Elevate Your Data, Empower Your Business
Welcome to the future of data management with Dynamic Data Quality – your automated solution for monitoring and enhancing data quality directly within the Snowflake environment. Designed to seamlessly integrate with your Snowflake data cloud, Dynamic Data Quality is the key to unlocking superior business value and optimizing AI-driven initiatives.
Why Dynamic Data Quality?
- Automated Monitoring: Leverage cutting-edge automation to ensure your data is consistently accurate, complete, and reliable. Dynamic Data Quality provides continuous, real-time oversight, detecting and alerting you to any quality issues as they arise.
- Snowflake-Native Solution: Built exclusively for Snowflake, our application ensures a smooth, integrated experience that maximizes your cloud data platform’s capabilities without the need for external tools or platforms.
- Drive Business Value: Clean and quality-assured data means better decision-making and enhanced business insights. Dynamic Data Quality ensures your organization operates on a foundation of trust in its data.
- AI and Machine Learning Optimization: The success of AI initiatives hinges on the quality of data fed into your models. Our solution guarantees that your AI and ML projects are powered by the highest quality data, enhancing outcomes and efficiencies.
Key Features
- Flexible Quality Rules: Customize your data quality checks with rules that fit your specific business requirements, ensuring relevance and precision in monitoring.
- Scalable for All Business Sizes: Dynamic Data Quality is designed to grow with you, providing scalable solutions that fit your business needs, regardless of size.
- Prevent Proliferation of Poor Quality Data: Create validation checks in your transformation pipeline to stop transformation in presence of critical test failures.
- Real-Time Data Quality Alerts: Stay ahead of issues with alerts that notify you immediately of any detected anomalies or errors in your data.
3 Simple Steps to Monitoring Data Quality
Step 1:
Identify Tables to Monitor for Data Quality
After providing the application access to the databases you wish to monitor, using the interface, select the tables to monitor for data quality. It is suggested to monitor raw source tables to catch and remediate data quality issues at the start of the pipeline. You can add and remove tables from monitoring as needed.
Step 2:
Configure Column Level Tests to Define Quality Data
Using the interface, you can customize column level tests to ensure the definition of quality data meets the needs of your pipeline. There is no limit to the number of tests and configurations per column.
Step 3:
Automate Data Quality
Configure an automated schedule per table to ensure new data quality meets expectations. With the flexibility to set a schedule per table, you can optimize your pipeline to ensure resources are being used appropriately to monitor data quality. Additionally you can manually run data quality tests at any time to spot check a table.
Prevent Proliferation
of Poor Quality Data
Using the critical failure view, you can add conditions to your transformation pipeline to pause in the event a table experiences a critical test failure, preventing poor quality data from being pushed further down your pipeline.
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