DataOps In 3 Simple Steps That Supercharge Your Team's Delivery

Let me help you get started on implementing DataOps in 3 steps.

3 min read
engineering-management data-engineering

Let me help you get started on implementing DataOps in 3 steps.

I am assuming you know what DataOps is and why it is important to scale the impact of your data organization. If not, stop. Give this a read.

Today, the success of an organization can be directly linked to its ability to make effective, fast decisions based on data. - Tristan Handy

Let me tell you, from my experience working with various enterprises and startups scaling their data function, you have an exciting and rewarding journey ahead.

DataOps helps you deliver faster and higher quality data assets and insights to the right stakeholders.

The common reason why teams struggle to make actionable progress is an outdated mindset, due to what the data world looked like 10 years ago:

  • Legacy Data platforms were expensive. It was hard to create multiple environments for the data teams.

  • GUI modeling tools made analytics inaccessible to non-experts

  • DBA and Business Analysts were far removed from each other

Finally, give the reader a sentence of hope: you're going to explain to them how they can overcome all these problems you just laid out!

Here's how you can take your first steps to adopt DataOps:

Step 1: Enable collaboration instead of blindly adhering to policies and procedures when it comes to developing Analytics Code

Version Control all your analytics code (Python, SQL, Java, or anything else).

Refrain from using drag and drop tools that don't allow you to see the end result as code. Bring in programming concepts of Don't repeat yourself and Modularity to your SQL code for transformation. Treat the schema of your tables and views as an API for data scientists and analysts.

Step 2: Invest in tooling to automate data and analytics testing; Quality is Chief

All data ingestion, transformation, or analysis code should be tested.

Bad data 👉 Bad decisions

Today, data analytics pipelines are complex processes with multiple moving parts: code, data, ml models, and business assumptions. To avoid distrust in data, invest in tooling and upskilling your team to write business logic tests, input data tests, and output data tests. Improve testing coverage incrementally and automate monitoring in production environments.

Step 3: Enable isolated dev environments for Data (Eng, Analyst, Scientist); Unlock parallelism without conflict

Use on-demand cloud services to spin up-down sandbox environments for your data team members. Freedom to work without impacting users and fellow data team members will leapfrog productivity.

Prevent your data scientist from being impacted by data engineers working on the same project; new data or a schema change can disrupt their work. Use continuous deployment to promote code from the dev environment all the way to production. Make sure the code is configured and tested in all the environments automatically. Invest in tooling to ease the path to production and ensure scalability.

These steps enable your data analytics groups to better communicate and coordinate their activities; in turn converting isolated guilds to high functioning data teams.