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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

1. Section 1: Introducing Databricks

2. Chapter 1: Introduction to Azure Databricks

3. Chapter 2: Creating an Azure Databricks Workspace

4. Section 2: Data Pipelines with Databricks

5. Chapter 3: Creating ETL Operations with Azure Databricks

6. Chapter 4: Delta Lake with Azure Databricks

7. Chapter 5: Introducing Delta Engine

8. Chapter 6: Introducing Structured Streaming

9. Section 3: Machine and Deep Learning with Databricks

10. Chapter 7: Using Python Libraries in Azure Databricks

11. Chapter 8: Databricks Runtime for Machine Learning

12. Chapter 9: Databricks Runtime for Deep Learning

13. Chapter 10: Model Tracking and Tuning in Azure Databricks

14. Chapter 11: Managing and Serving Models with MLflow and MLeap

15. Chapter 12: Distributed Deep Learning in Azure Databricks

16. Other Books You May Enjoy

PySpark API

We have been using the PySpark API across all sections when describing the features of Azure Databricks without discussing too much of its functionalities and how we can leverage them to make reliable ETL operations when working with big data. PySpark is the Python API for Apache Spark, a cluster-computing framework that is the heart of Azure Databricks.

Main functionalities of PySpark

PySpark allows you to harness the power of distributed computing with the ease of use of Python and it's the default way in which we express our computations through this book unless stated otherwise.

The fundamentals of PySpark lies in the functionality of its sub-packages of which the most central are the following:

PySpark DataFrames: Data stored in rows following a set of named columns. These DataFrames are immutable and allow us to perform lazy computations.
The PySpark SQL module: A higher-abstraction module for processing structured and semi-structured datasets...

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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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