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Azure Synapse Analytics Cookbook

Azure Synapse Analytics Cookbook

By : Agarwal(BLR), Muralidharan
4.7 (18)
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Azure Synapse Analytics Cookbook

Azure Synapse Analytics Cookbook

4.7 (18)
By: Agarwal(BLR), Muralidharan

Overview of this book

As data warehouse management becomes increasingly integral to successful organizations, choosing and running the right solution is more important than ever. Microsoft Azure Synapse is an enterprise-grade, cloud-based data warehousing platform, and this book holds the key to using Synapse to its full potential. If you want the skills and confidence to create a robust enterprise analytical platform, this cookbook is a great place to start. You'll learn and execute enterprise-level deployments on medium-to-large data platforms. Using the step-by-step recipes and accompanying theory covered in this book, you'll understand how to integrate various services with Synapse to make it a robust solution for all your data needs. Whether you're new to Azure Synapse or just getting started, you'll find the instructions you need to solve any problem you may face, including using Azure services for data visualization as well as for artificial intelligence (AI) and machine learning (ML) solutions. By the end of this Azure book, you'll have the skills you need to implement an enterprise-grade analytical platform, enabling your organization to explore and manage heterogeneous data workloads and employ various data integration services to solve real-time industry problems.
Table of Contents (11 chapters)
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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...

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