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Intro
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Outline
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Delta On Disk
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Table = result of a set of actions
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Implementing Atomicity
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Ensuring Serializability
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Solving Conflicts Optimistically
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Handling Massive Metadata Large tables can have millions of files in them! How do we scale the metadata? Use Spark for scaling!
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Checkpoints
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Computing Delta's State
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Updating Delta's State
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Time Travelling by version
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Time Travelling by timestamp
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Time Travel Limitations
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Batch Queries on a Delta Table
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Streaming Queries on a Delta Table
Description:
Explore the inner workings of Delta Lake's transaction log in this 30-minute tech talk from Databricks. Delve into the core component that enables ACID transactions, scalable metadata handling, and time travel functionality. Learn about the transaction log's structure, its role in managing concurrent reads and writes, and how it operates at the file level. Discover how this elegant solution addresses multiple use cases, including data lineage and debugging. Gain insights into implementing atomicity, ensuring serializability, and solving conflicts optimistically. Understand the challenges of handling massive metadata in large tables and how Spark is utilized for scaling. Examine checkpointing, state computation and updates, time travel capabilities, and limitations. Finally, explore how batch and streaming queries interact with Delta tables.

Diving into Delta Lake: Understanding the Transaction Log

Databricks
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