datachecks 0.2.1

Open Source Data Quality Monitoring

Open Source Data Quality Monitoring.

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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.

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

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	:thumbsup:
MySql	Transactional Database	:soon:
MS SQL Server	Transactional Database	:soon:
OpenSearch	Search Engine	:thumbsup:
Elasticsearch	Search Engine	:soon:
GCP BigQuery	Data Warehouse	:soon:
AWS RedShift	Data Warehouse	:soon:
DataBricks	Data Warehouse	:soon:
Snowflake	Data Warehouse	:soon:

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

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

Check out the contributions guide and open issues.

Datachecks contributors: :blue_heart:

Telemetry

Usage Analytics & Data Privacy

License

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