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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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Copying data from Google BigQuery to Azure Data Lake Store

In this recipe, we will use Azure Data Factory to import a subset of a public fdic_banks.locations dataset from the Google BigQuery service (a cloud data warehouse) into an Azure Data Lake store. We will write the data into destination storage in Parquet format for convenience.

Getting ready

For this recipe, we assume that you have a Google Cloud account and a project, as well as an Azure account and a Data Lake storage account (ADLS Gen2). The following is a list of additional preparatory work:

You need to enable the BigQuery API for your Google Cloud project. You can enable this API here: https://console.developers.google.com/apis/api/bigquery.googleapis.com/overview.
You will require information for the Project ID, Client ID, Client Secret, and Refresh Token fields for the BigQuery API app. If you are not familiar on how to set up a Google Cloud app and obtain these tokens, you can find detailed instructions...

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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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