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Ensure Effective Data Quality Management in Atlan

Overview

Atlan is a modern data collaboration and management platform designed to streamline data discovery, governance, and collaboration within organizations. It helps data teams work together more effectively by providing a unified workspace where they can discover, document, and collaborate on data assets.

DQLabs allows users to integrate with the Atlan data catalog, pulling metrics from Atlan into DQLabs and pushing metrics from DQLabs to Atlan. Integrating DQLabs with Atlan enhances data quality management by allowing seamless synchronization of key metrics between both platforms. With this integration, users can push essential data quality metrics from DQLabs directly to Atlan’s catalog, ensuring real-time visibility and management of data quality at the asset level. The integration enables the automatic transfer of metrics such as DQ Score, Total Rows, Freshness, Active Rules, Observed Rules, Duplicate Rows, Alerts, Issues, and more.

Data Quality and Observability for Atlan

Unified Data Quality Metrics

With DQLabs data quality metrics pushed directly into Atlan, users gain comprehensive visibility into data quality across all assets, attributes, and domains within the catalog. Key metrics such as DQ score, freshness, duplicate rows, alerts, and issues are automatically updated, enabling real-time monitoring and proactive identification of data quality issues without switching between systems. The integration also pushes DQLabs’ critical metrics to custom fields within Atlan. This ensures that data quality information is seamlessly incorporated into the metadata without the need for manual tracking or separate tools, improving operational efficiency.

Data Quality Governance

By pushing DQLabs metrics into Atlan, data stewards and governance teams can better enforce data quality standards across their organization. Key governance metrics such as active rules, observed rules, and scoring rules are easily accessible within Atlan’s metadata framework, making it easier to track compliance and enforce policies around data quality.

Alerts and Issues Integration

The integration enables alerts and issues from DQLabs to be directly linked within Atlan. This streamlines the process of identifying, addressing, and documenting data quality issues. Users can quickly navigate to DQLabs to view more details about the alerts or issues, reducing response time and minimizing potential risks related to poor data quality.

Automated Data Updates

The integration automatically pushes and pulls data quality metrics as part of Atlan’s catalog job process, reducing manual intervention and ensuring that data quality information is always up-to-date. The automation of this workflow eliminates the need for time-consuming data imports or manual data quality updates.

Flexible Metrics Configuration

Users can configure which metrics to push or pull to and from Atlan, providing flexibility in how data quality information is managed and reported. Whether it's focusing on high-priority assets or prioritizing specific domains, businesses can tailor the integration to meet their unique needs.

Data Quality Context at the Asset Level

The ability to view data quality metrics like DQ Score, Total Rows, Alerts, and Issues directly on the asset, attribute, and domain levels in Atlan allows users to contextualize metadata with data quality insights. This transparency supports more informed decision-making and collaboration around data usage, quality, and improvement.

Faster Issue Resolution

With DQLabs’ issues and alerts linked to Atlan’s catalog, users can quickly pinpoint where data quality problems are originating, whether at the asset, attribute, or domain level. This helps teams resolve issues faster and prevent data quality problems from propagating across the organization.

Comprehensive Reporting

The ability to push and pull comprehensive reports on data quality metrics (such as summary statistics, alerts, and issues) from DQLabs into Atlan simplifies reporting and decision-making. Teams can track the health of their data over time and ensure alignment with business goals.

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