As companies depend more on data for daily work, it is important to test data pipelines and models as carefully as software. This is the idea behind DataOps: using DevOps practices in data engineering to make analytics workflows more reliable and consistent.
Companies like Netflix and Uber have shown that using CI/CD and automated testing for data can create pipelines that are as dependable as software deployments.
Adding quality gates encourages teams to write more automated checks, catch problems sooner, and prevent bad data from spreading.
InfoQ notes that quality gates mean testing should happen automatically throughout the whole pipeline.
This change is important because data problems often spread quickly. Issues like broken schemas, outdated tables, or bad transformations can end up in dashboards, forecasts, and machine learning features before anyone realizes. DataOps helps by making teams check changes early, instead of waiting for something to break in production.
Treating data like code
DataOps is about using proven software engineering methods for data tasks. Teams put data models, ETL jobs, and transformation logic under version control, build them with automated workflows, and test them before releasing. In other words, they treat data like code.
In DataOps, teams use version control, automated build tools, and test frameworks to automate how they develop data models and ETL workflows, similar to how software is built. Automated testing, like unit tests for transformation logic and checks on datasets, is a key part of the CI/CD pipeline.
Every time a developer makes a change, it is tested and checked before going live. Tools like Jenkins and GitHub Actions help run these pipelines for every pull request. IBM calls this approach “the automation of ‘build, test and deploy’ processes, enabling data teams to quickly determine and address issues.”
In practice, this lets developers check that schema changes work as expected, make sure transformations still give the right results, and catch problems before they reach users. The main benefit is clear: when testing is built into development, reliability becomes part of the process, not an afterthought.
Building maturity one layer at a time
A good testing culture does not rely on just one tool or type of check. It is built step by step, in layers.
The first layer is schema and contract testing. These quick checks make sure the right columns are present, data types are correct, and constraints are still in place. For example, dbt has built-in tests for null values and uniqueness.
One testing expert says the “data-testing base” should start with “fast tests such as ‘Do ALL the expected columns exist?’, ‘Are Data Types Correct?’, and ‘Is Primary Key Unique?’” These simple checks are often the first defense against structural problems.
The second layer is content validation. After making sure the schema is correct, teams check that the data itself is reasonable. This means checking ranges, referential integrity, freshness, and other business rules.
For example, age fields should be within normal limits, foreign keys should match, and timestamps should be up to date. Unexpected nulls, outliers, or missing values should be caught early.
The third layer is regression and anomaly testing. This means comparing current results to past data to spot unexpected changes. Teams might look at row counts, sales totals, average revenue, or other key metrics over time.
In advanced setups, they also watch for changes in different user groups, like by location or subscription type, to make sure any shifts are intentional and understood.
Together, these layers move testing beyond basic validation and into a more mature quality framework.
The tools that make it possible
Today, data engineers have more tools than ever to automate these checks throughout the pipeline.
Great Expectations remains one of the most widely used open-source tools for declaring data expectations about tables and jobs. It works across Spark, SQL, and Python pipelines. dbt tests allow teams to run SQL-based assertions automatically after each model build.
dbt Labs emphasizes that “making tests and documentation part of the build phase is a hallmark of DevOps and DataOps” because building data models should include “tests to gauge and certify quality” alongside transformations.
For custom transformation logic, PyTest and other Python testing tools are just as important. They help check utility functions, Spark jobs, and custom data-processing code that is not part of SQL models.
Alongside these testing tools, there are workflow layers that help make quality checks routine:
- Data testing frameworks: Great Expectations, dbt tests, and custom PyTest suites
- CI/CD systems: Jenkins, GitHub Actions, or GitLab CI
- Monitoring and observability: Datadog, Prometheus, ELK, and built-in docs or data catalogs
- Version control: GitHub or GitLab to track every model, test, and transformation change
The aim is not to collect tools just to have them. The real goal is to build an environment where code and data quality are checked automatically and consistently.
Why testing must live inside deployment
Testing works best when it is built into the release process itself.
In a mature setup, CI jobs are triggered on every pull request. After merge, automated pipelines run in staging before moving to production. dbt users, for example, may configure CI to “run and test models in a staging environment before moving them into production.” If all tests and quality metrics pass, the change is deployed. If they fail, the deployment stops.
As dbt’s guide explains, CI/CD “automates code integration, testing, and delivery” and therefore “eliminates potentially costly errors before they’re shipped to data consumers.” InfoQ suggests incorporating linting and unit tests as early checks in the pipeline so that errors are caught as soon as possible.
This is where quality gates matter most. A pipeline can reject pull requests that fail null checks, block deployments with unexpected schema changes, or stop releases unless 100 percent of critical schema and unit tests pass. The principle is simple: if the data does not meet the standard, the change does not ship.
From a handful of checks to 1,290 tests
No team starts out with hundreds of automated tests. Most begin with a few key checks on their most important tables and metrics, like unique keys, required fields, row-count checks, and some basic business rules.
This early stage is important because it brings quick results. When teams see simple tests catch real problems before production, doubts start to disappear. Testing feels less like extra work and more like a safety net.
After that, teams add more coverage. Engineers include column-level checks, business rule validations, anomaly detection, and regression tests across more pipelines and data sources. Over time, nightly jobs or CI pipelines on each commit start catching issues right away.
This is how strong testing cultures develop: start with a basic test suite, then expand coverage, and finally automate at scale. Some organizations end up running hundreds or thousands of checks every day.
For example, one team grew its suite to over a thousand checks covering schema, transformation logic, and model output, catching issues that might have been missed. As Shehroz Abdullah says, “adding dbt tests makes your data pipeline more robust and reliable” because quality is built into the process.
In one advanced setup, test coverage grew to over 1,290 automated checks across data sources, transformation pipelines, and ML features, leading to a big drop in data errors. This did not happen all at once, but by adding tests where they were needed most.
The real challenge is culture
The toughest part of building a testing culture is usually not technical—it is about changing behavior.
A strong data testing culture happens when teams see data quality as a shared responsibility, not just a separate QA task. Engineering, analytics, and business teams all help define what trusted data means.
That is why technology by itself is not enough. Teams need shared ownership, clear accountability, training, and support from leaders. Apptad says that “cultural transformation is equally important” in DataOps. Atlan points to “ongoing training” and “data ownership” as key parts of a strong data quality culture.
You can see this culture in small but important ways: senior leaders treat reliability as a priority, tests are expected in pull requests, documentation is kept up to date, and failures are seen as chances to learn, not to blame.
Getting buy-in often starts with visible results. Add a few tests to protect key workflows and show what problems they catch. Make tests required in pull requests, and soon this becomes the norm. InfoQ’s rule is clear: “code [or data] that does not meet standards simply will not merge.” This is not just a technical rule—it is a cultural one.
The obstacles are real, but manageable
Of course, the process is not always smooth. Teams often face unreliable tests and slow pipelines. These problems can usually be solved by focusing on stable, fast checks, using simple SQL-based tests when possible, and using sampled or masked production data for speed and safety.
Schema migration is another challenge, but it can be managed with careful rollout processes and tools like Airflow, Alembic, or dbt migrations. Ideally, these changes are tested in database branches before going live.
The bigger risk is usually not technical complexity, but losing momentum. Without support from leaders, dedicated time, and visible results, testing can slip back into being “something we should do later.”
From firefighting to prevention
The payoff of a strong data testing culture is not just cleaner pipelines. It is a fundamentally different operating model.
Data engineering shifts from reacting to problems to preventing them. With automated CI/CD pipelines, organizations can spot issues before they affect business decisions. Tools like Great Expectations, dbt tests, and PyTest check datasets and models every time changes are made. But people are just as important. Organizations need to encourage shared responsibility, offer training, and make data quality a visible priority.
The reward is significant: faster delivery of high-quality, reliable analytics and AI that people can trust. As one data leader said, “in today’s economy, it is no longer sufficient to deliver data; we must deliver trusted data.”
Teams that track their tests, expand quality checks over time, and treat testing as part of their regular work build trust step by step.
And sometimes, one check at a time becomes 1,290 tests and counting.
