You're reading from Azure Data Engineering Cookbook Get well versed in various data engineering techniques in Azure using this recipe-based guide

Product type Paperback

Published in Sep 2022

Publisher Packt

ISBN-13 9781803246789

Length 608 pages

Edition 2nd Edition

Languages

SQL

Tools

Azure

Concepts

Data Engineering

Authors (3):

Ahmad Osama

Nagaraj Venkatesan

Luca Zanna

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Table of Contents (16) Chapters

Preface

1. Chapter 1: Creating and Managing Data in Azure Data Lake

2. Chapter 2: Securing and Monitoring Data in Azure Data Lake FREE CHAPTER

3. Chapter 3: Building Data Ingestion Pipelines Using Azure Data Factory

4. Chapter 4: Azure Data Factory Integration Runtime

5. Chapter 5: Configuring and Securing Azure SQL Database

6. Chapter 6: Implementing High Availability and Monitoring in Azure SQL Database

7. Chapter 7: Processing Data Using Azure Databricks

8. Chapter 8: Processing Data Using Azure Synapse Analytics

9. Chapter 9: Transforming Data Using Azure Synapse Dataflows

10. Chapter 10: Building the Serving Layer in Azure Synapse SQL Pool

11. Chapter 11: Monitoring Synapse SQL and Spark Pools

12. Chapter 12: Optimizing and Maintaining Synapse SQL and Spark Pools

13. Chapter 13: Monitoring and Maintaining Azure Data Engineering Pipelines

14. Index

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Monitoring Synapse SQL and Spark Pools

As introduced in Chapter 8, Processing Data Using Azure Synapse Analytics, Azure Synapse Analytics is comprised of three key components – a Synapse integration pipeline to ingest and transform the data, SQL pool and Spark pool to process and serve the data, and Power BI integration to visualize the data. Monitoring the health of Synapse SQL and Spark pools is an integral part of the work of a data engineer managing large data engineering projects.

In this chapter, you will learn how to integrate Synapse SQL and Spark pools with Azure Log Analytics, identify long-running Spark jobs and SQL queries using a Log Analytics workspace, monitor Synapse health from Azure Monitor, and check the table and index health of SQL dedicated pool tables using Dynamic Management Views (DMVs).

In this chapter, we will cover the following recipes: