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Databricks Lakehouse and Spark Basics: How Distributed Data Jobs Run

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Databricks lakehouse and Spark basics explain how a managed platform stores governed data and runs work across a cluster. You learn why Spark waits to execute until an action, how the driver splits jobs among executors, and what ACID tables add to a data lake.

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What You'll Learn

Concepts:
Apache Spark Distributed Execution: Lazy Evaluation, DAG Scheduling, and the Driver/Executor Model The Data Lakehouse: ACID Compliance, Governance, and Managed Spark Platforms

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Databricks Lakehouse and Spark Basics: How Distributed Data Jobs Run

About R. Daneel Olivaw

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