While exotic analytics and machine learning have captured the imagination during the big data era, Online Analytical Processing (OLAP) seemed to be on the verge of decline. As traditional OLAP platforms and vendors struggled with growing data volumes, the seemingly unlimited power and scale of the cloud offered new ways to extract insights from these large datasets.
But new open source technologies like Apache Kylin promise to bring the performance, concurrency, and cost advantages of distributed OLAP to the cloud. In this webinar, Li Kang will demonstrate and discuss:
-Key performance and concurrency advantages of Distributed OLAP vs Cloud Data Warehousing
-How precomputed aggregate indexes (distributed cubes) can dramatically offload query processing engines
-How Apache Kylin enables aggregate indexes of virtually limitless size and scale