You're reading from Limitless Analytics with Azure Synapse An end-to-end analytics service for data processing, management, and ingestion for BI and ML

Product type Paperback

Published in Jun 2021

Publisher Packt

ISBN-13 9781800205659

Length 392 pages

Edition 1st Edition

Languages

Python

Tools

Azure

Concepts

Data Processing

Authors (2):

Saranya Ravichander

Prashant Kumar Mishra

View More author details

Table of Contents (20) Chapters

Preface

1. Section 1: The Basics and Key Concepts

2. Chapter 1: Introduction to Azure Synapse FREE CHAPTER

3. Chapter 2: Considerations for Your Compute Environment

4. Section 2: Data Ingestion and Orchestration

5. Chapter 3: Bringing Your Data to Azure Synapse

6. Chapter 4: Using Synapse Pipelines to Orchestrate Your Data

7. Chapter 5: Using Synapse Link with Azure Cosmos DB

8. Section 3: Azure Synapse for Data Scientists and Business Analysts

9. Chapter 6: Working with T-SQL in Azure Synapse

10. Chapter 7: Working with R, Python, Scala, .NET, and Spark SQL in Azure Synapse

11. Chapter 8: Integrating a Power BI Workspace with Azure Synapse

12. Chapter 9: Perform Real-Time Analytics on Streaming Data

13. Chapter 10: Generate Powerful Insights on Azure Synapse Using Azure ML

14. Section 4: Best Practices

15. Chapter 11: Performing Backup and Restore in Azure Synapse Analytics

16. Chapter 12: Securing Data on Azure Synapse

17. Chapter 13: Managing and Monitoring Synapse Workloads

18. Chapter 14: Coding Best Practices

19. Other Books You May Enjoy

Bringing data to Azure Synapse

In the Understanding the architecture and components section, we saw how architectures use different Azure services to perform real-time analytics on Azure. In this section, our main focus is to bring data from all data sources to Azure Synapse. We are going to learn about bringing data to Azure Synapse by using Azure Stream Analytics jobs, and later we will see how we can use Azure Databricks to copy data to Azure Synapse.

Using Azure Stream Analytics

Azure Stream Analytics is a real-time analytics engine that is designed to process a large volume of streaming data from various sources to various targets. Within Azure Stream Analytics, you can create an Azure Stream Analytics job that consists of an input, a query, and an output. You can use the Stream Analytics job to ingest data directly from the source to the target as is, or you can perform certain aggregation operations on the input data before sending it to the target.

These are the...