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1
Introduction
2
Data Lakes
3
Typical Data Lake Project
4
Who uses Delta
5
Getting started
6
Data
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Download Data
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Park Table
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Stop Streaming
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Initializing Streaming
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Working with Parker
12
Using Delta Lake
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Streaming Job
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Multiple Streaming Queries
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Counting Continuously
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Schema Evolution
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Merged Schema
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Summary
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History
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Vacuum
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Mods
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Merge
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Update Data
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Define DataFrame
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Merge Syntax
26
Random Data
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For Each Batch
28
Summarize
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Community
30
Question
31
Thank you
Description:
Explore the world of data reliability and performance in big data workloads through this 43-minute tutorial on building data-intensive analytic applications with Delta Lake. Learn how Delta Lake, an open-source storage layer, brings ACID transactions to Apache Spark™ and addresses key challenges faced by data engineers. Discover the requirements of modern data engineering and how Delta Lake can improve data reliability at scale. Through presentations, code examples, and interactive notebooks, gain insights into applying this innovation to your data architecture. Understand key data reliability challenges, how Delta Lake fits within an Apache Spark™ environment, and practical ways to implement data reliability improvements. Dive into topics such as data lakes, streaming, schema evolution, and merge operations while exploring hands-on examples using Delta Lake's features.

Building Data Intensive Analytic Applications on Top of Delta Lakes

Databricks
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