In brief
- Arun Khandelwal explains how Databricks, along with Unity Catalog, helps address challenges in traditional data governance such as fragmented data management, manual processes, security concerns, and limited scalability
- Arun also highlights the value of automating data governance processes and integrating real-time monitoring for enhanced decision-making and regulatory compliance
- Key components of a successful data governance framework, include data quality management, security, compliance, and data lineage
- Poor data governance can lead to costly mistakes and compliance issues, you can turn these challenges into opportunities for growth
- Databricks engineering can be leveraged to build a robust data governance framework
- By utilizing Databricks’ data intelligence platform, organizations can transform data governance into a strategic advantage
- The integration of data governance with data engineering practices, traditionally treated as separate disciplines, can be combined within a single platform
- The whitepaper includes best practices to demonstrate how businesses can improve data quality, security, and accessibility, thereby transforming data governance from a burdensome task into a strategic asset
