Learn Hadoop 2020 – Best Hadoop courses & Best Hadoop tutorials & Best Hadoop books

Best Hadoop Courses 2020


Best Hadoop tutorials 2020

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!

Learn and master the most popular big data technologies in this comprehensive course, taught by a former engineer and senior manager from Amazon and IMDb. We’ll go way beyond Hadoop itself and dive into all kinds of distributed systems that you may need to integrate with.

Install and work with a true Hadoop installation right on your desktop with Hortonworks (now part of Cloudera) and Ambari user interface

Manage Big Data on a Cluster with HDFS and MapReduce

Write programs to analyze data on Hadoop with Pig and Spark

Store and query your data with Sqoop, Hive, MySQL, HBase, Cassandra, MongoDB, Drill, Phoenix and Presto

Design real systems using the Hadoop ecosystem

Find out how your cluster is managed with YARN, Mesos, Zookeeper, Oozie, Zeppelin and Hue

Manage streaming data in real time with Kafka, Flume, Spark Streaming, Flink and Storm

Understanding Hadoop is a very valuable skill for anyone working in companies with large amounts of data.

Almost every big company you want to work for uses Hadoop in some way, including Amazon, Ebay, Facebook, Google, LinkedIn, IBM, Spotify, Twitter, and Yahoo! And it’s not just tech companies that need Hadoop; even the New York Times uses Hadoop to process the images.

This course is comprehensive, covering over 25 different technologies in addition to 14 hours of video lectures. It’s packed with hands-on activities and exercises, so you can get real experience using Hadoop – it’s not just theory.

You will find a range of activities in this course for people of all skill levels. If you are a project manager and just want to learn the buzzwords, there are web UIs for many of the course activities that do not require any programming knowledge. If you’re comfortable with command lines, we’ll show you how to use them as well. And if you are a programmer, I challenge you to write real scripts on a Hadoop system using Scala, Pig Latin and Python.

You will leave this course with a real and in-depth understanding of Hadoop and its associated distributed systems, and you will be able to apply Hadoop to real-world problems. Plus, a precious certificate of completion awaits you at the end!

Please note that this course focuses on application development, not Hadoop administration. Although you gain administrative skills along the way.

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

In this course, you will learn about Big Data using the Hadoop ecosystem. Why Hadoop? It is one of the most sought-after skills in the IT industry. The average salary in the United States is $ 112,000 per year, up to an average of $ 160,000 in San Francisco (source: Indeed).

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.

The course is very practical, with more than 6 hours of lessons. You want to try everything yourself, adding several hours of learning. If you get stuck with the tech trying, support is available. I will respond to your messages on the message boards and we have a Facebook group where you can post questions.

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 learn from a former engineer and senior manager at Amazon and IMDb.

Learn the concepts of MapReduce
Quickly run MapReduce jobs using Python and MRJob
Translate complex analysis problems into multi-step MapReduce tasks
Upgrade to Larger Data Sets Using Amazon’s Elastic MapReduce Service
Understand how Hadoop distributes MapReduce on compute clusters
Discover other Hadoop technologies, such as Hive, Pig and Spark
By the end of this course, you’ll be running code that analyzes gigabytes of information – in the cloud – in minutes.

We’re going to have fun along the way. You’ll warm up with some easy examples of using MapReduce to analyze movie rating data and book text. Once you have the basics under your belt, we’ll move on to more complex and interesting tasks. We’ll use a million movie ratings to find movies that look alike, and you might even discover new movies that you might like in the process! We’ll analyze a social graph of superheroes, and find out who is the most “popular” superhero – and develop a system to find “degrees of separation” between superheroes. Are all Marvel superheroes within a few degrees of being connected to The Incredible Hulk? You will find the answer.

This course is very practical; you will spend most of your time following up with the instructor as we write, analyze, and run real code together – both on your own system and in the cloud using Amazon’s Elastic MapReduce service. Over 5 hours of video content is included, with over 10 real life examples of increasing complexity that you can create, run and study on your own. Explore them at your own pace, on your own schedule. The course ends with an overview of other Hadoop-based technologies including Hive, Pig, and the very hot Spark framework – with a working example in Spark.

You will learn
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 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 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 2020

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

Expert Hadoop Administration: Managing, Tuning, and Securing Spark, YARN, and HDFS (Addison-Wesley...
  • Addison-Wesley Professional
  • Alapati, Sam (Author)
  • English (Publication Language)
  • 848 Pages - 12/06/2016 (Publication Date) - Addison-Wesley Professional (Publisher)

In expert Hadoop administration, 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.

Hadoop: The Definitive Guide: Storage and Analysis at Internet Scale

Hadoop: The Definitive Guide: Storage and Analysis at Internet Scale
  • O Reilly Media
  • White, Tom (Author)
  • English (Publication Language)
  • 756 Pages - 04/14/2015 (Publication Date) - O'Reilly Media (Publisher)

Hadoop: The Definitive Guide: Storage and Analysis at Internet Scale 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. Hadoop: The Definitive Guide will 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. Hadoop: The Definitive Guide is ideal 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 2020.

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

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

Mastering Hadoop 3: Big data processing at scale to unlock unique business insights
  • Singh, Chanchal (Author)
  • English (Publication Language)
  • 544 Pages - 02/28/2019 (Publication Date) - Packt Publishing (Publisher)

A complete guide to mastering the most advanced Hadoop 3 concept. Discover new features and capabilities of 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

Data Analytics with Hadoop: An Introduction for Data Scientists
  • O Reilly Media
  • Bengfort, Benjamin (Author)
  • English (Publication Language)
  • 288 Pages - 06/21/2016 (Publication Date) - O'Reilly Media (Publisher)
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 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 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

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