Ensuring data quality in complex data environments can be challenging, particularly when monitoring “data at rest”—data that resides within warehouses. Data characteristics can change over time, leading to inaccuracies. Continuous data quality monitoring is required to make sure that the data remains accurate, complete, consistent, unique, and timely.
SQL data quality complements Databand’s existing capabilities for data-in-motion observability by allowing users to monitor static data assets within their warehouses proactively. Through customizable SQL-based rule settings, you can now define data quality criteria, receive alerts when data deviates from these criteria, and track historical trends to ensure ongoing data integrity.
With this feature, Databand provides a solution for users responsible for various data assets, such as datasets, tables, columns, or specific data fragments over a given timeframe. Users now have a powerful tool to identify and resolve issues early, minimizing the risk of incorrect data propagating through analytics and decision-making workflows.
Key Features
1. Customizable SQL-Based Data Quality Rules
Users can define validation rules based on SQL queries, tailoring the criteria to align with specific data quality standards. This flexibility enables monitoring at any level of granularity—from an entire dataset down to individual columns or particular data fragments. Data owners can specify precise validation criteria to capture data inconsistencies, anomalies, or changes in structure.
2. Proactive Alerts for Data Quality Breaches
Data quality issues often surface at critical moments, resulting in reactive firefighting by data teams. Our alerting system allows data owners to establish proactive monitoring where breaches of defined criteria trigger notifications. These alerts empower teams to immediately address and resolve quality issues, reducing downstream impacts on analytics, reporting, and other data-dependent processes.
3. Historical Trend Analysis for Ongoing Improvement
Beyond immediate alerts, users can leverage trend monitoring to track the historical behavior of their data quality metrics. This feature provides valuable insight into recurring issues or emerging patterns, equipping data owners with actionable information for continuous improvement. The ability to monitor trends over time aids in developing robust data governance practices and fosters a proactive approach to data quality management.
Overall, IBM Databand’s support for custom SQL-based validation rules provides a flexible and powerful way to maintain data integrity and accuracy in a data warehouse. By automating data validation, you can improve data quality, reduce errors, and enforce business rules, ultimately leading to more reliable and trustworthy data. Sign up for a free 14-day IBM Databand sandbox and experience it yourself.
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