Hadoop is the most popular, reliable and scalable distributed computing and storage for Big Data solutions. It comprises of components designed to enable tasks on a distributed scale, across multiple servers and thousands of machines.
This comprehensive 3-in-1 training course gives you a strong foundation by exploring Hadoop ecosystem with real-world examples. You’ll discover the process to set up an HDFS cluster along with formatting and data transfer in between your local storage and the Hadoop filesystem. Also get a hands-on solution to 10 real-world use-cases using Hadoop.
Contents and Overview This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.
The first course, Getting Started with Hadoop 2.x, opens with an introduction to the world of Hadoop, where you will learn Nodes, Data Sets, and operations such as map and reduce. The second section deals HDFS, Hadoop's file-system used to store data. Further on, you’ll discover the differences between jobs and tasks, and get to know about the Hadoop UI. After this, we turn our attention to storing data in HDFS and Data Transformations. Lastly, we will learn how to implement an algorithm in Hadoop map-reduce way and analyze the overall performance.
The second course, Hadoop Administration and Cluster Management, starts by installing the Apache Hadoop for cluster installation and configuring the required services. Learn various cluster operations like validations, and expanding and shrinking Hadoop services. You will then move onto gain a better understanding of administrative tasks like planning your cluster, monitoring, logging, security, troubleshooting and best practices. Techniques to keep your Hadoop clusters highly available and reliant are also covered in this course.
The third course, Solving 10 Hadoop'able Problems, covers the core parts of the Hadoop ecosystem, helping to give a broad understanding and get you up-and-running fast. Next, it describes a number of common problems as case-study projects Hadoop is able to solve. These sections are broken down into sections by different projects, each serving as a specific use case for solving big data problems.
By the end of this Learning Path, you’ll be able to plan, deploy, manage and monitor and performance-tune your Hadoop Cluster with Apache Hadoop.
About the Author
A K M Zahiduzzaman is a software engineer with NewsCred Dhaka. He is a software developer and technology enthusiast. He was a Ruby on Rails developer, but now working on NodeJS and angularJS and python. He is also working with a much wider vision as a technology company. The next goal is introducing SOA within the current applications to scale development via microservices. Zahiduzzaman has a lot of experience with Spark and is passionate about it. He is also a guitarist and has a band too. He was also a speaker for an international event in Dhaka. He is very enthusiastic and love to share his knowledge.
Gurmukh Singh is a technology professional with 14+ years of industry experience in infrastructure design, distributed systems, performance optimization, and networks. He has worked in big data domain for the last 5 years and provides consultancy and training on various technologies. He has worked with companies such as HP, JP Morgan, and Yahoo and has authored the book Monitoring Hadoop.
Tomasz Lelek is a Software Engineer and Co-Founder of InitLearn. He mostly does programming in Java and Scala. He dedicates his time and efforts to get better at everything. He is currently delving into big data technologies. Tomasz is very passionate about everything associated with software development. He has been a speaker at a few conferences in Poland-Confitura and JDD, and at the Krakow Scala User Group. He has also conducted a live coding session at Geecon Conference. He was also a speaker at an international event in Dhaka. He is very enthusiastic and loves to share his knowledge.
