databricks-labs-dqx
Data Quality eXtended (DQX) is a Python library for data quality checks and data quality monitoring
Decision gist · record as of 2026-08-14
Yes, with conditions. DQX is actively maintained, has low install friction, and offers a comprehensive rule-based quality framework tailored to Databricks workloads. However, the license treatment is unclear—verify the actual license before production use. Also confirm that advanced features fit your Databricks setup and cost model. Not formally supported by Databricks with SLAs; issues are reviewed as time permits.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; databricks-sdk must be configured with valid Databricks workspace credentials.
- Low friction: pure Python wheel with five runtime dependencies (databricks-sdk, pydantic, pyyaml, sqlalchemy, databricks-labs-blueprint).
- Active maintenance with a release 1 day old and recent commits.
License · maintenance · safety
(unclear) — License treatment is unclear—no SPDX identifier or raw license text is available. Verify the actual license terms before adopting in production, especially for commercial use.
last release 2026-08-13 (1 days) · last repo commit 2026-08-14 · 447 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 7,497,459 downloads/mo, #1,735 on PyPI
Alternatives
Verify before relying
pip install databricks-labs-dqx
from databricks_labs_dqx import DQX
dqx = DQX()
results = dqx.run_checks(df, checks=[...])- Exact scope and maturity level of the built-in checks across different categories (null, range, regex, referential, aggregate, geo, PII).
- Performance characteristics and scalability limits for very large datasets or high-frequency streaming workloads.
- Whether LLM-driven rule generation and anomaly detection require additional Databricks services or incur extra costs.
- Compatibility and integration details with Databricks Unity Catalog, Volumes, and Lakebase (PostgreSQL) backends.
What it is and what it does
DQX is a data quality framework for PySpark workloads on Databricks that lets you define, run, and monitor quality checks on both batch and streaming DataFrames. It supports built-in checks (null, range, regex, referential, aggregate, geo, PII) that you can apply row-level or column/dataset-level, plus custom check functions. You can define checks as code or declaratively in YAML/JSON, mark failures as warnings or errors, and route invalid data to quarantine, drop, or mark operations. The library includes data profiling, automatic rule generation from existing data, ML-based row anomaly detection, and integration with Databricks data contracts for schema validation.
Results are persisted to Delta tables with built-in aggregate metrics and a Lakeview dashboard for tracking quality over time. You can trigger Slack, Teams, or webhook alerts when metrics cross thresholds, and optionally fail pipelines on quality violations. DQX Studio provides a browser-based no-code UI for authoring and monitoring rules as a Databricks App. The framework applies the same API to batch DataFrames and Spark Structured Streaming (including DLT pipelines).
Use it for
- Validate incoming data against business rules before loading into a data lake.
- Monitor data quality metrics on streaming pipelines and alert teams when error rates exceed thresholds.
- Generate quality rules automatically from existing datasets and refine them with LLM suggestions.
- Enforce schema and referential integrity checks as part of a Databricks data contract.
- Detect anomalous rows in large datasets and get explanations for investigation.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, with conditions.
DQX is actively maintained, has low install friction, and offers a comprehensive rule-based quality framework tailored to Databricks workloads. However, the license treatment is unclear—verify the actual license before production use. Also confirm that advanced features fit your Databricks setup and cost model. Not formally supported by Databricks with SLAs; issues are reviewed as time permits.
Install
databricks-labs-dqx on PyPI
Before you install
Low friction: pure Python wheel with five runtime dependencies (databricks-sdk, pydantic, pyyaml, sqlalchemy, databricks-labs-blueprint). Active maintenance with a release 1 day old and recent commits. Requires Python 3.10+.
Requires Python 3.10 or later; databricks-sdk must be configured with valid Databricks workspace credentials.
License in practice
License treatment is unclear—no SPDX identifier or raw license text is available. Verify the actual license terms before adopting in production, especially for commercial use.
Quickstart
pip install databricks-labs-dqx
from databricks_labs_dqx import DQX
dqx = DQX()
results = dqx.run_checks(df, checks=[...])Verify before relying
- Exact scope and maturity level of the built-in checks across different categories (null, range, regex, referential, aggregate, geo, PII).
- Performance characteristics and scalability limits for very large datasets or high-frequency streaming workloads.
- Whether LLM-driven rule generation and anomaly detection require additional Databricks services or incur extra costs.
- Compatibility and integration details with Databricks Unity Catalog, Volumes, and Lakebase (PostgreSQL) backends.
Package facts
| License | Not declared unclear |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesdatabricks-labs-blueprintdatabricks-sdkpydanticpyyamlsqlalchemy |
| Maintenance | Actively maintained 1 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 7,497,459 / month, #1,735 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaEnvironment :: ConsoleFramework :: PytestIntended Audience :: DevelopersIntended Audience :: System AdministratorsLicense :: Other/Proprietary LicenseOperating System :: MacOSOperating System :: Microsoft :: WindowsProgramming Language :: PythonProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: Implementation :: CPythonTopic :: Software Development :: LibrariesTopic :: Utilities |
Evidence: databricks_labs_dqx-0.16.0-py3-none-any.whl
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See also dbldatagen · spark-expectations · cuallee · pydeequ · dbt-databricks · databricks-labs-remorph · databricks-labs-lsql · databricks-test · pyddq · panzi-json-logic