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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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Understanding data migration challenges

In this section, we will cover the data migration challenges that we face while migrating an on-premises data warehouse to the cloud, the ways of mitigating these challenges, and the successful migration journey to Synapse using Azure Synapse Pathway.

Data warehouses are usually used to store large amounts of data for analytical purposes. The data is ingested from a variety of sources and transactional systems into this data warehouse store, where raw data is transformed based on the schema and stored for future analytical purposes to derive business insights. Over a period of time, the data becomes overwhelming, such that the scalability becomes bottlenecked in an on-premises store, which is one of the compelling reasons for most organizations to adopt cloud data warehouses.

Let's take an example of an on-premises Teradata Data Warehouse where the customer intends to move it to Azure Synapse. There are significant design differences...

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