What is DataOps?
Introduction to DataOps
In traditional IT environments, releasing a new data dashboard was a slow process. The business team requested a metric. The data engineering team spent weeks writing brittle Python scripts to extract the data. The DBA team spent weeks creating the database tables. By the time the dashboard was finally pushed to production, the data was often wrong, the business logic had changed, and the multi-month cycle had to start over.
Software engineering addressed this problem years earlier by adopting DevOps, a cultural and technical movement that combined development and operations through automation (CI/CD) and Agile methodologies, allowing teams to deploy software safely many times a day.
DataOps (Data Operations) is the application of these DevOps principles to the entire data lifecycle. It is a collaborative data management practice focused on improving the communication, integration, and automation of data flows between data managers and data consumers across an organization.
The Core Principles of DataOps
DataOps is not a specific software tool you can buy; it is a methodology built on several principles.
1. Agile Development for Data
Instead of gathering requirements for 6 months to build a monolithic “Enterprise Data Warehouse,” DataOps teams work in 2-week sprints. They deliver small, functional, incremental pieces of data (e.g., releasing a single, accurate “Customer Retention” table) and gather feedback from the business users right away. This keeps the data team focused on what the business needs.
2. Version Control Everything (Data as Code)
In a DataOps environment, as little as possible is done manually through a graphical interface.
- Data transformation logic is written in SQL (via dbt) and stored in Git.
- The orchestration pipeline is written in Python (via Apache Airflow) and stored in Git.
- The underlying infrastructure (like Snowflake warehouses or Dremio clusters) is defined using Terraform (Infrastructure as Code) and stored in Git. This means the data platform can be torn down and recreated from code in a repeatable way.
3. Automated Testing and CI/CD
In legacy data teams, testing meant a human staring at a spreadsheet looking for errors. In DataOps, testing is automated. When a data engineer modifies a SQL script and submits a Pull Request, a CI/CD pipeline (like GitHub Actions) spins up a temporary database clone (often using Zero-Copy Cloning). It runs the new SQL script, executes automated data quality assertions (using tools like Great Expectations), and checks that the change will not break downstream Tableau dashboards. If the tests pass, the code is automatically merged and deployed to production.
4. Continuous Observability
Data pipelines will break at times because third-party APIs change or upstream software developers alter database schemas. DataOps accepts this by implementing Data Observability. When an anomaly occurs (e.g., a sudden spike in null values), the DataOps platform alerts the engineering team via Slack, often before the business users notice the dashboard is broken.
The Cultural Shift
The hardest part of implementing DataOps is not setting up GitHub Actions; it is the cultural transformation.
Historically, data engineers, data scientists, and business analysts worked in siloed environments, throwing tickets over the wall to each other. DataOps requires cross-functional, collaborative teams. It requires data engineers to stop acting like “ticket takers” and start acting like software engineers building reliable data products with clear Service Level Agreements (SLAs).
Conclusion
DataOps is the maturity model for the modern data team. By borrowing the automation, testing, and agile collaboration practices of software engineering, DataOps shortens the cycle time of data analytics. It turns data engineering from a fragile, slow-moving bottleneck into a reliable, fast-moving function that continuously delivers trusted insights to the enterprise.
Learn More
To learn more about the Data Lakehouse, read The Lakehouse Built for Everyone by Alex Merced. You can find this and other books by Alex Merced at books.alexmerced.com.