Chapter 1. Processing Big Data Using Hadoop and MapReduce
Continuous evolution in computer sciences has enabled the world to work in a faster, more reliable, and more efficient manner. Many businesses have been transformed to utilize electronic media. They use information technologies to innovate the communication with their customers, partners, and suppliers. It has also given birth to new industries such as social media and e-commerce. This rapid increase in the amount of data has led to an "information explosion." To handle the problems of managing huge information, the computational capabilities have evolved too, with a focus on optimizing the hardware cost, giving rise to distributed systems. In today's world, this problem has multiplied; information is generated from disparate sources such as social media, sensors/embedded systems, and machine logs, in either a structured or an unstructured form. Processing of these large and complex data using traditional systems and methods is a challenging task. Big Data is an umbrella term that encompasses the management and processing of such data.
Big data is usually associated with high-volume and heavily growing data with unpredictable content. The IT advisory firm Gartner defines big data using 3Vs (high volume of data, high velocity of processing speed, and high variety of information). IBM has added a fourth V (high veracity) to this definition to make sure that the data is accurate and helps you make your business decisions. While the potential benefits of big data are real and significant, there remain many challenges. So, organizations that deal with such a high volumes of data, must work on the following areas:
- Data capture/acquisition from various sources
- Data massaging or curating
- Organization and storage
- Big data processing such as search, analysis, and querying
- Information sharing or consumption
- Information security and privacy
Big data poses a lot of challenges to the technologies in use today. Many organizations have started investing in these big data areas. As per Gartner, through 2015, 85% of the Fortune 500 organizations will be unable to exploit big data for a competitive advantage.
To handle the problem of storing and processing complex and large data, many software frameworks have been created to work on the big data problem. Among them, Apache Hadoop is one of the most widely used open source software frameworks for the storage and processing of big data. In this chapter, we are going to understand Apache Hadoop. We will be covering the following topics:
- Apache Hadoop's ecosystem
- Configuring Apache Hadoop
- Running Apache Hadoop
- Setting up a Hadoop cluster
