In the age of data-driven decision-making, it is crucial to ensure the quality of databases and data pipelines. Traditional application performance monitoring (APM) tools are not sufficient for monitoring data applications. This is where Datachecks, an open-source data monitoring tool, comes into play.
Datachecks is designed to identify potential issues in databases and data pipelines, helping you improve data quality. It offers a range of reliability, uniqueness, completeness, and validity metrics from various data sources. With its comprehensive data quality reports, you can easily visualize and share the status of your data assets with your team.
Key Features
Datachecks provides two main ways to visualize data quality:
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Reports: Data Quality Visualization – With a single command, you can generate a beautiful HTML report that showcases all the metrics. This report can be easily shared with your team for collaboration and decision-making.
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CLI: Data Quality Visualization in Bash – Data quality reports can also be generated in the terminal, making it convenient for debugging and quick assessments.
Supported Data Sources
Datachecks supports a variety of data sources, including transactional databases like Postgres and MySQL, search engines like OpenSearch and Elasticsearch, and data warehouses like GCP BigQuery and DataBricks. You can choose the data source that suits your needs, and Datachecks will provide the necessary metrics and insights.
Metrics for Data Quality
Datachecks offers a range of metrics to assess different aspects of data quality:
- Reliability Metrics: Detect whether tables, indices, and collections are updating with timely data.
- Numeric Distribution Metrics: Track changes in numeric distributions, such as values, variance, and skew.
- Uniqueness Metrics: Identify data constraints breaches, such as duplicates and the number of distinct values.
- Completeness Metrics: Detect missing values in datasets, including null and empty values.
- Validity Metrics: Assess whether data is formatted correctly and represents a valid value.
Architecture
Datachecks follows a robust architecture that ensures efficient data quality monitoring. It integrates with various data sources, collects relevant metrics, and provides visualization options. The architecture diagram gives an overview of how Datachecks works.
Community & Support
Datachecks has a vibrant community that offers support and assistance through various channels, including Slack and GitHub issues. If you have questions, need help, or want to contribute, you can join the Datachecks community.
Conclusion
Datachecks is a powerful open-source data monitoring tool that helps you enhance the quality of your databases and data pipelines. By leveraging its features, supported data sources, and comprehensive metrics, you can gain valuable insights into your data assets and take the necessary steps to improve data quality.
Remember, data quality is essential for accurate decision-making, and with Datachecks, you have a reliable tool to monitor and maintain your data assets effectively.
Have questions or want to learn more? Join the Datachecks community and start improving your data quality today!
References:
Contributors:
We greatly appreciate the contributions of the following individuals to the Datachecks project:
License:
Datachecks is licensed under the terms of the APACHE 2 License.
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