Delta from a Data Engineer's Perspective
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This document describes the Delta architecture, which unifies batch and streaming data processing. Delta achieves this through a continuous data flow model using structured streaming. It allows data engineers to read consistent data while being written, incrementally read large tables at scale, rollback in case of errors, replay and process historical data along with new data, and handle late arriving data without delays. Delta uses transaction logging, optimistic concurrency, and Spark to scale metadata handling for large tables. This provides a simplified solution to common challenges data engineers face.
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Delta from a Data Engineer's Perspective
- 1. Delta by example Palla Lentz, Assoc. Resident Solutions Architect Jake Therianos, Customer Success Engineer
- 2. A Data Engineer’s Dream... Data Lake CSV, JSON, TXT… Kinesis AI & Reporting Process data continuously and incrementally as new data arrive in a cost efficient way without having to choose between batch or streaming
- 3. Table (Data gets written continuously) AI & Reporting Events Spark job gets exponentially slower with time due to small files. Stream Stream The Data Engineer’s Journey...
- 4. Table (Data gets written continuously) AI & Reporting Events Table (Data gets compacted every hour) Batch Batch Late arriving data means processing need to be delayed Stream The Data Engineer’s Journey...
- 5. Table (Data gets written continuously) AI & Reporting Events Table (Data gets compacted every hour) Few hours latency doesn’t satisfy business needs Batch Batch Stream The Data Engineer’s Journey...
- 6. Table (Data gets written continuously) AI & Reporting Events Batch Stream Unified View Lambda arch increases operational burden Stream Table (Data gets compacted every hour) The Data Engineer’s Journey...
- 7. Table (Data gets written continuously) AI & Reporting Events Batch Batch Stream Unified ViewValidation Validations and other cleanup actions need to be done twice Stream Table (Data gets compacted every hour) The Data Engineer’s Journey...
- 8. Table (Data gets written continuously) AI & Reporting Events Batch Batch Stream Unified ViewValidation Fixing mistakes means blowing up partitions and doing atomic re-publish Reprocessing Stream Table (Data gets compacted every hour) The Data Engineer’s Journey...
- 9. Table (Data gets written continuously) AI & Reporting Events Batch Batch Stream Unified ViewValidation Updates & Merge get complex with data lake Reprocessing Update & Merge Stream Table (Data gets compacted every hour) The Data Engineer’s Journey...
- 10. Table (Data gets written continuously) AI & Reporting Events Batch Batch Stream Unified ViewValidation Updates & Merge get complex with data lake Reprocessing Update & Merge Can this be simplified? Stream The Data Engineer’s Journey... Table (Data gets compacted every hour)
- 11. What was missing? 1. Ability to read consistent data while data is being written 1. Ability to read incrementally from a large table with good throughput 1. Ability to rollback in case of bad writes 1. Ability to replay historical data along new data that arrived 1. Ability to handle late arriving data without having to delay downstream processing Data Lake CSV, JSON, TXT… Kinesis AI & Reporting ?
- 12. So… What is the answer? STRUCTURED STREAMING + = The Delta Architecture 1. Unify batch & streaming with a continuous data flow model 2. Infinite retention to replay/reprocess historical events as needed 3. Independent, elastic compute and storage to scale while balancing costs
- 13. How Delta Lake Works?
- 14. Delta On Disk my_table/ _delta_log/ 00000.json 00001.json date=2019-01-01/ file-1.parquet Transaction Log Table Versions (Optional) Partition Directories Data Files
- 15. Table = result of a set of actions Change Metadata – name, schema, partitioning, etc Add File – adds a file (with optional statistics) Remove File – removes a file Result: Current Metadata, List of Files, List of Txns, Version
- 16. Implementing Atomicity Changes to the table are stored as ordered, atomic units called commits Add 1.parquet Add 2.parquet Remove 1.parquet Remove 2.parquet Add 3.parquet 000000.json 000001.json …
- 17. Solving Conflicts Optimistically 1. Record start version 2. Record reads/writes 3. Attempt commit 4. If someone else wins, check if anything you read has changed. 5. Try again. 000000.json 000001.json 000002.json User 1 User 2 Write: Append Read: Schema Write: Append Read: Schema
- 18. Handling Massive Metadata Large tables can have millions of files in them! How do we scale the metadata? Use Spark for scaling! Add 1.parquet Add 2.parquet Remove 1.parquet Remove 2.parquet Add 3.parquet Checkpoint
- 20. Connecting the dots... Data Lake CSV, JSON, TXT… Kinesis AI & Reporting ?
- 21. Connecting the dots... Snapshot isolation between writers and readers Data Lake CSV, JSON, TXT… Kinesis AI & Reporting ? 1. Ability to read consistent data while data is being written
- 22. Snapshot isolation between writers and readers Optimized file source with scalable metadata handling Connecting the dots... Data Lake CSV, JSON, TXT… Kinesis AI & Reporting ? 1. Ability to read consistent data while data is being written 1. Ability to read incrementally from a large table with good throughput
- 23. Snapshot isolation between writers and readers Optimized file source with scalable metadata handling Time travel Connecting the dots... Data Lake CSV, JSON, TXT… Kinesis AI & Reporting ? 1. Ability to read consistent data while data is being written 1. Ability to read incrementally from a large table with good throughput 1. Ability to rollback in case of bad writes
- 24. Snapshot isolation between writers and readers Optimized file source with scalable metadata handling Time travel Stream the backfilled historical data through the same pipeline Connecting the dots... Data Lake CSV, JSON, TXT… Kinesis AI & Reporting ? 1. Ability to read consistent data while data is being written 1. Ability to read incrementally from a large table with good throughput 1. Ability to rollback in case of bad writes 1. Ability to replay historical data along new data that arrived
- 25. 1. Ability to read consistent data while data is being written 1. Ability to read incrementally from a large table with good throughput 1. Ability to rollback in case of bad writes 1. Ability to replay historical data along new data that arrived 1. Ability to handle late arriving data without having to delay downstream processing Snapshot isolation between writers and readers Optimized file source with scalable metadata handling Time travel Stream the backfilled historical data through the same pipeline Stream any late arriving data added to the table as they get added Connecting the dots... Data Lake CSV, JSON, TXT… Kinesis AI & Reporting ?
- 26. 1. Ability to read consistent data while data is being written 1. Ability to read incrementally from a large table with good throughput 1. Ability to rollback in case of bad writes 1. Ability to replay historical data along new data that arrived 1. Ability to handle late arriving data without having to delay downstream processing Snapshot isolation between writers and readers Optimized file source with scalable metadata handling Time travel Stream the backfilled historical data through the same pipeline Stream any late arriving data added to the table as they get added Connecting the dots... Data Lake CSV, JSON, TXT… Kinesis AI & Reporting
- 27. AI & Reporting Streaming Analytics Data Lake CSV, JSON, TXT… Kinesis The Delta Architecture A continuous data flow model to unify batch & streaming
- 28. Up next...DEMO Website: https://delta.io Community (Slack/Email): https://delta.io/#community