In this video, I talk to Dhruba, CTO @Rockset about search and realtime analytics. We discussed deep internals of Rockset, its architecture and why is it a great fit for search and realtime analytics use cases.

Chapters: 00:00 Introduction02:45 The Evolution of Data Systems: From Hadoop to Rockset07:30 Understanding Rockset: Real-Time Analytics and Search Defined12:01 The Technical Edge: Rockset vs. Elasticsearch18:16 Deep Dive into Rockset's Architecture and Internals28:21 Partitioning, Hashing, and Data Distribution in Rockset36:56 Exploring Hot Storage and Cache Layers37:40 Why Hot Storage is Essential for Low Latency39:05 Optimizing Data Storage with Compression and Delta Encoding39:49 Balancing Cost and Performance in Data Storage41:50 The Power of Converged Indexing in Rockset45:50 Efficient Query Execution and Index Management54:51 Leveraging Mutability for Real-Time Analytics59:24 Deep Dive into Query Processing and Optimization01:04:21 Understanding Joins and Reporting Queries in Rockset01:12:23 Future Directions and Vector Search Innovations

Index Conference: https://rockset.com/index-conf/

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Database internals series: https://youtu.be/yV_Zp0Mi3xs

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