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Azure Data Engineer Associate Certification Guide

You're reading from  Azure Data Engineer Associate Certification Guide

Product type Book
Published in Feb 2022
Publisher Packt
ISBN-13 9781801816069
Pages 574 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Newton Alex Newton Alex
Profile icon Newton Alex

Table of Contents (23) Chapters

Preface Part 1: Azure Basics
Chapter 1: Introducing Azure Basics Part 2: Data Storage
Chapter 2: Designing a Data Storage Structure Chapter 3: Designing a Partition Strategy Chapter 4: Designing the Serving Layer Chapter 5: Implementing Physical Data Storage Structures Chapter 6: Implementing Logical Data Structures Chapter 7: Implementing the Serving Layer Part 3: Design and Develop Data Processing (25-30%)
Chapter 8: Ingesting and Transforming Data Chapter 9: Designing and Developing a Batch Processing Solution Chapter 10: Designing and Developing a Stream Processing Solution Chapter 11: Managing Batches and Pipelines Part 4: Design and Implement Data Security (10-15%)
Chapter 12: Designing Security for Data Policies and Standards Part 5: Monitor and Optimize Data Storage and Data Processing (10-15%)
Chapter 13: Monitoring Data Storage and Data Processing Chapter 14: Optimizing and Troubleshooting Data Storage and Data Processing Part 6: Practice Exercises
Chapter 15: Sample Questions with Solutions Other Books You May Enjoy

Designing metastores in Azure Synapse Analytics and Azure Databricks

Metastores store the metadata of data in services such as Spark or Hive. Think of a metastore as a data catalog that can tell you which tables you have, what the table schemas are, what the relationships among the tables are, where they are stored, and so on. Spark supports two metastore options: an in-memory version and an external version.

In-memory metastores are limited in accessibility and scale. They can help jobs running on the same Java virtual machine (JVM) but not much further than this. Also, the metadata is lost once the cluster is shut down.

For all practical purposes, Spark uses an external metastore, and the only supported external metastore at the time of writing this book was Hive Metastore. Hive's metastore is mature and provides generic application programming interfaces (APIs) to access it. Hence, instead of rebuilding a new metastore, Spark just uses the mature and well-designed Hive...

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