Hadoop is increasingly being adopted across industry verticals for information management and analytics. It is a potential solution to the challenges posed by Big Data. It enables storage and processing of large amounts of data without investing in expensive, proprietary hardware. It facilitates distributed and limitless parallel processing of huge amounts of data across inexpensive, industry standard commodity servers that store as well as process the data. Hadoop’s unlimited scalability allows organizations to store data without worrying about performance, storage costs, archival, and retention periods. In addition to new business related capabilities, it offers a host of options for IT simplification and cost reduction. Initiatives such as offloads are at the heart of this type of optimization.
As a result, Hadoop capacity planning should be carried out as the first step in both IT-driven and business-driven use cases whenever Big Data projects are considered.
Capacity planning is an exercise and a continuous practice to arrive at the right infrastructure that caters to the current, near future, and future needs of a business. Businesses that embrace capacity planning will realize the ability to efficiently handle massive amounts of data and manage the user base. This in turn has the potential to positively impact the bottom line and help organizations gain a competitive edge in the marketplace.
This paper takes a look at why Big Data processing frameworks such as Hadoop clusters require careful capacity planning to ensure the timely launch of Big Data related capabilities. Additionally, it discusses how Hadoop capacity planning can facilitate appropriate Service Level Agreement (SLA) guarantees and ensure deliveries within defined budgets.