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StarRocks 3.3 is Here: Key Features and Improvements

StarRocks 3.3 is here, and it's more powerful than ever! In this video, we'll walk you through everything you need to know to get the most out of this release. Let's dive in and explore the new features and enhancements together! ----------------------------------------------------------------------------------------------------------------------- 00:00 Intro & Agenda 01:21 StarRocks Use Cases - Lakehouse Query Engine 03:07 StarRocks Use Cases - Real-Time Analytics Workloads 05:24 StarRocks 3.3: Shared-Data 05:36 Shared-Data: Fast Scheme Evolution 07:12 Shared-Data: Shared Data Manual Compaction 08:01 Shared-Data: Other 10:34 Q&A: Is there any downside to using Fast Schema Evolution? or is this strictly better than previous functionality? 11:11 Q&A: Is the Data Migration Tool a 1-time copy tool or does it sync data between clusters live continuously (CDC)? 11:34 Q&A: What is the query performance with Express One Zone? Is there an improvement? 12:19 Q&A: An implementation question, what protocol does Starrocks use for internode shuffling? Does it use grpc or something similar? 12:38 StarRocks 3.3: Data Lake Analytics 12:50 Data Lake Analytics: Data Cache-Cache Warmup 14:58 Data Lake Analytics: Data Cache-Others 16:41 Data Lake Analytics: Apache Iceberg Catalog - New Metadata Framework 19:17 Data Lake Analytics: Apache Paimon Catalog, Clcikhouse Catalog and Kudu Catalog 22:09 Q&A: Any support for Avro files ingestion yet? 22:31 StarRocks 3.3: Materialized View 22:34 Challenge with Pre-Computation Pipelines 24:20 StarRocks Materialized View 25:45 Materialized View Rewrite - View-Based Rewrite 27:26 Materialized View Rewrite - Aggregated Push-Down 28:38 Materialized View Consistency - When Querying MV Directly 30:01 Materialized View Consistency - Data Freshness vs. Performance 31:05 Partitioned Materialized View -Enhancements 32:55 Optimizer - Materialized View Performance 36:06 Q&A: Assuming that the materialized view supports multiple tables, if at 7:00 PM the materialized view builds 100 rows based on the state of the tables, and at 7:01 PM the underlying tables change, resulting in 10 records being updated in the materialized view, as a consumer of the materialized view, will I have timestamp support that shows 7:01 PM for those 10 rows? 37:46 StarRocks 3.3: Query and Storage 37:48 Query and Storage- Data Processing: Spill to Disk GA; Temporary Table 39:37 Query and Storage-Data Processing: Grouped Execution for Collocated Groups 40:47 Semi-Structured Data- JSON 41:53 Storage - Expression Partition 42:41 Storage - Indexes: Ngram Bloom Filter; Inverted Index; 44:03 AWS Graviton Support 44:56 StarRocks Community Updates 46:33 Q&A: Is the enable_spill set in fe.conf? 46:47 Q&A: Do you guys have any webinars planned for teaching about optimizing partitioning? See StarRocks Best Practices: Data Modeling - https://www.starrocks.io/blog/starroc... ----------------------------------------------------------------------------------------------------------------------- Learn more at https://starrocks.com/ Connect with us: LinkedIn:   / celerdata   Twitter:   / celerdata   CelerData Website: https://celerdata.com/ StarRocks GitHub: https://github.com/StarRocks/StarRocks StarRocks Website: https://www.starrocks.io/ Slack: https://try.starrocks.com/join-starro... #DataAnalytics #DataEngineering #DataLakeAnalytics #OLAP #DataAnalyst #DataEngineer #DataInfrastructure #UserFacingAnalytics #Database #AnalyticalDatabase #DataLake #DataLakeHouse #DataWarehouse #DataScience