Recent developments in data management—self-service, big data, data lakes, NoSQL, Hadoop, and the cloud—raise questions about the role of the data warehouse in the age of analytics. Legacy data warehouses must be modernized if they are to fit gracefully into modern analytics ecosystems. Despite declarations to the contrary, the data warehouse is not dead.
Recent surveys show that more than 60% of companies are operating between two and five data warehouses, and fewer than 10% have only one data warehouse or none at all. It is clear that data warehousing is needed. People continue to need well-integrated, systematically cleansed, easy-to-access data that includes time-variant history. But data warehousing must evolve and adapt to fit with the realities of modern data management and to overcome the challenges of scalability and elasticity, data variety, data latency, and adaptability.
Join us to learn about the challenges of legacy data warehousing, the goals of modern data warehousing, and the design patterns and frameworks that help to accelerate modernization efforts. You will learn:
The big challenges of legacy data warehousing
Architectural frameworks to position data warehousing as an integral component of a modern analytics ecosystem
How big data and unstructured data influence the future of data warehousing and modern analytics infrastructure
How high-velocity data and data streams influence the future of data warehousing and modern analytics infrastructure
Advantages of cloud data warehousing and cloud-optimized architecture for warehouse modernization
Tips for getting started with data warehouse modernization