| Name | sparkdq JSON |
| Version |
0.11.0
JSON |
| download |
| home_page | None |
| Summary | A declarative PySpark framework for row- and aggregate-level data quality validation. |
| upload_time | 2025-08-09 16:03:40 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. 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For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. "Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution." "Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. 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While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets "[]" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright 2025 Marcel Kennert Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. |
| keywords |
apache-spark
data-pipeline
data-quality
data-validation
etl
lightweight
pyspark
spark
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[](https://github.com/sparkdq-community/sparkdq/actions/workflows/ci.yaml)
[](https://codecov.io/gh/sparkdq-community/sparkdq)
[](https://sparkdq-community.github.io/sparkdq/)
[](https://pypi.org/project/sparkdq/)
[](https://github.com/sparkdq-community/sparkdq)
[](LICENSE)
# SparkDQ — Data Quality Validation for Apache Spark
Most data quality frameworks weren’t designed with PySpark in mind. They aren’t Spark-native and often lack proper support for declarative pipelines. Instead of integrating seamlessly, they require you to build custom wrappers around them just to fit into production workflows. This adds complexity and makes your pipelines harder to maintain. On top of that, many frameworks only validate data after processing — so you can’t react dynamically or fail early when data issues occur.
**SparkDQ** takes a different approach. It’s built specifically for PySpark — so you can define and run data quality checks directly inside your Spark pipelines, using Python. Whether you're validating incoming data, verifying outputs before persistence, or enforcing assumptions in your dataflow: SparkDQ helps you catch issues early, without adding complexity.
<!-- doc-link-start -->
🚀 See the [official documentation](https://sparkdq-community.github.io/sparkdq/) to learn more.
<!-- doc-link-end -->
## Quickstart Examples
Define checks as dictionaries that can be loaded from YAML/JSON files, stored in databases, or generated by APIs — perfect for CI/CD pipelines and data contracts.
```python
from pyspark.sql import SparkSession
from sparkdq.engine import BatchDQEngine
from sparkdq.management import CheckSet
spark = SparkSession.builder.getOrCreate()
df = spark.createDataFrame(
[
{"id": 1, "name": "Alice"},
{"id": 2, "name": None},
{"id": 3, "name": "Bob"},
]
)
# Declarative configuration via dictionary
# Could be loaded from YAML, JSON, or any external system
check_definitions = [
{"check-id": "my-null-check", "check": "null-check", "columns": ["name"]},
]
check_set = CheckSet()
check_set.add_checks_from_dicts(check_definitions)
result = BatchDQEngine(check_set).run_batch(df)
print(result.summary())
```
**Prefer Python-native development?**
Alternatively, you can define checks using Python classes for full type safety, IDE autocompletion, and compile-time validation. [See docs](https://sparkdq-community.github.io/sparkdq/) for examples of both approaches.
## Installation
### For Local Development / Standalone Clusters
Install with PySpark included:
```bash
pip install sparkdq[spark]
```
### For Databricks / Managed Platforms
Install without PySpark (runtime provided by platform):
```bash
pip install sparkdq
```
The framework supports Python 3.10+ and is fully tested with PySpark 3.5.x. SparkDQ will automatically check for PySpark availability on import and provide clear error messages if PySpark is missing in your environment.
## Why SparkDQ?
- ✅ **Robust Validation Layer**: Clean separation of check definition, execution, and reporting
- ✅ **Declarative or Programmatic**: Define checks via config files or directly in Python
- ✅ **Severity-Aware**: Built-in distinction between warning and critical violations
- ✅ **Row & Aggregate Logic**: Supports both record-level and dataset-wide constraints
- ✅ **Typed & Tested**: Built with type safety, testability, and extensibility in mind
- ✅ **Zero Overhead**: Pure PySpark, no heavy dependencies
## Typical Use Cases
SparkDQ is built for modern data platforms that demand trust, transparency, and resilience.
It helps teams enforce quality standards early and consistently — across ingestion, transformation, and delivery layers.
- **Data Ingestion**: Validate raw data as it enters your platform with schema validation, completeness detection, format validation, and early failure detection
- **Lakehouse Quality**: Enforce rules before persisting to storage including Delta/Iceberg/Hudi table validation, partition checks, and data freshness validation
- **ML & Analytics**: Assert conditions before model training with feature quality checks, training data validation, bias detection, and model I/O validation
- **Pipeline Monitoring**: Flag violations in production workflows through real-time alerts, SLA compliance monitoring, data drift detection, and automated incident response
## Let’s Build Better Data Together
⭐️ Found this useful? Give it a star and help spread the word!
📣 Questions, feedback, or ideas? Open an issue or discussion — we’d love to hear from you.
🤝 Want to contribute? Check out [CONTRIBUTING.md](https://github.com/sparkdq-community/sparkdq/blob/main/CONTRIBUTING.md) to get started.
Raw data
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"description": "[](https://github.com/sparkdq-community/sparkdq/actions/workflows/ci.yaml)\n[](https://codecov.io/gh/sparkdq-community/sparkdq)\n[](https://sparkdq-community.github.io/sparkdq/)\n[](https://pypi.org/project/sparkdq/)\n[](https://github.com/sparkdq-community/sparkdq)\n[](LICENSE)\n\n# SparkDQ \u2014 Data Quality Validation for Apache Spark\n\nMost data quality frameworks weren\u2019t designed with PySpark in mind. They aren\u2019t Spark-native and often lack proper support for declarative pipelines. Instead of integrating seamlessly, they require you to build custom wrappers around them just to fit into production workflows. This adds complexity and makes your pipelines harder to maintain. On top of that, many frameworks only validate data after processing \u2014 so you can\u2019t react dynamically or fail early when data issues occur.\n\n**SparkDQ** takes a different approach. It\u2019s built specifically for PySpark \u2014 so you can define and run data quality checks directly inside your Spark pipelines, using Python. Whether you're validating incoming data, verifying outputs before persistence, or enforcing assumptions in your dataflow: SparkDQ helps you catch issues early, without adding complexity.\n\n<!-- doc-link-start -->\n\n\ud83d\ude80 See the [official documentation](https://sparkdq-community.github.io/sparkdq/) to learn more.\n\n<!-- doc-link-end -->\n\n## Quickstart Examples\n\nDefine checks as dictionaries that can be loaded from YAML/JSON files, stored in databases, or generated by APIs \u2014 perfect for CI/CD pipelines and data contracts.\n\n```python\nfrom pyspark.sql import SparkSession\n\nfrom sparkdq.engine import BatchDQEngine\nfrom sparkdq.management import CheckSet\n\nspark = SparkSession.builder.getOrCreate()\n\ndf = spark.createDataFrame(\n [\n {\"id\": 1, \"name\": \"Alice\"},\n {\"id\": 2, \"name\": None},\n {\"id\": 3, \"name\": \"Bob\"},\n ]\n)\n\n# Declarative configuration via dictionary\n# Could be loaded from YAML, JSON, or any external system\ncheck_definitions = [\n {\"check-id\": \"my-null-check\", \"check\": \"null-check\", \"columns\": [\"name\"]},\n]\ncheck_set = CheckSet()\ncheck_set.add_checks_from_dicts(check_definitions)\n\nresult = BatchDQEngine(check_set).run_batch(df)\nprint(result.summary())\n```\n\n**Prefer Python-native development?**\nAlternatively, you can define checks using Python classes for full type safety, IDE autocompletion, and compile-time validation. [See docs](https://sparkdq-community.github.io/sparkdq/) for examples of both approaches.\n\n## Installation\n\n### For Local Development / Standalone Clusters\n\nInstall with PySpark included:\n\n```bash\npip install sparkdq[spark]\n```\n\n### For Databricks / Managed Platforms\n\nInstall without PySpark (runtime provided by platform):\n\n```bash\npip install sparkdq\n```\n\nThe framework supports Python 3.10+ and is fully tested with PySpark 3.5.x. SparkDQ will automatically check for PySpark availability on import and provide clear error messages if PySpark is missing in your environment.\n\n## Why SparkDQ?\n\n- \u2705 **Robust Validation Layer**: Clean separation of check definition, execution, and reporting\n\n- \u2705 **Declarative or Programmatic**: Define checks via config files or directly in Python\n\n- \u2705 **Severity-Aware**: Built-in distinction between warning and critical violations\n\n- \u2705 **Row & Aggregate Logic**: Supports both record-level and dataset-wide constraints\n\n- \u2705 **Typed & Tested**: Built with type safety, testability, and extensibility in mind\n\n- \u2705 **Zero Overhead**: Pure PySpark, no heavy dependencies\n\n## Typical Use Cases\n\nSparkDQ is built for modern data platforms that demand trust, transparency, and resilience.\nIt helps teams enforce quality standards early and consistently \u2014 across ingestion, transformation, and delivery layers.\n\n- **Data Ingestion**: Validate raw data as it enters your platform with schema validation, completeness detection, format validation, and early failure detection\n\n- **Lakehouse Quality**: Enforce rules before persisting to storage including Delta/Iceberg/Hudi table validation, partition checks, and data freshness validation\n\n- **ML & Analytics**: Assert conditions before model training with feature quality checks, training data validation, bias detection, and model I/O validation\n\n- **Pipeline Monitoring**: Flag violations in production workflows through real-time alerts, SLA compliance monitoring, data drift detection, and automated incident response\n\n## Let\u2019s Build Better Data Together\n\n\u2b50\ufe0f Found this useful? Give it a star and help spread the word!\n\n\ud83d\udce3 Questions, feedback, or ideas? Open an issue or discussion \u2014 we\u2019d love to hear from you.\n\n\ud83e\udd1d Want to contribute? Check out [CONTRIBUTING.md](https://github.com/sparkdq-community/sparkdq/blob/main/CONTRIBUTING.md) to get started.\n",
"bugtrack_url": null,
"license": "Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. \"License\" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. \"Licensor\" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. \"Legal Entity\" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. 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For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. \"Contribution\" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. 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Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a \"NOTICE\" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. 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You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets \"[]\" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same \"printed page\" as the copyright notice for easier identification within third-party archives. Copyright 2025 Marcel Kennert Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.",
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