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Data Engineering with Databricks Cookbook

You're reading from Data Engineering with Databricks Cookbook Build effective data and AI solutions using Apache Spark, Databricks, and Delta Lake

Product type Paperback

Published in May 2024

Publisher Packt

ISBN-13 9781837633357

Length 438 pages

Edition 1st Edition

Tools

Apache Spark

Concepts

Data Engineering

Author (1):

Pulkit Chadha

View More author details

Table of Contents (16) Chapters

Preface

1. Part 1 – Working with Apache Spark and Delta Lake FREE CHAPTER

2. Chapter 1: Data Ingestion and Data Extraction with Apache Spark

3. Chapter 2: Data Transformation and Data Manipulation with Apache Spark

4. Chapter 3: Data Management with Delta Lake

5. Chapter 4: Ingesting Streaming Data

6. Chapter 5: Processing Streaming Data

7. Chapter 6: Performance Tuning with Apache Spark

8. Chapter 7: Performance Tuning in Delta Lake

9. Part 2 – Data Engineering Capabilities within Databricks

10. Chapter 8: Orchestration and Scheduling Data Pipeline with Databricks Workflows

11. Chapter 9: Building Data Pipelines with Delta Live Tables

12. Chapter 10: Data Governance with Unity Catalog

13. Chapter 11: Implementing DataOps and DevOps on Databricks

14. Index

Why subscribe?

15. Other Books You May Enjoy

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

A

adaptive query execution (AQE) 216, 217

aggregations, with Apache Spark

approximate functions 51-53

performing 48-50

pivot tables 50

alerts and notifications

setting up, for workflows 272-274

Amazon Simple Storage Service (Amazon S3) 139

Apache Spark 139, 177

considerations, for caching and persistence 207

CSV data, reading with 4-8

custom UDFs, writing in 57-59

data, writing with 31, 32

documentation link 69

null values, handling with 60-62

text data, processing in 26-28

used, for parsing XML data 17-20

working, with nested data structures in 20-22

Apache Spark Structured Streaming 139

checkpoints, configuring for 115-120

configuring, for real-time data processing 100-105

custom sources and sinks 105

data, reading from real-time...

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Authors (1)

Pulkit Chadha

Pulkit Chadha

Pulkit Chadha is a Sr. Solutions Architect at Databricks. He has over 12 years of experience working in Data Engineering. With his expertise in building and optimizing data pipelines using various tools and technologies Pulkit has worked with enterprises in various industries, including healthcare, Media and Entertainment, Hi-Tech, and Manufacturing providing data engineering solutions to meet enterprises' unique business needs. His work history includes the likes of Dell Services, Adobe, and Databricks. Pulkit holds a Masters's Degree in Management Information Systems from Eller College of Management at the University Of Arizona and has several cloud certifications in data analytics.

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