Migrating Legacy Data Warehouses to Cloud-Based Data Lakes: A Practical Approach [Video]

As data volumes increase at an unprecedented rate, traditional data warehouses often fall short in meeting the growing need for real-time analytics and scalable data management.  Vishnu Vardhan Reddy Chilukoori, Srikanth Gangarapu, Abhishek Vajpayee, and Rathish Mohan examine the move from legacy data warehouses to cloud-based data lakes. They offer an in-depth guide on navigating this transition, addressing key challenges, outlining effective strategies, and sharing best practices for a smooth and successful migration process.

Understanding the Migration Landscape

Legacy data warehouses rely on structured models and predefined schemas, which can cause delays in data availability because of their batch-oriented ETL processes. This approach limits the ability to handle diverse and rapidly changing data types efficiently. In contrast, cloud-based data lakes provide flexible storage and processing capabilities for structured, semi-structured, and unstructured data, eliminating the need for predefined schemas. This shift allows for real-time data processing, supports advanced analytics, and offers virtually unlimited scalability, fostering a more agile and responsive …