Petabytes, Exabytes, and Beyond - Managing Delta Lakes for Inter

Data production continues to scale up and the techniques for managing it need to scale too. Building pipelines that can process petabytes per day in turn create data lakes with exabytes of historical data. At Databricks, we help our customers turn these data lakes into gold mines of valuable information using Apache Spark. This talk will cover techniques to optimize access to these data lakes using Delta Lakes, including range partitioning, file-based data skipping, multi-dimensional clustering, and read-optimized files. We’ll cover sample implementations and see examples of querying petabytes of data in seconds, not hours.

We’ll also discuss tradeoffs that data engineers deal with everyday like read speed vs. write throughput, managing storage costs, and duplicating data to support multiple query profiles. We’ll also discuss combining batch with streaming to achieve desired query performance. After this session, you will have new ideas for managing truly massive Delta Lakes.

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2.Petabytes, Exabytes, and Beyond Managing Delta Lakes for Interactive Queries at Scale Chris Hoshino-Fish, Databricks #UnifiedDataAnalytics #SparkAISummit

3.Range Partitioning • Evenly balanced partitions • Tune partition and cluster size #UnifiedDataAnalytics #SparkAISummit 3

4.ZOrder Indexing • Multidimensional clustering • Maps multiple columns to 1- dimensional binary space • Effectiveness falls off after 3-5 columns #UnifiedDataAnalytics #SparkAISummit 4

5.Dataskipping • Collects metadata about files • Uses metadata to improve query plans • Combined with ZOrder, can reduce data needed to read by 90% or more #UnifiedDataAnalytics #SparkAISummit 5

6.Tuning File Size • Smaller files - dataskipping can be more effective • Write/Read cost - performance and $ #UnifiedDataAnalytics #SparkAISummit 6

7.Multiple Query Profiles • Assess common query patterns • Understand end-users’ needs • If possible, map secondary queries to use primary index • Create second table with different ZOrder #UnifiedDataAnalytics #SparkAISummit 7

8.Write Throughput vs. Query Speed • More frequent writes -> new data is unoptimized • Tradeoff performance for recency • ZOrder is incremental - only creates new ZCube after threshold of new data #UnifiedDataAnalytics #SparkAISummit 8

9.Streaming vs. Batch • Streaming has incremental processing and reduces compute load, improves stability, and can reduce latency • RocksDB StateStore to scale up state management #UnifiedDataAnalytics #SparkAISummit 9

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