DataOps

DataOps is a set of practices, processes and technologies that combines an integrated and process-oriented perspective on data with automation and methods from agile software engineering to improve quality, speed, and collaboration and promote a culture of continuous improvement in the area of data analytics.

At Data Engineers we are big believers that you can bring the same rigor to the Data Platforms as you would do when developing software. From being able to use source control through to performing unit testing through to continuous integration and deployments we are now able to apply the same to the world of Data.

Traditionally its been hard to do repeatable deployments and testing on a database due to the need to take a copy of an existing database and restore it just for testing purposes. Therefore most organization's have cut down versions of their real databases or don't both at all as it adds too much time to perform the action and potentially use too much disk space up. Same for development, data engineers share a dev database rather than their own instance of a full database. Utilizing Snowflake's Zero Copy Clone Feature each data engineers can get their own database so data engineers can work atomically and each build can run unit tests against it a full copy of the production database, ensuring you know the results before you get to production.

What is the difference between DevOps and DataOps?

DataOps builds upon the DevOps development model. The DevOps process flow includes a series of steps that are common to software development projects

Develop — create/modify an application
Build — assemble application components
Test — verify the application in a test environment
Deploy — transition code into production
Run — execute the application

Where as DataOps extends this out to

Sandbox Management — create a dedicated sandbox for a data engineer to be able to work atomically
Develop — create/modify your data project
Build — validate your components are valid and complies to your standards
Test — verify your code changes are working against a cloned version of the production database
Release — transition code into production
Orchestrate — ensure your tasks and data flows are working
Monitor — monitor your data flows

Benefits of DataOps

Speed — DevOps practices let you move at the velocity you need to innovate faster, adapt to changing markets better, and become more efficient at driving business results.
Rapid Delivery — When you increase the pace of releases, you can improve your product faster and build competitive advantage.
Reliability — DataOps practices like continuous integration and continuous delivery can ensure the quality of application updates and infrastructure changes so you can reliably deliver at a more rapid pace while maintaining an optimum experience for end users ensuring you catch errors immediately
Real-time insights — By speeding up the entire data analytics process, you get closer to real-time insights in your data. In the fast-changing world, we live in, we need to have the ability to adapt to any market changes, as fast as we can.
Improved Collaboration — Under a DataOps model, data engineers and operations teams collaborate closely, share responsibilities, and combine their workflows. This reduces inefficiencies and saves time.

DataOps Framework

We have seen at the struggles at numerous organizations the struggles of building a framework around open source toolsets due to the length of time it takes to ensure the smoothness of the DataOps framework vs the need to just get the job done.

We have built a DataOps Framework which works using opensource toolsets such as dbt and schemachange for the Snowflake Data Platform and includes several out of the box pipelines from Sandbox management through to Continuous Integration and Continuous Delivery to enable teams to get up and running quickly where Data Governance is also built in.

If you aren't using any of these toolsets, don't worry, we can still help you on the journey using our experience and expertise built up from building our own framework.