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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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Transforming and cleaning data

After our data has been loaded into a Spark dataframe, we can manipulate it in different ways. We can directly manipulate our Spark dataframe or save the data to a table, and use Structured Query Language (SQL) statements to perform queries, data definition language (DDL), data manipulation language (DML), and more.

You will need to have the Voting_Turnout_US_2020 dataset loaded into a Spark dataframe.

Spark data frames

A Spark data frame is a tabular collection of data organized in rows with named columns, which in turn have their own data types. All this information is stored as metadata that we can access using displaySchema in order to display the data types of each column or display the actual data, or describe in order to view the statistical summary of the data. One of its characteristics is that it is able to handle big amounts of data thanks to its distributed nature.

We can perform transformations such as selecting rows and columns...

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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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