Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Azure Synapse Analytics Cookbook

You're reading from   Azure Synapse Analytics Cookbook Implement a limitless analytical platform using effective recipes for Azure Synapse

Arrow left icon
Product type Paperback
Published in Apr 2022
Publisher Packt
ISBN-13 9781803231501
Length 238 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Authors (2):
Arrow left icon
Gaurav Agarwal(BLR) Gaurav Agarwal(BLR)
Author Profile Icon Gaurav Agarwal(BLR)
Gaurav Agarwal(BLR)
Meenakshi Muralidharan Meenakshi Muralidharan
Author Profile Icon Meenakshi Muralidharan
Meenakshi Muralidharan
Arrow right icon
View More author details
Toc

Table of Contents (11) Chapters Close

Preface 1. Chapter 1: Choosing the Optimal Method for Loading Data to Synapse 2. Chapter 2: Creating Robust Data Pipelines and Data Transformation FREE CHAPTER 3. Chapter 3: Processing Data Optimally across Multiple Nodes 4. Chapter 4: Engineering Real-Time Analytics with Azure Synapse Link Using Cosmos DB 5. Chapter 5: Data Transformation and Processing with Synapse Notebooks 6. Chapter 6: Enriching Data Using the Azure ML AutoML Regression Model 7. Chapter 7: Visualizing and Reporting Petabytes of Data 8. Chapter 8: Data Cataloging and Governance 9. Chapter 9: MPP Platform Migration to Synapse 10. Other Books You May Enjoy

Data loading best practices

Azure Synapse Analytics has a rich set of tools and methods available to load data into SQL pool. You can load data from relational or non-relational data stores; structured or semi-structured data; on-premises systems or other clouds; in batches or streams. The loading can be done using various methods, such as with PolyBase, using the COPY into command, using ADF, or creating a data flow.

How to do it…

In this section, we'll look at some basic best practices to keep in mind as you work.

Retaining a well-engineered data lake structure

Retaining a well-engineered data lake structure allows you to know that the data you're loading regularly is consistent with the data requirements for your system.

When loading large datasets, it's recommended to use the compression capabilities of the file format. This ensures that less time is spent on the process of transferring data, using instead the power of Azure Synapse's...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime