The increasing amount of data generated from various resources had lead to the rise of an approach called DataOps. DataOps which refers to modern approach to data management has bring about many solution to the organisation today. While its implementation is not an easy task and require small steps to achieve big results, a successful DataOps promises great achievement. Among those are enhanced collaborations between the teams, better data quality as well as improved data governance. As the amount of data will continue to grow in future, DataOps organisation is the future organisation. Therefore, what does DataOps organisation look like?
First and foremost, a DataOps organisation involved the adoption of architecture and processes that provide various business insights through rapid development and deployment of high quality data analytic pipelines.
DataOps processes can be described as in the figure below:
In DataOps organisation, statistical process control or SPC constantly monitor and control data analytics pipeline, thus issues on data flow can be detected and notified to the analytics team through automated alert.
DataOps architecture, on the other hand, involves support for environment creation and management. With this, development, test and production environment are implemented separately thus provide support for orchestration, monitoring and test automation. Test automation verifies data that enters the system and alerted the data analytics team in real-time where only high quality data to enter the pipeline, making DataOps organisation to almost completely reduce unplanned work due to errors.
DataOps is not just about process and adoption of technology but a collaboration. In DataOps organisation, the DataOps architecture work in heterogeneous tools and systems as every environment agent runs on behalf of the DataOps Platform to manage code and configuration, perform tasks, and return test results, logs, as well as runtime information. Thus, the teams are able to work independently and also collaborate with other teams to give better products to the customer.
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Other posts you may be interested in:
1. Establishing a Successful DataOps Practice
2. What are the Benefits of DataOps in Organisation?
3. What is DataOps – Origin, Evolution and Future
