Data Validation Testing Tools: 9 Options + the Free SQL Method

Data validation testing tools check that data is complete, accurate and consistent as it moves from source systems into a data warehouse, lake or report. This guide compares 9 tools, free and paid, and then shows the free SQL method that every tool is built on, so you can start validating data today without buying anything.

What Data Validation Tools Check

• Completeness: row counts match, no missing records.
• Accuracy: values and totals match the source after transformation.
• Uniqueness: no duplicate keys.
• Validity: values follow rules (formats, ranges, allowed lists).
• Consistency: foreign keys match their parent tables.
• Timeliness: data arrived when expected.

These are the same dimensions described in ETL validation techniques and data quality checks.

The 9 Best Data Validation Testing Tools

1. SQL (free)

Every database already has the most important validation tool: SQL. Counts, minus queries, joins and aggregates answer most validation questions. Every other tool on this list either generates SQL or asks you to write it.

2. Great Expectations (free, Python)

Define expectations on columns and tables, run them on pandas, Spark or SQL databases and get HTML “data docs”. See our Great Expectations tutorial.

3. Soda Core (free)

Readable checks such as missing_count(email) = 0 in a YAML file, scheduled against your warehouse.

4. dbt tests (free)

Built-in unique, not_null, accepted_values and relationships tests plus custom SQL tests that run with each dbt build. Guide: dbt testing tutorial.

5. Deequ / PyDeequ (free)

An open-source library from AWS Labs for validating very large datasets on Apache Spark. Good for big data and lake projects (data lake testing).

6. Python with pandas + pytest (free)

Load source and target into DataFrames, compare them and assert results in pytest. Flexible and great for interviews. See ETL testing using Python.

7. QuerySurge (paid)

Source-to-target data comparison across databases with scheduling and CI integration. Tutorial: QuerySurge ETL testing.

8. iCEDQ (paid)

Rules-based reconciliation and validation with production monitoring and audit trails.

9. Datagaps DataOps Suite / ETL Validator (paid)

Visual data compare, profiling and validation rules for teams that prefer low-code tools.

Data Validation Tools Comparison

The Free SQL Method: 5 Validation Queries

These five queries cover most data validation testing. Each should return the expected result (or zero rows) when the data is correct.

1. Row count reconciliation

SELECT (SELECT COUNT(*) FROM src.orders)  AS source_count,
       (SELECT COUNT(*) FROM dwh.fact_orders) AS target_count;

2. Missing records (source minus target)

SELECT order_id FROM src.orders
EXCEPT
SELECT order_id FROM dwh.fact_orders;

3. Duplicate keys

SELECT order_id, COUNT(*) FROM dwh.fact_orders
GROUP BY order_id HAVING COUNT(*) > 1;

4. Null or invalid values

SELECT * FROM dwh.fact_orders
WHERE customer_key IS NULL OR order_amount < 0;

5. Aggregate (sum) match

SELECT SUM(amount) FROM src.orders;
SELECT SUM(order_amount) FROM dwh.fact_orders;

Use MINUS instead of EXCEPT on Oracle. More queries: SQL for ETL testing and data reconciliation in ETL testing.

How to Choose a Data Validation Tool

• Small team, any database: SQL scripts + Python.
• dbt already in use: dbt tests first, add Great Expectations or Soda for deeper checks.
• Spark / data lake: Deequ or Great Expectations on Spark.
• Enterprise with audit needs and budget: QuerySurge, iCEDQ or Datagaps.

For automation-focused tools, see ETL automation testing tools compared. To learn the SQL validation skills behind every tool, try ETL Testing: From Beginners to Advanced.

Related: ETL validation · data profiling · data integrity testing · data transformation testing · ETL testing tools

Frequently Asked Questions

What are data validation testing tools?

Tools that check data is complete, accurate, unique and valid after it moves between systems. Examples include SQL, Great Expectations, Soda Core, dbt tests, Deequ, QuerySurge, iCEDQ and Datagaps.

Which data validation tool is free?

SQL, Great Expectations, Soda Core, dbt tests, Deequ and Python with pandas and pytest are all free.

Can I do data validation with SQL only?

Yes. Row counts, minus/except queries, duplicate checks, null checks and sum comparisons in SQL cover most data validation testing.

What is the difference between data validation and data verification?

Validation checks that data follows the rules and matches the business expectation; verification checks that data was copied or loaded exactly as the source. In ETL testing you do 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.

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