Best Hadoop books & Best Hadoop courses in 2024

Best Hadoop Books 2023

Best Hadoop Courses 2023

 

Best Hadoop tutorials 2023

Big Data Hadoop Certification Training

Edureka’s extensive Big Data Analytics certification is curated by Hadoop experts and covers in-depth knowledge of Big Data tools and the Hadoop ecosystem such as HDFS, YARN, MapReduce, Hive and Pig. Throughout this instructor-led Big Data Hadoop certification training, you will work on real-world industry use cases in retail, social media, aviation, tourism, and industry. finance using Edureka’s Cloud Lab. Register now to learn Big Data with instructors with over 10 years of experience, with hands-on demonstrations.

Hadoop is an Apache project (i.e. open source software) for storing and processing Big Data application. Hadoop stores big data in a distributed and fault-tolerant manner on basic hardware. Next, Hadoop tools are used to perform parallel data processing on HDFS (Hadoop Distributed File System). As organizations realize the benefits of Big Data Analytics, there is a huge demand for Big Data and Hadoop professionals. Companies are looking for Big Data and Hadoop experts with knowledge of the Hadoop ecosystem and best practices in HDFS, MapReduce, Spark, HBase, Hive, Pig, Oozie, Sqoop & Flume. Edureka Hadoop training is designed to make you a certified Big Data practitioner by providing you with rich hands-on training on the Hadoop ecosystem. This Hadoop Developer Certification training is a stepping stone to your Big Data journey and you will have the opportunity to work on various Big Data projects.

Big Data Hadoop certification training is designed by industry experts to make you a certified Big Data practitioner. The Big Data Hadoop course offers:
In-depth knowledge of Big Data and Hadoop including HDFS (Hadoop Distributed File System), YARN (Yet Another Resource Negotiator) and MapReduce programming
In-depth knowledge of various tools belonging to the Hadoop ecosystem such as Apache Pig, Hive, Sqoop, Flume, Oozie and HBase
The ability to ingest data into HDFS using Sqoop & Flume, and analyze those large datasets stored in HDFS
Exposure to many real-world industry-based projects that will be executed in Edureka’s CloudLab
Projects of a diverse nature covering diverse datasets from multiple fields such as banking, telecommunications, social media, insurance and e-commerce
Rigorous involvement of a Hadoop expert throughout Big Data Hadoop training to learn industry standards and best practices

This is the best Hadoop Certification Training Course in 2023.

The Ultimate Hands-On Hadoop – Tame your Big Data!

The world of Hadoop and “Big Data” can be daunting – hundreds of different technologies with cryptic names form the Hadoop ecosystem. With this Hadoop tutorial, you will not only understand what these systems are and how they fit together, but you will also learn how to use them to solve real business problems! You will:

You will learn

Design distributed systems that manage “big data” using Hadoop and related technologies.
Use HDFS and MapReduce to store and analyze data at scale.
Use Pig and Spark to create scripts to process data on a Hadoop cluster in a more complex way.
Analyze relational data using Hive and MySQL
Analyze non-relational data using HBase, Cassandra, and MongoDB
Interactively query data with Drill, Phoenix and Presto
Choose the right data storage technology for your application
Understand how Hadoop clusters are managed by YARN, Tez, Mesos, Zookeeper, Zeppelin, Hue, and Oozie.
Publish data to your Hadoop cluster using Kafka, Sqoop, and Flume
Consume streaming data with Spark Streaming, Flink and Storm

Learn Big Data: The Hadoop Ecosystem Masterclass

The course is intended for software engineers, database administrators, and system administrators who want to learn more about Big Data. Other IT professionals may also take this course, but may need to do additional research to understand some of the concepts. You will learn how to use the most popular software in the big data industry today, using batch processing as well as real-time processing. This course will give you enough knowledge to be able to talk about real problems and solutions with industry experts. Updating your LinkedIn profile with these technologies will make recruiters want you to get interviews at the most prestigious companies in the world.

You will learn:

Process Big Data using the batch
Process big data using real-time data
Familiarize yourself with the technologies of the Hadoop stack
Be able to install and configure the Hortonworks Data Platform (HDP)

Taming Big Data with MapReduce and Hadoop – Hands On!

Analyzing “big data” is a sought-after and very valuable skill – and this course will quickly teach you two fundamental technologies of big data: MapReduce and Hadoop. Have you ever wondered how Google manages to constantly crawl the entire Internet? You will learn these same techniques, using your own Windows system at home.

Learn and master the art of framing data analysis problems as MapReduce problems through more than 10 practical examples, then scale them to run on cloud computing services in this course. You will:

Understand how MapReduce can be used to analyze sets of big data
Write your own MapReduce jobs using Python and MRJob
Run MapReduce Jobs on Hadoop Clusters Using Amazon Elastic MapReduce
Chain map: reduce tasks together to analyze more complex problems
Analyze social media data with MapReduce
Analyze movie rating data using MapReduce and generate movie recommendations with it.
Understand other Hadoop based technologies including Hive, Pig, and Spark
Understand what Hadoop is for and how it works

Big Data and Hadoop for Beginners – with Hands-on!

The main objective of this course is to help you understand the complex architectures of Hadoop and its components, to guide you in the right direction to get started, and to quickly start working with Hadoop and its components.

It covers everything you need as a big data newbie. Discover the Big Data market, the different professional roles, technological trends, the history of Hadoop, HDFS, Hadoop Ecosystem, Hive and Pig. In this course, we will see how, as a Hadoop beginner, you should start with Hadoop. This course is accompanied by many practical examples that will help you learn Hadoop quickly.

The course consists of 6 sections and focuses on the following topics:

Big Data at a Glance: Discover Big Data and the different professional roles required in the Big Data market. Learn about Big Data salary trends around the world. Discover the hottest technologies and their trends in the market.

Getting started with Hadoop: Understand Hadoop and its complex architecture. Learn the Hadoop ecosystem with simple examples. Know different versions of Hadoop (Hadoop 1.x vs Hadoop 2.x), different Hadoop vendors on the market and Hadoop on Cloud. Understand how Hadoop framework uses the ELT approach. Learn how to install Hadoop on your machine. We will see running HDFS commands from the command line to manage HDFS.

Getting started with Hive: Understand what kind of problem Hive solves in big data. Learn about its architectural design and how it works. Know the data models in Hive, different file formats supported by Hive, Hive queries, etc. We will see some queries running in Hive.

Getting started with Pig: Find out how Pig solves big data challenges. Learn about its architectural design and how it works. Understand how Pig Latin works in Pig. You will understand the differences between SQL and Pig Latin. Demos on running various queries in Pig.

Use case: Real Hadoop applications are really important to better understand Hadoop and its components, so let’s learn by designing an example data pipeline in Hadoop to process big data. Also understand how companies are adopting a modern data architecture, namely Data Lake, in their data infrastructure.

Practice: Train with huge data sets. Learn design and optimization techniques by designing data models, data pipelines using real application datasets.

You will learn
Understand the different technological trends, salary trends, the Big Data market and the different professional roles in Big Data
Understand what Hadoop is for and how it works
Understand the complex architectures of Hadoop and its components
Installing Hadoop on your machine
Understand how MapReduce, Hive, and Pig can be used to analyze large data sets
High quality documents
Demos: HDFS Command Execution, Hive Queries, Pig Queries
Sample Datasets and Scripts (HDFS Commands, Sample Hive Queries, Sample Pig Queries, Sample Data Pipeline Queries)
Start writing your own code in Hive and Pig to process huge volumes of data
Design your own data pipeline using Pig and Hive
Understand Modern Data Architecture: Data Lake
Practice with big data sets

Best Hadoop books 2023

Hadoop: The Definitive Guide: Storage and Analysis at Internet Scale 4th Edition

by Tom White will teach you everything you need to know about Hadoop. Tom White has been an Apache Hadoop committer since February 2007, and is a member of the Apache Software Foundation. This book will help start you of at the fundamental concepts of Hadoop. It then moves on to Hadoop’s new features and projects. This Hadoop book is well over 700 pages containing Hadoop features and uses. This is easy to understand for beginners and advanced programmers who want to work with Big Data. Systems Administrators will also find great value in this book to setup Hadoop clusters. This is the best Hadoop book in 2023.

Learn basic elements like Mapradius, HDFS and Yarn
Explore the map in depth with the app development steps
Configure and maintain a Hadoop cluster running HDFS and Maps on YARN
Discover two data formats: Avro for serializing data and Perquite for nested data
Use data integration tools like Flume (for data streaming) and Scoop (for bulk data transfer)
Understand how high-level data processing tools like Pig, Hive, Crunch and Spark work with Hadoop
Explore the HBase Distribution Database and Zoo Distribution Configuration Services

Hadoop in 24 Hours, Sams Teach Yourself

by Jeffrey Aven will help you learn how to deploy each important component of a Hadoop platform in your local environment or in the cloud, as well as how to build a fully functional Hadoop cluster and use it with real-world programs and datasets. Each short, simple session builds on the previous ones, allowing you to learn Hadoop’s principles and extend it to meet your specific requirements. Sams Teach Yourself’s Apache Hadoop in 24 Hours covers all of this and more:

Hadoop and the Hadoop Distributed File System: An Overview (HDFS)
Data is imported into Hadoop and processed there.
Using advanced MapReduce API principles and mastering basic MapReduce Java programming
Using Apache Pig and Apache Hive to their full potential
YARN implementation and administration
Making use of the entire Hadoop ecosystem
Apache Ambari for Hadoop Cluster Management
Using Hadoop in the User Environment (HUE)
Hadoop environments: scaling, securing, and troubleshooting
Integrating Hadoop into a business
Hadoop deployment in the cloud
Apache Spark: Getting Started

Step-by-step instructions guide you through frequent problems, concerns, and tasks; Q&As, Quizzes, and Exercises help you gain and test your knowledge; “Did You Know?” hints provide insider information and shortcuts; and “Watch Out!” alerts warn you about potential dangers. By the end of the course, you’ll be able to use Apache Hadoop to handle a wide range of Big Data problems.

Hadoop in Action

by Chuck Lam teaches readers how to use Hadoop and write MapReduce programs. The target readers are programmers, architects, and project managers who have to process large amounts of data offline. Hadoop in Action will take the reader from obtaining a copy of Hadoop to setting it up on a cluster and writing data analysis programs. The book begins by making the basic idea of ​​Hadoop and MapReduce easier to understand by applying the default Hadoop installation to some easy-to-follow tasks, such as analyzing word frequency changes in a body of documents. The book continues with the basics of MapReduce applications developed with Hadoop, including an in-depth look at the components of the framework, using Hadoop for a variety of data analysis tasks, and many examples of Hadoop in action.

Hadoop in Action will explain how to use Hadoop and introduce MapReduce design patterns and programming practices. MapReduce is a complex idea both conceptually and in implementation, and Hadoop users are challenged to learn all the buttons and levers to run hadoop framework. This book takes you beyond the Hadoop execution mechanisms, teaching you how to write meaningful programs in a MapReduce framework. This Hadoop book assumes that the reader will have a basic understanding of Java, as most of the code samples will be written in Java. Knowledge of basic statistical concepts (eg, histogram, correlation) will help the reader to appreciate more advanced data processing examples. You will maintain reliable scalable distributed systems. This is the best Hadoop book for beginners in 2023.

Hadoop in Practice: Includes 104 Techniques 2nd Edition

by Alex Holmes provides more than 100 proven and instantly useful techniques to help you conquer big data using Hadoop. This new revised edition covers changes and new features in the base Hadoop architecture, including MapReduce 2. The new chapters cover YARN and the integration of Kafka, Impala, and Spark SQL with Hadoop. You’ll also get new and updated techniques for Flume, Sqoop, and Mahout, all of which have seen major new releases recently. In short, it’s the most convenient and up-to-date Hadoop coverage available anywhere.

It’s always a good time to improve your Hadoop skills! Hadoop in Practice, Second Edition provides a collection of 104 proven and instantly useful techniques for analyzing flows in real time, moving data safely, automatically learning, managing large-scale clusters, and taming big data with Hadoop. This completely revised edition covers changes and new features in the Hadoop core, including MapReduce 2 and YARN. You will learn about best practices for integrating Spark, Kafka, and Impala with Hadoop, and you will get new and updated techniques for the latest versions of Flume, Sqoop, and Mahout. In short, it is the most practical and up-to-date Hadoop coverage available. Readers should know a programming language such as Java and have a basic understanding of Hadoop. This book contains:

Fully updated for Hadoop 2
How to write YARN applications
Integrate real-time technologies like Storm, Impala and Spark
Predictive analysis using Mahout and RR
Readers should know a programming language such as Java and have a basic understanding of Hadoop.

Expert Hadoop Administration: Managing, Tuning, and Securing Spark, YARN, and HDFS (Addison-Wesley Data & Analytics Series)

Hadoop Chief Administrator Sam R. Alapati brings together the knowledge of approval for creating, configuring, securing, managing, and optimizing clusters of Hadoop production in any environment. The interaction combines action-based counseling with carefully studied explanations of problems and solutions to draw on his experience with large-scale Hadoop administration. It covers an unrivaled range of topics and provides an unrivaled collection of practical examples.

You will:
Understand Hadoop architecture from an administrator’s perspective
Create simple, complete distribution clusters
Run Mapredius and Spark applications in a Hadoop cluster
Manage and secure Hadoop data and high availability
Work with HDFS commands, file permissions and storage management
Remove data and use YARN to allocate resources and work schedules
Manage workflow with Oz and Hugh
Protect, monitor, record and customize Hadoop
Hadoop benchmarking and problem solving

Complete and up-to-date Apache Hadoop Administration and Reference Manual.

Mastering Hadoop 3: Big data processing at scale to unlock unique business insights

by Chanchal Singh, Manish Kumar is a complete guide to mastering the most advanced Hadoop 3 concepts. Discover new Hadoop features and capabilities. Hadoop 3 Compress and process data using the tools of a few hosts within the Map Mapredeus, Yarn and Hadoop ecosystems. Improve your Hadoop skills with case studies and real-world code Apache Hadoop is one of the most popular solutions among the big data solutions for distributed storage and large amount of data processing. With Hadoop 3, Apache promises to provide a high-performance, more error-tolerant and highly efficient large data processing platform with a focus on improved scalability and enhanced efficiency. You will:

Gain a deeper understanding of distributed computing using Hadoop 3
Develop enterprise-level applications using Apache Spark, Flink, etc.
Create scalable, high-performance Hadoop data pipelines with data protection, monitoring and administration
Find batch data processing models and how to model data in Hadoop
Maximum practice for companies planning or planning to use Hadoop 3 as a data platform
Understand the security aspects of Hadoop, including approval and authentication

Data Analytics with Hadoop: An Introduction for Data Scientists

by Benjamin Bengfort and Jenny Kim is a practical guide shows you why the Hadoop ecosystem is perfect for the job. Ready to use statistics and machine learning techniques on big data sets? This practical Hadoop guide shows you why the Hadoop ecosystem is suitable for work. Instead of deploying, operating, or developing software that is typically related to distributed computing, focus on the data warehousing strategies and data workflows provided by Hadoop that can create this structure that can produce higher orders.

Understand the basic concepts behind Hadoop and cluster computing
Use parallel design models and analytic algorithms to create distributed data analysis work
Discover data management, search, and storage for distribution using Apache Hive and HBS
Use Scoop and Apache Flume to inject data from relational databases
Program complex Hadoop application and spark application with Apache Pig and Spark data frames
Implement machine learning techniques such as classification, clustering, and collaborative filtering with MLBs from Spark

Hadoop Real-World Solutions Cookbook – Second Edition

by Tanmay Deshpande gives readers an overview of learning and mastering big data through recipes. The book not only clarifies most of the big data tools on the market, but also provides best practices for using them. The book provides recipes based on the latest versions of Apache Hadoop 2.X, YARN, Hive, Pig, Sqoop, Flume, Apache Spark, Mahout, and many other ecosystem tools. This real-world solutions cookbook is packed with practical recipes that you can apply to your own everyday problems. Each chapter provides detailed recipes that can be easily referenced. This book provides detailed practices on the latest technologies, such as YARN and Apache Spark. Readers can consider themselves big data experts at the end of this book. You will:

Installation and maintenance of the Hadoop 2.X cluster and its ecosystem.
Write advanced card reduction programs and understand design patterns.
Advanced data analysis using Hive, Pig and Map Reduce programs.
Import and export data from various sources using Sqoop and Flume.
Data storage in various file formats such as text, sequential, parquet, ORC and RC files.
Principles of machine learning with libraries like Mahout
Batch and Stream Data Processing with Apache Spark

Popular