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Start with the failures you need to prevent or detect, then choose a tool that can test those expectations where they matter in your pipeline. Define checks for correctness and freshness, confirm support for your actual database or processing engine, and compare how teams will author rules, see failures, and maintain the system. A small trial using representative data and rules is more useful than a feature checklist alone.
Define what “good data” means for your use case
Data quality is fitness for a particular use, not a universal checklist. A table feeding a financial report may need strict completeness and reconciliation checks; a rapidly updated event stream may prioritize freshness and volume. Begin with concrete failure modes and express each as an assertion that can pass or fail.
- Missing or duplicate records: check required fields for nulls and keys for uniqueness.
- Invalid values: constrain categories to allowed values and numeric or date fields to valid ranges.
- Broken relationships: test that foreign keys or other required references point to existing records.
- Unexpected volume: check row counts or other volume measures against defined expectations.
- Late or stale data: test freshness against the cadence the consuming workflow requires.
- Business-specific failures: encode domain rules, such as totals reconciling or event dates following a business sequence.
Do not assume a vendor’s named quality dimensions or terminology are universal. A 2024 survey by Papastergios and Gounaris reports that ISO/IEC 25012 defines 15 data-quality dimensions; the survey associated six of those dimensions with functionalities recorded in the six tools it examined. That is a bounded study result, not evidence that tools support only six dimensions.
Place checks at the pipeline stage where they help
A useful testing plan covers both data correctness and freshness, but not every check needs to run everywhere. Put an assertion close to the point where a failure can be caught and acted on.
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- Raw ingestion: check that incoming records are present, parseable, and within basic schema or value expectations.
- Transformation: validate transformed outputs, relationships, and business rules.
- Pull requests and CI/CD: run suitable checks before a change is deployed, so a broken model or rule can be caught during review.
- Scheduled jobs and production: check that recurring data remains correct and fresh after deployment; add monitoring when you need to detect changes or anomalies over time.
Map each rule to an owner and a response. A failing assertion is most useful when the team knows whether to stop a deployment, repair an upstream source, investigate a transformation, or notify a data consumer.
Choose the approach that fits your workflow
These approaches overlap, but they are not interchangeable. A framework that evaluates known expectations is different from a system that monitors production behavior for unexpected change.
Rank #2
| Approach | Useful when | What to verify |
|---|---|---|
| SQL assertions in an analytics workflow | Checks belong alongside SQL transformations and the team already uses dbt. | Confirm that the database adapter and execution workflow support the required checks and deployment stages. |
| General-purpose expectation and validation framework | You want reusable expectation suites and explicit validation workflows. | Verify connectors, deployment, alerting, and reporting in current documentation for your architecture. |
| Testing plus production observability and contracts | You need both checks against known requirements and monitoring for deviations from production history; contracts can formalize producer-consumer expectations. | Establish whether you need both testing and monitoring, and how contracts represent schema, types, ranges, and constraints. |
| AWS-native checks or Spark-based constraints | Your workflows are centered on AWS Glue or Spark and the team can operate the chosen service or code. | Confirm current service status, engine support, setup, and pricing for your deployment. |
SQL tests with dbt
dbt’s Developer Hub describes data tests as SQL select queries that seek records disproving an assertion—for example, duplicate rows for a uniqueness rule or rows with null values for a not-null rule. Its documentation describes four built-in generic data tests and also supports singular SQL tests for a single-purpose assertion. Generic tests can be reused; singular tests suit a rule written for one particular case. As the dbt Developer Hub explains, “If the data test returns zero failing rows, it passes, and your assertion has been validated.”
This is a natural option when tests should live with SQL transformations. The documentation cited here does not establish compatibility with every engine or feature, so check the exact adapter and workflow you use.
Rank #3
General-purpose expectations
Great Expectations’ overview describes defining and validating data-quality checks across quality and observability dimensions. Consider it when reusable expectation suites and explicit validation workflows fit your architecture. The overview does not by itself settle connector, deployment, alerting, or reporting details; confirm those in the current documentation for your intended setup.
Testing, observability, and contracts
Soda’s documentation distinguishes proactive data testing from production observability. Testing checks known expectations during development, deployment, transformation, and CI/CD; observability monitors production behavior and deviations from historical norms. Data contracts can formalize agreements about schema, types, ranges, and constraints between producers and consumers. These are complementary functions: Soda describes the combination this way: “Together, they enable end-to-end data quality management: testing prevents problems, and observability detects those that escape prevention.”
Decide whether your team needs production monitoring in addition to deterministic assertions. Monitoring can address behavior that fixed rules do not anticipate, but it is not automatically necessary for a workflow with a small set of well-defined checks.
AWS Glue and Deequ
AWS Prescriptive Guidance maps different needs to Glue DataBrew for no-code column or table conditions, Glue Data Quality checks in Glue jobs, custom ETL code for bespoke checks, and Deequ for metric reporting, constraint validation, and constraint suggestions. AWS describes Deequ as implemented on Apache Spark; its tutorial lists familiarity with Spark and Scala among the prerequisites. Deequ is therefore a candidate to assess for Spark-oriented teams, while Glue services merit evaluation in AWS-centered workflows. Check current service availability, supported engines, setup, and pricing before choosing.
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Compare candidates against the work your team must do
Once you have a shortlist, evaluate it against real rules and workflows—not just a list of advertised features.
- Platform fit: verify databases, warehouses, Spark environments, lake storage, file formats, versions, and deployment environments actually in use.
- Rule coverage: check support for nulls, uniqueness, values and ranges, relationships, schema changes, freshness, volume, distribution changes, and business-specific SQL or code.
- Authoring and reuse: determine whether rules are written in SQL, YAML or other configuration, Python, or Scala; check how reusable tests, contracts, and reviews work for the people who will own them.
- Pipeline integration: confirm where checks run—ingestion, transformations, pull requests, CI/CD, scheduled jobs, and production—and how they fit existing deployment practices.
- Failure visibility and remediation: inspect whether the tool provides failing records, saved failures, reports, alerts, lineage or impact context, and a practical way to trace an issue upstream.
- Governance and collaboration: assess ownership, permissions, auditability, and how data producers and consumers agree on expectations.
- Scale and operating cost: account for query workloads, repeated scans, clusters or managed services, runtime, and ongoing maintenance. Measure these with your data instead of inferring performance from marketing claims.
- Total operating effort: include deployment, upgrades, rule maintenance, integrations, alert tuning, and incident response—not only the initial setup.
Run a representative evaluation before selecting
- Choose a small set of meaningful rules. Include a correctness check such as uniqueness or referential integrity, a freshness or volume check if relevant, and a business-specific invariant.
- Use representative data and pipeline stages. Try the rules where they would actually run, such as a transformation workflow, pull request, scheduled job, or production monitor.
- Inspect the failure path. Introduce or identify a known violation and see what evidence the tool returns, who receives it, and how readily the team can find the cause.
- Assess runtime and maintenance. Observe query or cluster demands and estimate the work needed to update rules, integrations, and alerts as the system changes.
- Check operational details with current vendor documentation. Confirm supported engines and versions, deployment options, data handling, service availability, pricing, and contract terms for the exact edition and environment under consideration.
No market-wide adoption, performance, data-loss reduction, or return-on-investment figure is established by the sources cited here. Treat a tool choice as an engineering fit decision: compare how candidates behave on your rules, platform, and operating workflow.
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