Hadoop is a free and open source distributed storage and computational platform. It was created to allow storing and processing large amounts of data using clusters of commodity hardware. In the last couple of years, Hadoop became a de facto standard for the big data projects. In this chapter, we will cover the following topics:

This chapter will give an overview of the Hadoop philosophy when it comes to choosing and configuring hardware for the cluster. We will also review the different Hadoop distributions, the number of which is growing every year. This chapter will explain the similarities and differences between those distributions.

For you, as a Hadoop administrator or an architect, the practical part of cluster implementation starts with making decisions on what kind of hardware to use and how much of it you will need, but there are some essential questions that need to be asked before you can place your hardware order, roll up your sleeves, and start setting things up. Among such questions are those related to cluster design, such as how much data will the cluster need to store, what are the projections of data growth rate, what would be the main data access pattern, will the cluster be used mostly for predefined scheduled tasks, or will it be a multitenant environment used for exploratory data analysis? Hadoop's architecture and data access model allows great flexibility. It can accommodate different types of workload, such as batch processing huge amounts of data or supporting real-time analytics with projects like Impala.

At the same time, some clusters are better suited for specific types of work and hence it is important to arrive at the hardware specification phase with the ideas about cluster design and purpose in mind. When dealing with clusters of hundreds of servers, initial decisions about hardware and general layout will have a significant influence on a cluster's performance, stability, and associated costs.