How does the Atlas framework monitor data quality?
The Atlas framework is an open-source platform for monitoring data quality and managing metadata. It offers a set of tools and techniques to assist users in implementing data quality monitoring. The following are the general steps for conducting data quality monitoring within the Atlas framework.
- Define data quality metrics: first, you need to define the data quality metrics to monitor. These metrics may include data completeness, accuracy, consistency, etc. You can customize these metrics based on your needs and configure them within the Atlas framework.
- Collecting data quality metrics: Once the metrics are defined, the Atlas framework automatically gathers and calculates the measurements. It can collect data from various sources such as relational databases, Hadoop clusters, data lakes, etc., and calculate metrics based on the defined indicators.
- Analyze and monitor data quality: The Atlas framework offers visual dashboards and reports for analyzing and monitoring data quality. Using these tools, you can view and track real-time data quality metrics and identify any potential data quality issues.
- Atlas framework can automatically send alerts and notifications if any data quality issues are detected. You can set up alert rules and choose the preferred notification method, such as email or text message, to notify the relevant team members.
- Data quality governance: The Atlas framework also offers data quality governance functionality to help establish and enforce data quality policies. It can track solutions to data quality issues, as well as record and audit the process of improving data quality.
In conclusion, the Atlas framework helps users monitor data quality by defining metrics, collecting measurements, analyzing monitoring, sending alerts and notifications, and governing data quality.
