Are you looking for the best books on Apache Spark?… If yes, then this article is for you. In this article, you will find the Best Apache Spark Books for Beginners & advanced like Beginner courses, and Practice test courses. So, check these Best Apache Spark Books for Beginners and find the Best Apache Spark Books for Beginners to Advanced according to your need.
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Best Apache Spark Books for Beginners to Advanced to know in 2022
High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark
Apache Spark is amazing when everything clicks. But if you haven’t seen the performance improvements you expected, or still don’t feel confident enough to use Spark in production, this practical book is for you. Authors Holden Karau and Rachel Warren demonstrate performance optimizations to help your Spark queries run faster and handle larger data sizes, while using fewer resources.
Ideal for software engineers, data engineers, developers, and system administrators working with large-scale data applications, this book describes techniques that can reduce data infrastructure costs and developer hours. Not only will you gain a more comprehensive understanding of Spark, you’ll also learn how to make it sing.
- How Spark SQL’s new interfaces improve performance over SQL’s RDD data structure
- The choice between data joins in Core Spark and Spark SQL
- Techniques for getting the most out of standard RDD transformations
- How to work around performance issues in Spark’s key/value pair paradigm
- Writing high-performance Spark code without Scala or the JVM
- How to test for functionality and performance when applying suggested improvements
- Using Spark MLlib and Spark ML machine learning libraries
- Spark’s Streaming components and external community packages
Data Engineering with Apache Spark, Delta Lake, and Lakehouse: Create scalable pipelines that ingest, curate, and aggregate complex data in a timely and secure way
In the world of ever-changing data and schemas, it is important to build data pipelines that can auto-adjust to changes. This book will help you build scalable data platforms that managers, data scientists, and data analysts can rely on.
Starting with an introduction to data engineering, along with its key concepts and architectures, this book will show you how to use Microsoft Azure Cloud services effectively for data engineering. You’ll cover data lake design patterns and the different stages through which the data needs to flow in a typical data lake.
Once you’ve explored the main features of Delta Lake to build data lakes with fast performance and governance in mind, you’ll advance to implementing the lambda architecture using Delta Lake. Packed with practical examples and code snippets, this book takes you through real-world examples based on production scenarios faced by the author in his 10 years of experience working with big data.
Finally, you’ll cover data lake deployment strategies that play an important role in provisioning the cloud resources and deploying the data pipelines in a repeatable and continuous way.
By the end of this data engineering book, you’ll know how to effectively deal with ever-changing data and create scalable data pipelines to streamline data science, ML, and artificial intelligence (AI) tasks.
- Discover the challenges you may face in the data engineering world
- Add ACID transactions to Apache Spark using Delta Lake
- Understand effective design strategies to build enterprise-grade data lakes
- Explore architectural and design patterns for building efficient data ingestion pipelines
- Orchestrate a data pipeline for preprocessing data using Apache Spark and Delta Lake APIs
- Automate deployment and monitoring of data pipelines in production
- Get to grips with securing, monitoring, and managing data pipelines models efficiently
Spark in Action, with Examples in Java, Python, and Scala
The Spark distributed data processing platform provides an easy-to-implement tool for ingesting, streaming, and processing data from any source. In Spark in Action, you’ll learn to take advantage of Spark’s core features and incredible processing speed, with applications including real-time computation, delayed evaluation, and machine learning. Spark skills are a hot commodity in enterprises worldwide, and with Spark’s powerful and flexible Java APIs, you can reap all the benefits without first learning Scala or Hadoop.
teaches you to create end-to-end analytics applications. In this entirely new book, you’ll learn from interesting Java-based examples, including a complete data pipeline for processing NASA satellite data. And you’ll discover Java, Python, and Scala code samples hosted on GitHub that you can explore and adapt, plus appendixes that give you a cheat sheet for installing tools and understanding Spark-specific terms.
Stream Processing with Apache Spark: Mastering Structured Streaming and Spark Streaming
Before you can build analytics tools to gain quick insights, you first need to know how to process data in real time. With this practical guide, developers familiar with Apache Spark will learn how to put this in-memory framework to use for streaming data. You’ll discover how Spark enables you to write streaming jobs in almost the same way you write batch jobs.
Authors Gerard Maas and François Garillot help you explore the theoretical underpinnings of Apache Spark. This comprehensive guide features two sections that compare and contrast the streaming APIs Spark now supports: the original Spark Streaming library and the newer Structured Streaming API.
- Learn fundamental stream processing concepts and examine different streaming architectures
- Explore Structured Streaming through practical examples; learn different aspects of stream processing in detail
- Create and operate streaming jobs and applications with Spark Streaming; integrate Spark Streaming with other Spark APIs
- Learn advanced Spark Streaming techniques, including approximation algorithms and machine learning algorithms
- Compare Apache Spark to other stream processing projects, including Apache Storm, Apache Flink, and Apache Kafka Streams
Beginning Apache Spark Using Azure Databricks: Unleashing Large Cluster Analytics in the Cloud
Analyze vast amounts of data in record time using Apache Spark with Databricks in the Cloud. Learn the fundamentals, and more, of running analytics on large clusters in Azure and AWS, using Apache Spark with Databricks on top. Discover how to squeeze the most value out of your data at a mere fraction of what classical analytics solutions cost, while at the same time getting the results you need, incrementally faster.
This book explains how the confluence of these pivotal technologies gives you enormous power, and cheaply, when it comes to huge datasets. You will begin by learning how cloud infrastructure makes it possible to scale your code to large amounts of processing units, without having to pay for the machinery in advance.
From there you will learn how Apache Spark, an open source framework, can enable all those CPUs for data analytics use. Finally, you will see how services such as Databricks provide the power of Apache Spark, without you having to know anything about configuring hardware or software.
By removing the need for expensive experts and hardware, your resources can instead be allocated to actually finding business value in the data.
This book guides you through some advanced topics such as analytics in the cloud, data lakes, data ingestion, architecture, machine learning, and tools, including Apache Spark, Apache Hadoop, Apache Hive, Python, and SQL. Valuable exercises help reinforce what you have learned.
- Discover the value of big data analytics that leverage the power of the cloud
- Get started with Databricks using SQL and Python in either Microsoft Azure or AWS
- Understand the underlying technology, and how the cloud and Apache Spark fit into the bigger picture
- See how these tools are used in the real world
- Run basic analytics, including machine learning, on billions of rows at a fraction of a cost or free
Azure Databricks Cookbook: Accelerate and scale real-time analytics solutions using the Apache Spark-based analytics service
The book starts by teaching you how to create an Azure Databricks instance within the Azure portal, Azure CLI, and ARM templates. You’ll work through clusters in Databricks and explore recipes for ingesting data from sources, including files, databases, and streaming sources such as Apache Kafka and EventHub.
The book will help you explore all the features supported by Azure Databricks for building powerful end-to-end data pipelines. You’ll also find out how to build a modern data warehouse by using Delta tables and Azure Synapse Analytics.
Later, you’ll learn how to write ad hoc queries and extract meaningful insights from the data lake by creating visualizations and dashboards with Databricks SQL. Finally, you’ll deploy and productionize a data pipeline as well as deploy notebooks and Azure Databricks service using continuous integration and continuous delivery (CI/CD).
By the end of this Azure book, you’ll be able to use Azure Databricks to streamline different processes involved in building data-driven apps.
- Read and write data from and to various Azure resources and file formats
- Build a modern data warehouse with Delta Tables and Azure Synapse Analytics
- Explore jobs, stages, and tasks and see how Spark lazy evaluation works
- Handle concurrent transactions and learn performance optimization in Delta tables
- Learn Databricks SQL and create real-time dashboards in Databricks SQL
- Integrate Azure DevOps for version control, deploying, and productionizing solutions with CI/CD pipelines
- Discover how to use RBAC and ACLs to restrict data access
- Build end-to-end data processing pipeline for near real-time data analytics
Mastering Machine Learning on AWS: Advanced machine learning in Python using SageMaker, Apache Spark, and TensorFlow
AWS is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This book is your comprehensive reference for learning and implementing advanced ML algorithms in AWS cloud.
As you go through the chapters, you’ll gain insights into how these algorithms can be trained, tuned and deployed in AWS using Apache Spark on Elastic Map Reduce (EMR), SageMaker, and TensorFlow.
While you focus on algorithms such as XGBoost, linear models, factorization machines, and deep nets, the book will also provide you with an overview of AWS as well as detailed practical applications that will help you solve real-world problems.
Every practical application includes a series of companion notebooks with all the necessary code to run on AWS. In the next few chapters, you will learn to use SageMaker and EMR Notebooks to perform a range of tasks, right from smart analytics, and predictive modeling, through to sentiment analysis.
- Manage AI workflows by using AWS cloud to deploy services that feed smart data products
- Use SageMaker services to create recommendation models
- Scale model training and deployment using Apache Spark on EMR
- Understand how to cluster big data through EMR and seamlessly integrate it with SageMaker
- Build deep learning models on AWS using TensorFlow and deploy them as services
- Enhance your apps by combining Apache Spark and Amazon SageMaker
And here the list ends. So, these are the Best Apache Spark Books for Beginners to Advanced. I will keep adding more Best Apache Spark Books for Beginners to advance to this list.
I hope these Best Apache Spark Books for Beginners to Advanced will definitely help you to enhance your skills. If you have any doubts or questions, feel free to ask me in the comment section.