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  • Book Overview & Buying Learn Azure Synapse Data Explorer
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Learn Azure Synapse Data Explorer

Learn Azure Synapse Data Explorer

By : Pericles Rocha
5 (12)
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Learn Azure Synapse Data Explorer

Learn Azure Synapse Data Explorer

5 (12)
By: Pericles Rocha

Overview of this book

Large volumes of data are generated daily from applications, websites, IoT devices, and other free-text, semi-structured data sources. Azure Synapse Data Explorer helps you collect, store, and analyze such data, and work with other analytical engines, such as Apache Spark, to develop advanced data science projects and maximize the value you extract from data. This book offers a comprehensive view of Azure Synapse Data Explorer, exploring not only the core scenarios of Data Explorer but also how it integrates within Azure Synapse. From data ingestion to data visualization and advanced analytics, you’ll learn to take an end-to-end approach to maximize the value of unstructured data and drive powerful insights using data science capabilities. With real-world usage scenarios, you’ll discover how to identify key projects where Azure Synapse Data Explorer can help you achieve your business goals. Throughout the chapters, you'll also find out how to manage big data as part of a software as a service (SaaS) platform, as well as tune, secure, and serve data to end users. By the end of this book, you’ll have mastered the big data life cycle and you'll be able to implement advanced analytical scenarios from raw telemetry and log data.
Table of Contents (19 chapters)
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1
Part 1 Introduction to Azure Synapse Data Explorer
6
Part 2 Working with Data
12
Part 3 Managing Azure Synapse Data Explorer

Summary

In this chapter, you learned why you should care about data export scenarios and how they may be useful for you in real life. You’ve learned how to perform some basic data export work by using Azure Synapse Studio.

You then continued to learn about using server-side exports for a more robust data export experience. We looked at examples of how to export data using a pull approach, where we created a new table that pulls the result from a query to load data into it. Next, you exported data using server-side mechanisms that leverage the .export command to push data to cloud storage, SQL tables, and external tables in Data Explorer pools.

To close, you learned about configuring continuous data export jobs that, once configured, maintain an external table in sync with a source table by running recurrently at a pre-defined interval. We looked at how we can check the details of the job to see its status and how we can disable and enable the job again if needed.

This...

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