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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

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Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781837633357
Length 438 pages
Edition 1st Edition
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Author (1):
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Pulkit Chadha Pulkit Chadha
Author Profile Icon Pulkit Chadha
Pulkit Chadha
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Toc

Table of Contents (16) Chapters Close

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 15. Other Books You May Enjoy

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “Import the required libraries and create a SparkSession object.”

A block of code is set as follows:

from pyspark.sql import SparkSession
spark = (SparkSession.builder
    .appName("read-csv-data")
    .master(«spark://spark-master:7077»)
    .config(«spark.executor.memory", "512m")
    .getOrCreate())
spark.sparkContext.setLogLevel("ERROR")

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

builder = (SparkSession.builder
               .appName("optimize-delta-table")
               .master("spark://spark-master:7077")
               .config("spark.executor.memory", "512m")

Any command-line input or output is written as follows:

$ docker-compose stop

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: “The Spark UI also has a dedicated Structured Streaming tab, which shows detailed information about your streaming queries, such as input rate, processing rate, latency, state size, and event timeline.”

Tips or important notes

Appear like this.

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