Demystifying DataOps — Series: 5.
9 min readSep 10, 2023
DataOps
DataOps (Data Operations) is an agile, process-oriented methodology for developing and delivering data analytics. It involves practices and technologies for improving the quality and speed of analytics, including statistics, data exploration, and machine learning. The goal is to improve collaboration and accelerate the delivery cycle of data analytics, much in the same way that DevOps has done for software development.
DataOps brings together the following disciplines:
- Data Engineering: Management of data flow, storage, and architecture.
- Data Integration: Combining data from different sources, possibly using different technologies.
- Data Quality: Ensuring the data used is accurate, consistent, secure, and used responsibly.
- Data Analytics: Using statistical and machine learning methods to derive insights from data.
- Data Visualization: Displaying data in graphical format to support decision-making.
It usually incorporates automation, continuous integration/continuous delivery (CI/CD) pipelines for data, monitoring, and other software engineering best practices to improve the data analytics lifecycle. Here are some of the key benefits and features of a DataOps approach:
