"We Have DevOps, So Why Not DataOps?"

sezin öztekin — Sun, 10 May 2026 13:34:33 +0000

Lately, we’ve all noticed the explosion of DevOps job postings on platforms like LinkedIn. It’s become a savior for most companies, yet for some, there’s still a why do we even need this, I can handle it myself mentality. In Turkey's corporate landscape, this often stems from what we might call white-collar pride or, to put it bluntly, a bit of an elitist perspective. We’ve all encountered that I created the world ego in big corporate brands. I wonder if this same ego exists among DevOps engineers? As a data engineer, I can’t help but ask: why shouldn't I have a DataOps of my own? Maybe I want to feel like I created the world too.

The reality of DataOps is that software developers enjoy every kind of AI support and a million DevOps tools that make their lives easier. Their feedback loops are much simpler because they usually have an undo button. If they are experienced, taking a backup or rolling back a deployment is significantly easier than what we face in the data world. In DevOps, you can scrap a broken container and spin up a new one in seconds. But in data, you can’t just scrap and recreate a five-year-old corrupted table. Data is a living organism; the comfortable break-and-fix world of DevOps doesn't apply to us. I feel like people in the data field are often pushed to the background or even looked down upon. Everyone notices when a developer builds a system, but they need to realize that without the right nuances, the data, that system is useless.

The data landscape is far more complex than it appears. Moving, organizing, and decluttering millions of rows is a massive undertaking. You can spin up a microservice in seconds, but moving or reformatting a 50-terabyte table is like fighting the laws of physics. In software, a bug crashes the app. In the data world, the system doesn't crash; it just flows incorrectly. The revenue on a dashboard looks wrong, but no one notices until it’s too late. While DevOps engineers monitor system pulses in milliseconds, we often find out about a data error via a phone call from the CEO. We used to have just a single SQL procedure. Now, we have Kafka, Airflow, Spark, dbt, and Snowflake, all interconnected. When one link breaks, the whole company goes blind. Once an executive sees a wrong number on a dashboard, it takes six months to earn that trust back. DataOps is the defense line built to protect that trust. The list goes on, but one thing is clear: DataOps is not just about increasing the headcount in a data team; it’s about giving data the engineering respect it deserves.

DEV Community: sezin öztekin

"We Have DevOps, So Why Not DataOps?"