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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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Achieving parallelism in data loading using PolyBase

PolyBase is the one of best data loading methods when it comes to performance after the COPY Into command. So, it's recommended to use PolyBase where it's supported or possible when it comes to parallelism.

How it achieves parallelism is similar to what a parallel data warehouse system does. There will be a control node, followed by multiple compute nodes, which are then linked to multiple data nodes. The data nodes will consist of the actual data, which will get the process by the compute nodes in parallel, and all these operations and instructions are governed by the control node.

In the following architecture diagrams, each Hadoop Distributed File System (HDFS) bridge of the service from every compute node can connect to an external resource, such as Azure Blob storage, and then bidirectionally transfer data between a SQL data warehouse and the external resource. This is fully scalable and highly robust: as you...

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