Skip to content

Latest commit

 

History

History
123 lines (85 loc) · 7.5 KB

README.md

File metadata and controls

123 lines (85 loc) · 7.5 KB
Raw

Logo

Open Source Data Quality Monitoring.

License Versions coverage coverage Status

⭐️ If you like it, star the repo ⭐

| Docs | Discord |

What is datachecks?

Datachecks is an open-source data monitoring tool that helps to monitor the data quality of databases and data pipelines. It identifies potential issues, including in the databases and data pipelines. It helps to identify the root cause of the data quality issues and helps to improve the data quality.

Datachecks can generate several reliability, uniqueness, completeness metrics from several data sources

Why Data Monitoring?

APM (Application Performance Monitoring) tools are used to monitor the performance of applications. APM tools are mandatory part of dev stack. Without AMP tools, it is very difficult to monitor the performance of applications.

why_data_observability

But for Data products regular APM tools are not enough. We need a new kind of tools that can monitor the performance of Data applications. Data monitoring tools are used to monitor the data quality of databases and data pipelines. It identifies potential issues, including in the databases and data pipelines. It helps to identify the root cause of the data quality issues and helps to improve the data quality.

Architecture

datacheck_architecture

What Datacheck does not do?

Getting Started

Install datachecks with the command that is specific to the database.

Install Datachecks

To install all datachecks dependencies, use the below command.

pip install datachecks -U

Please visit the Quick Start Guide

Supported Data Sources

Datachecks supports sql and search data sources. Below are the list of supported data sources.

Data Source Type Supported
Postgres Transactional Database 👍
MySql Transactional Database 👍
MS SQL Server Transactional Database 🔜
OpenSearch Search Engine 👍
Elasticsearch Search Engine 👍
GCP BigQuery Data Warehouse 👍
DataBricks Data Warehouse 👍
Snowflake Data Warehouse 🔜
AWS RedShift Data Warehouse 🔜

Metric Types

Metric Description
Reliability Metrics Reliability metrics detect whether tables/indices/collections are updating with timely data
Numeric Distribution Metrics Numeric Distribution metrics detect changes in the numeric distributions i.e. of values, variance, skew and more
Uniqueness Metrics Uniqueness metrics detect when data constraints are breached like duplicates, number of distinct values etc
Completeness Metrics Completeness metrics detect when there are missing values in datasets i.e. Null, empty value
Validity Metrics Validity metrics detect whether data is formatted correctly and represents a valid value

Community & Support

For additional information and help, you can use one of these channels:

  • Discord (Live chat with the team, support, discussions, etc.)
  • GitHub issues (Bug reports, feature requests)

Contributions

🙌 We greatly appreciate contributions - be it’s a bug fix, new feature, or documentations!

Check out the contributions guide and open issues.

Datachecks contributors: 💙

Telemetry

Usage Analytics & Data Privacy

License

This project is licensed under the terms of the APACHE 2 License.