Data quality framework on Databricks
Ensure reliable, high-quality data for seamless business operations with our advanced data quality framework built on Unity Catalog and Lakehouse Monitoring.
Elevate your data integrity and proactive data management with zeb’s Data Quality Framework
Inaccurate, incomplete, or inconsistent data can result in misleading insights and hinder business processes. Our Data Quality Framework enables end-to-end data quality pipelines that validate, cleanse, and monitor data in real time, ensuring accuracy and consistency. By integrating automated quality checks into your workflows, you can minimize risks, enhance decision-making, and drive greater operational efficiency. Additionally, our framework includes proactive reporting and alerting of data failures, providing real-time monitoring and detailed reports whenever data issues are detected, enabling timely intervention and minimizing the impact on business operations. The system also automates alerts when a pipeline fails or a data integrity rule is breached, ensuring that issues are promptly identified and addressed without manual oversight, maintaining the reliability and accuracy of your data processes.
What can we do for you?
Seamless Data Quality Integration
Implement scalable data quality pipelines that automate data validation, transformation, and governance. Ensure every data asset meets accuracy and compliance standards, strengthening the foundation for analytics and AI-driven initiatives.
Proactive Monitoring with Databricks Lakehouse Monitoring
Utilize Databricks Lakehouse Monitoring to track data integrity and detect anomalies before they impact critical business decisions. Set up automated alerts to maintain data consistency across data engineering and AI workloads.
Enhanced Data Governance on Unity Catalog
Implement robust data governance frameworks to ensure data privacy, security, and compliance. Leverage Databricks' capabilities to manage data access controls, audit trails, and policy enforcement, ensuring that your data assets are protected and compliant with regulatory requirements.
Intelligent Data Validation with Databricks DQX
Leverage Databricks DQX to define, monitor, and enforce data quality rules within your Apache Spark pipelines. With row- and column-level validation, automated profiling, and real-time monitoring, DQX ensures seamless data integrity across batch and streaming workflows. Its configurable rule-based framework enables custom reactions to failed checks, empowering teams to maintain compliance and make data-driven decisions.
Scalable and Customizable Framework
Adapt and scale your data quality framework to meet evolving business needs. Our modular approach ensures seamless integration with existing workflows, allowing continuous improvements in data governance and operational efficiency.
Future-proof your data strategy with intelligent quality controls
- Maintain end-to-end data integrity with continuous quality checks and automated validation mechanisms.
- Empower business users and data teams with a centralized framework that enhances visibility, governance, and compliance.
- Enable AI-driven analytics with clean, structured, and high-fidelity data to maximize business impact.
- Gain a competitive edge with a scalable, intelligent data quality solution designed for dynamic data environments.
- Set up automated alerts for data failures or data pipeline completion to ensure timely intervention and operational efficiency.
- Visualize data drift using Databricks Lakehouse Monitoring to detect and address changes in data distributions, maintaining the accuracy and reliability of your analytics and AI models.