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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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Migrating data from Google BigQuery to Azure Synapse

In this recipe, we will import a public dataset, github_repo.files, from Google BigQuery into Azure Synapse – formerly Azure Data Warehouse. We will create a SQL data pool, create the table to store our imported data, and configure the pipeline to migrate data from a public dataset hosted at Google BigQuery.

Getting ready

To complete this recipe, you will need a Google Cloud project with the BigQuery API enabled. Refer to the Getting ready section in the previous recipe for instructions on how to set those up and obtain your Project ID, Client ID, Client Secret, and Refresh Token fields.

You will also need an instance of an Azure Synapse SQL pool to import the data. Refer to the chapter on Azure Synapse on how to create and configure a SQL pool. Have the login credentials for this SQL pool to hand.

You will also need to create a table in your database to store the data we import. Download the script to create...

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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.

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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.

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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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