Data Testing Tools: 10 Best Data Testing & Automation Tools Compared

"Data testing tools" covers a wide range: a SQL query you run by hand, an open source library like Great Expectations, a YAML check file in Soda, or an enterprise platform like QuerySurge or RightData. They all answer the same question: is the data in the target complete, correct and fresh?

This guide compares 10 data testing tools and data testing automation tools, shows what a test looks like in each, and tells you which one to learn first if you are aiming for an ETL tester or data QA job.

Quick list: SQL, Python (pytest + pandas), Great Expectations, Soda, dbt tests, Deequ, QuerySurge, iCEDQ, Datagaps ETL Validator and RightData. A comparison table follows the reviews.

What Are Data Testing Tools?

Data testing tools check data, not application code. They run rules such as "no NULL customer IDs", "row counts match between source and target", "every order has a valid product" and "yesterday's load arrived by 6 AM", then report anything that fails. In ETL work they sit after each load; in analytics teams they run inside the pipeline or on a schedule.

They fall into three groups:

• Code-first libraries (free): SQL scripts, pytest, Great Expectations, Soda Core, dbt tests, Deequ.
• ETL testing platforms (commercial): QuerySurge, iCEDQ, Datagaps, RightData DataTrust — built for source-to-target comparison at scale.
• Data observability tools: RightData RightSight, Soda Cloud, Monte Carlo — continuous monitoring of freshness, volume, schema and anomalies.

Pipeline-focused testing (ETL) is a subset of this. For the ETL-only list, see ETL testing tools.

What Should a Data Test Check?

Before picking a tool, know what you are testing for. Every tool on this list is a way to automate checks on the same six data quality dimensions:

If a tool cannot express one of these, you will end up writing SQL for it anyway. More examples: data quality checks and ETL test cases.

1. SQL

Price: Free · Best for: everyone

Every other tool on this list either generates SQL or needs it when a test fails. A tester who can write a minus query, a duplicate check and an aggregate reconciliation can test any database without buying anything.

-- Duplicate check on the business key
SELECT order_id, COUNT(*)
FROM dw.fact_orders
GROUP BY order_id
HAVING COUNT(*) > 1;

More: 15 SQL queries for ETL testing.

2. Python (pytest + pandas)

Price: Free · Best for: testers who want their own automation framework

Wrap SQL checks in pytest functions and you have a data testing automation framework that runs in Jenkins, GitHub Actions or Azure DevOps. pandas handles file comparisons (CSV, Excel, Parquet) that SQL cannot reach.

def test_row_count_matches(src, tgt):
    s = src.execute("SELECT COUNT(*) FROM orders").scalar()
    t = tgt.execute("SELECT COUNT(*) FROM fact_orders").scalar()
    assert s == t, f"source={s} target={t}"

Step-by-step: ETL testing using Python.

3. Great Expectations (GX)

Price: Free open source (GX Core) + paid GX Cloud · Best for: Python data teams

You declare "expectations" such as expect_column_values_to_not_be_null and GX validates a batch of data against them, producing readable data docs. Strong library of built-in checks; the setup has a learning curve. Tutorial: Great Expectations tutorial.

4. Soda (Soda Core + Soda Cloud)

Price: Soda Core is free; Soda Cloud has a free tier and paid plans · Best for: teams who want checks in plain YAML

Checks are written in SodaCL, a YAML-based language that non-developers can read. Soda Core runs them from the command line or inside Airflow/dbt; Soda Cloud adds dashboards, alerts, anomaly detection and data contracts.

# checks.yml (SodaCL)
checks for fact_orders:
  - row_count > 0
  - missing_count(customer_id) = 0
  - duplicate_count(order_id) = 0
  - freshness(load_ts) < 1d

Comparing Soda with an enterprise platform? See RightData vs Soda.

5. dbt Tests

Price: Free (dbt Core) · Best for: ELT teams already on dbt

Four generic tests (unique, not_null, accepted_values, relationships) cover most column checks, and any SQL that returns failing rows becomes a custom test. Tests run with dbt test after every build. Tutorial: dbt testing tutorial.

6. Deequ / PyDeequ

Price: Free (open source from AWS Labs) · Best for: very large datasets on Apache Spark

Deequ defines "unit tests for data" that run as Spark jobs, so it scales to billions of rows. It also suggests constraints by profiling your data. Needs Spark knowledge; PyDeequ is the Python wrapper. Related: big data testing.

7. QuerySurge

Price: Commercial · Best for: large source-to-target comparisons

Purpose-built for ETL and data warehouse testing: you write source and target queries, QuerySurge compares the results row by row, schedules runs and reports differences. Walkthrough: QuerySurge ETL testing.

8. iCEDQ

Price: Commercial · Best for: rule-based testing with audit trails

Rule-based data testing and monitoring with a strong audit history, popular in banking and insurance where test evidence must be kept.

9. Datagaps ETL Validator

Price: Commercial (trial available) · Best for: low-code ETL and BI report testing

Visual test builder for data comparison, metadata and transformation checks, with CI/CD hooks and BI report testing in the same tool.

10. RightData (DataTrust & RightSight)

Price: Commercial (enterprise) · Best for: enterprises that want testing and observability in one vendor

RightData's DataTrust covers no-code data reconciliation and validation (classic ETL testing), while RightSight focuses on continuous data health: freshness, volume, drift and anomaly monitoring with alerts. Head-to-head comparison: RightData RightSight vs Soda.

Data Testing Tools Comparison

Data Testing Automation Tools: How Automation Works

A data testing automation tool is any of the above wired to run without a human: on every pipeline run, on a schedule, or on every pull request. A typical setup:

1. Define checks as code (SQL files, pytest, SodaCL YAML, dbt tests or GX expectations) and keep them in Git.
2. Trigger them from the orchestrator (Airflow, Azure Data Factory, AWS Glue workflows) right after the load step.
3. Fail the pipeline on critical checks (duplicates, missing rows) and only warn on soft ones (small volume drift).
4. Run in CI/CD so a transformation change cannot merge until tests pass.
5. Alert and report through Slack/Teams/email and keep the history for audit.

Full framework with code: ETL automation testing · tool shortlist: 10 ETL automation testing tools.

Example: a small automated data test suite

This is the kind of suite a tester builds in the first week on a project. Each check is a SQL query that must return zero rows; pytest runs them all and the CI job fails if any returns data.

import pytest

CHECKS = {
    "no_duplicate_orders":
        "SELECT order_id FROM fact_orders GROUP BY order_id HAVING COUNT(*) > 1",
    "no_null_customer":
        "SELECT order_id FROM fact_orders WHERE customer_id IS NULL",
    "no_orphan_customer":
        "SELECT f.order_id FROM fact_orders f LEFT JOIN dim_customer c "
        "ON f.customer_id = c.customer_id WHERE c.customer_id IS NULL",
    "loaded_today":
        "SELECT 1 WHERE (SELECT MAX(load_ts) FROM fact_orders) < CURRENT_DATE",
}

@pytest.mark.parametrize("name", CHECKS)
def test_data(dw, name):
    bad = dw.execute(CHECKS[name]).fetchall()
    assert not bad, f"{name}: {len(bad)} failing rows, e.g. {bad[:5]}"

The same four checks could be written as SodaCL lines, dbt tests or GX expectations. The tool changes; the checks do not.

Free vs Paid Data Testing Tools

Free tools (SQL, pytest, Great Expectations, Soda Core, dbt, Deequ) cost nothing to licence but need someone to build the framework, the reporting and the scheduling. Paid tools (QuerySurge, iCEDQ, Datagaps, RightData) sell exactly that: a UI, scheduling, reports, audit history and vendor support.

• Paid tools make sense for large migrations with thousands of tables, regulated industries that need audit evidence, and teams with few people who write code.
• Free tools make sense for engineering-led teams, cloud warehouses with dbt or Airflow, and anyone learning.
• Most real projects mix both: a commercial reconciliation tool for the migration, plus SQL or dbt tests that stay in the pipeline after go-live.

Common Mistakes When Choosing a Data Testing Tool

• Buying the tool before defining the checks. Write the test cases first; then see which tool expresses them with the least effort.
• Testing only counts. Row counts can match while every amount is wrong. Add checksum and transformation checks (data reconciliation).
• Alerting on everything. If every small drift pages someone, people mute the alerts. Separate blocking checks from warnings.
• Leaving tests outside version control. Checks that live only in a UI are hard to review and easy to lose.
• Skipping SQL because "the tool does it". When a test fails, you still investigate with SQL.

Choosing a Data Testing Solution: Build, Buy or Mix

A data testing solution is more than a tool: it is the checks, where they run, who owns failures and how results are reported. Teams usually end up in one of three setups:

• Build: SQL checks in pytest or dbt, triggered by Airflow or CI, results to Slack. Lowest cost, needs people who code.
• Buy: a platform such as QuerySurge, iCEDQ, Datagaps or RightData with UI, scheduling and audit history. Faster for large estates and non-coding teams, with licence cost.
• Mix: a commercial tool for migration-time reconciliation plus code-first checks that stay in the pipeline afterwards. This is the most common setup on large projects.

Whichever you choose, the solution only works if every check has an owner and a clear "block or warn" rule. For a head-to-head of a buy and a build option, see RightData vs Soda.

Which Data Testing Tool Should You Learn First?

• Freshers and manual testers: SQL first, then pytest. These two appear in most ETL tester job descriptions and cost nothing.
• Working in a dbt shop: dbt tests, then Soda or Great Expectations for anything dbt cannot express.
• Enterprise QA teams: learn the commercial tool your client uses on the job; recruiters still test SQL in the interview.

Tools are learned in days; the testing method (what to check, how to reconcile, how to report a defect) takes longer and is what interviews actually test. That method is what ETL Testing: From Beginners to Advanced teaches, independent of any vendor.

Related: ETL testing tools · data validation testing tools · data quality checks · ETL testing types · Start Here

Frequently Asked Questions

What are data testing tools?

Data testing tools check that data is complete, accurate, consistent and fresh. They run rules such as row-count reconciliation, NULL and duplicate checks and referential integrity, and report failures. Examples are SQL, Great Expectations, Soda, dbt tests, Deequ, QuerySurge, iCEDQ, Datagaps and RightData.

What are the best free data testing tools?

SQL, pytest with pandas, Great Expectations (GX Core), Soda Core, dbt Core tests and Deequ are all free and open source. SQL plus pytest is the most flexible starting point for testers.

What is a data testing automation tool?

It is a data testing tool that runs checks automatically, triggered by the pipeline, a schedule or a CI/CD pull request, and fails the run or sends an alert when a check fails. Great Expectations, Soda, dbt tests, QuerySurge and Datagaps can all be automated this way.

Which data testing tool should a fresher learn?

Learn SQL first, then Python with pytest. Most ETL tester job descriptions ask for SQL, and every data testing tool depends on it. Pick up Great Expectations, Soda or dbt tests once the basics are solid.

Is data testing the same as ETL testing?

ETL testing is a part of data testing. ETL testing focuses on source-to-target validation of a pipeline; data testing also covers data quality monitoring, observability and checks on data that was never moved by an ETL job.

What is a data testing solution?

A data testing solution is the combination of data quality checks, the tools that run them, the schedule or pipeline trigger, alerting and ownership of failures. It can be built with free tools such as SQL, pytest and dbt tests, bought as a platform such as QuerySurge, iCEDQ, Datagaps or RightData, or a mix of both.

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Written by

Asim Noaman Lodhi

Certified Google Partner · QA Consultant · 12+ Years IT

QA consultant specializing in ETL testing and data quality. Trained 913+ students to transition into data testing roles through hands-on, real-world instruction.

4.5 Rating 913+ Students Google Partner 79 Lectures