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ETL with Azure Cookbook

You're reading from ETL with Azure Cookbook Practical recipes for building modern ETL solutions to load and transform data from any source

Product type Paperback

Published in Sep 2020

Publisher Packt

ISBN-13 9781800203310

Length 446 pages

Edition 1st Edition

Languages

Python

Tools

Azure

Concepts

Databases

Authors (3):

Christian Cote

Matija Lah

Madina Saitakhmetova

View More author details

Table of Contents (12) Chapters

Preface

1. Chapter 1: Getting Started with Azure and SSIS 2019

2. Chapter 2: Introducing ETL FREE CHAPTER

3. Chapter 3: Creating and Using SQL Server 2019 Big Data Clusters

4. Chapter 4: Azure Data Integration

5. Chapter 5: Extending SSIS with Custom Tasks and Transformations

6. Chapter 6: Azure Data Factory

7. Chapter 7: Azure Databricks

8. Chapter 8: SSIS Migration Strategies

9. Chapter 9: Profiling data in Azure

10. Chapter 10: Manage SSIS and Azure Data Factory with Biml

11. Other Books You May Enjoy

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Transforming data with Hive

Now that we have some data copied to Azure, we'll transform it using a big data language called Hive. Hive is known by the big data community as being the data warehouse language in the cloud. It's essentially SQL, except we use it to transform data.

Getting ready

This recipe requires that you have the following:

Visual Studio 2019 with the Integration Services extension installed.
Azure Feature Pack installed.
Java Runtime Engine installed.
Access to an Azure subscription.
An on-demand HDInsight cluster task in your package. Make sure you've completed the Creating an on-demand Azure HDInsight cluster recipe in this chapter.

How to do it…

Now, it's time to copy some data into a container in our storage account. This data will be used to make some transformations using Apache Hive. Let's get started:

From the SSIS toolbox, drag and drop a dataflow task. Rename it DFT_Sales.

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Authors (3)

Cote

Cote

Christian Cote is an IT professional with more than 15 years of experience working in a data warehouse, Big Data, and business intelligence projects. Christian developed expertise in data warehousing and data lakes over the years and designed many ETL/BI processes using a range of tools on multiple platforms. He's been presenting at several conferences and code camps. He currently co-leads the SQL Server PASS chapter. He is also a Microsoft Data Platform Most Valuable Professional (MVP).

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Saitakhmetova

Saitakhmetova

Madina Saitakhmetova is a developer specializing in BI. She has been in IT for 15 years, working first with Microsoft SQL, .Net Framework, and then Microsoft BI, BIML and Azure. Her adventure with Microsoft BI began with Analysis Services and SSIS, but she is leaning towards ETL development, both on premises and in the cloud, in later years. Finding patterns, automating processes and making BI team work more efficient are challenges that drive her. During past few years, BIML has become an important part of her work, increasing efficiency and quality

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Lah

Lah

Matija Lah has more than 18 years of experience working with Microsoft SQL Server, mostly from architecting data-centric solutions in the legal domain. His contributions to the SQL Server community have led to him being awarded the MVP Professional award (Data Platform) between 2007 and 2017/2018. He spends most of his time on projects involving advanced information management and natural language processing, but often finds time to speak at events related to Microsoft SQL Server where he loves to share his experience with the SQL Server platform.

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