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Distributed Data Systems with Azure Databricks

You're reading from Distributed Data Systems with Azure Databricks Create, deploy, and manage enterprise data pipelines

Product type Paperback

Published in May 2021

Publisher Packt

ISBN-13 9781838647216

Length 414 pages

Edition 1st Edition

Languages

Python

Tools

Azure

Concepts

Data Science

Author (1):

Alan Bernardo Palacio

View More author details

Table of Contents (17) Chapters

Preface

1. Section 1: Introducing Databricks

2. Chapter 1: Introduction to Azure Databricks FREE CHAPTER

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

Summary

In this chapter, we have applied the concepts learned previously to create and connect to Azure Databricks resources to store our data, such as ADLS Gen2, AWS S3, and Azure Blob storage. We have also learned how to ingest data from the storage to a Spark dataframe, transform it using Spark methods or using SQL scripts, and then persist the data into tables. Finally, we have seen how to schedule our jobs with Azure Databricks jobs, and how to create a pipeline in ADF and trigger it using a defined variable.

In the next chapter, we will learn about Azure Delta Lake and how to use it to create reliable ETL pipelines using Azure Databricks.

The rest of the chapter is locked

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Authors (1)

Palacio

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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