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Distributed Data Systems with Azure Databricks

You're reading from Distributed Data Systems with Azure Databricks

Product type Book

Published in May 2021

Publisher Packt

ISBN-13 9781838647216

Pages 414 pages

Edition 1st Edition

Languages

Concepts

Author (1):

Alan Bernardo Palacio

Table of Contents (17) Chapters

Preface

Section 1: Introducing Databricks

Chapter 1: Introduction to Azure Databricks

Chapter 2: Creating an Azure Databricks Workspace

Section 2: Data Pipelines with Databricks

Chapter 3: Creating ETL Operations with Azure Databricks

Chapter 4: Delta Lake with Azure Databricks

Chapter 5: Introducing Delta Engine

Chapter 6: Introducing Structured Streaming

Section 3: Machine and Deep Learning with Databricks

Chapter 7: Using Python Libraries in Azure Databricks

Chapter 8: Databricks Runtime for Machine Learning

Chapter 9: Databricks Runtime for Deep Learning

Chapter 10: Model Tracking and Tuning in Azure Databricks

Chapter 11: Managing and Serving Models with MLflow and MLeap

Chapter 12: Distributed Deep Learning in Azure Databricks

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Ingesting data using Delta Lake

Data can be ingested into Delta Lake in several ways. Azure Databricks offers several integrations with Partners, which provide data sources that are loaded as Delta tables. We can copy a file directly into a table, use AutoLoader, or create a new streaming table. Let's take a deeper look at this.

Partner integrations

Azure Databricks allows us to connect to different partners that provide data sources. These are easy to implement and provide scalable ingestion.

We can view the options that we have for ingesting data from Partner Integrations when creating a new table in the UI, as shown in the following screenshot:

Figure 4.1 – Ingesting data from Partner Integrations

Some of these integrations, such as Qlink, allow you to get data from multiple data sources such as Oracle, Microsoft SQL Server, and SAP and load them into Delta Lake. Of course, these integrations require you to have a subscription to...

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Authors (1)

Alan Bernardo Palacio

Alan Bernardo Palacio is a data scientist and an engineer with vast experience in different engineering fields. His focus has been the development and application of state-of-the-art data products and algorithms in several industries. He has worked for companies such as Ernst and Young, Globant, and now holds a data engineer position at Ebiquity Media helping the company to create a scalable data pipeline. Alan graduated with a Mechanical Engineering degree from the National University of Tucuman in 2015, participated as the founder in startups, and later on earned a Master's degree from the faculty of Mathematics in the Autonomous University of Barcelona in 2017. Originally from Argentina, he now works and resides in the Netherlands.

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