continuously moves code and configuration continuously from development environments into production. D) Data Science Model Deployment. DataOps-driven data science teams make reproducible development environments and move models into production. Called MLOps. 2) DataOps Supporting Functions. There are many software components that play a critical supporting role in the DataOps ecosystem: A) Code & Artifact storage. B) Parametrization and secure key storage. C) Distributed computing. D) Data Virtualization, Versioning, and Test Data Management. E) Big Data Performance Management. 3) Other Vendors Talking DataOps. There are many software components that are messaging on DataOps: A) Data Integration & Unification with a DataOps Message. B) All-in-One Cloud Platforms talking DataOps. C) Service and Consulting Organizations with a DataOps slant.