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Azure Data Factory Cookbook

You're reading from Azure Data Factory Cookbook

Product type Book

Published in Dec 2020

Publisher Packt

ISBN-13 9781800565296

Pages 382 pages

Edition 1st Edition

Languages

Concepts

Authors (4):

Dmitry Anoshin

Dmitry Foshin

Roman Storchak

Xenia Ireton

View More author details

Table of Contents (12) Chapters

Preface

1. Chapter 1: Getting Started with ADF

2. Chapter 2: Orchestration and Control Flow

3. Chapter 3: Setting Up a Cloud Data Warehouse

4. Chapter 4: Working with Azure Data Lake

5. Chapter 5: Working with Big Data – HDInsight and Databricks

6. Chapter 6: Integration with MS SSIS

7. Chapter 7: Data Migration – Azure Data Factory and Other Cloud Services

8. Chapter 8: Working with Azure Services Integration

9. Chapter 9: Managing Deployment Processes with Azure DevOps

10. Chapter 10: Monitoring and Troubleshooting Data Pipelines

11. Other Books You May Enjoy

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Using SQL on-demand

In this recipe, you will learn how to use SQL on-demand in an Azure Synapse workspace.

Getting ready

You need to have an Azure Synapse workspace created and the file in Parquet format kept in your Azure Synapse storage account.

How to do it…

Open the Azure Synapse workspace, go to Data, and open the folder that contents the Parquet format file.
Right-click on the file and choose New SQL script | Select TOP 100 rows:
Figure 3.37 – Creating a new SQL script for a file in a storage account
A new script is created for connecting to the file using SQL on-demand:
Figure 3.38 – Connecting to the file from the Synapse workspace using SQL on-demand
Note
The query executes within several seconds; you don't need to wait several minutes for the cluster to start. You can copy this script and then paste it into SSMS.
You can also use SQL on-demand to connect from SSMS or a visualization tool (such as Power BI or Tableau). For this...

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Authors (4)

Dmitry Anoshin

Dmitry Anoshin is a data-centric technologist and a recognized expert in building and implementing big data and analytics solutions. He has a successful track record when it comes to implementing business and digital intelligence projects in numerous industries, including retail, finance, marketing, and e-commerce. Dmitry possesses in-depth knowledge of digital/business intelligence, ETL, data warehousing, and big data technologies. He has extensive experience in the data integration process and is proficient in using various data warehousing methodologies. Dmitry has constantly exceeded project expectations when he has worked in the financial, machine tool, and retail industries. He has completed a number of multinational full BI/DI solution life cycle implementation projects. With expertise in data modeling, Dmitry also has a background and business experience in multiple relation databases, OLAP systems, and NoSQL databases. He is also an active speaker at data conferences and helps people to adopt cloud analytics.

See other products by Dmitry Anoshin

Dmitry Foshin

Dmitry Foshin is a business intelligence team leader, whose main goals are delivering business insights to the management team through data engineering, analytics, and visualization. He has led and executed complex full-stack BI solutions (from ETL processes to building DWH and reporting) using Azure technologies, Data Lake, Data Factory, Data Bricks, MS Office 365, PowerBI, and Tableau. He has also successfully launched numerous data analytics projects – both on-premises and cloud – that help achieve corporate goals in international FMCG companies, banking, and manufacturing industries.

See other products by Dmitry Foshin

Roman Storchak

Roman Storchak is a PhD, and is a chief data officer whose main interest lies in building data-driven cultures through making analytics easy. He has led teams that have built ETL-heavy products in AdTech and retail and often uses Azure Stack, PowerBI, and Data Factory.

See other products by Roman Storchak

Xenia Ireton

Xenia Ireton is a Senior Software Engineer at Microsoft. She has extensive knowledge in building distributed services, data pipelines and data warehouses.

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