What Is Big Data With Apache Hadoop?

what is big data hadoop

What Is Big Data With Apache Hadoop?

What is big data? A simple definition is the mass of data that is accumulated over time and used for decision making. Big Data is used by various industries including telecommunications, retail, financial services and consumer market research. As its name suggests, big data refers to huge amounts of data which is collected over a short period of time and used for making strategic decisions. The phrase itself explains the basic idea of big data: unstructured data, which is collected from all possible sources and used for making strategic decisions.

The basic idea behind the development of what is big data was presented at a web conference hosted by Google. Most of us know that Google’s PageRank algorithm was developed using unstructured data storage. In fact, this was one of the major reasons why PageRank became such a success for Google. Google’s other product, Google Trends, is another example of big data analytics. It uses complex mathematical algorithms to analyze consumer browsing habits over a given period of time. Each of these examples has its own pros and cons but what is most important is that big data analytics gives businesses the power to make informed decisions based on the collective data.

To get a better understanding of what is big data, you need to familiarize yourself with the popular open source project Hadoop. Hadoop is a framework for managing large data collections through streaming and parallel computing. The tutorial shows you how to set up your own Hadoop cluster using the popular Apache Hadoop framework.

The Apache Hadoop framework makes it easy to develop a web server based on a large collection of unstructured or semi-structured data. MapReduce is another key player in the Hadoop ecosystem, serving as an advanced middleware. MapReduce is able to eliminate the need for storing large datasets directly into the user’s computer. Instead, it divides the job between MapReduce nodes, each acting as a consumer or producer of data.

As the second tutorial in the series, Apache Hadoop 2.4 continues, the second step in the tutorial focuses on the MapReduce architecture. The second section of the tutorial covers topics such as Collections, which are used to implement MapReduce. MapReduce takes care of the storage, indexing, and searching of large amounts of unprocessed data and enables the developer to use a large number of CPU cycles for relatively small tasks. MapReduce also supports the concept of aggregation, which allows the developer to combine multiple tasks into one. Furthermore, the second section of the tutorial covers topics such as Web routing, which enables the application of dynamic web pages through an intermediate layer.

The third tutorial in the series, Apache Hadoop 2.5, further explores the use of MapReduce and shows how the system works. In this tutorial, participants will learn about concepts such as batch processing, data storage, and distribution. As previously mentioned, MapReduce is used for large data storage needs and is quite complex in its current form; participants will learn how to develop applications that efficiently utilize MapReduce.

The fourth and final installment of the series, Apache Hadoop 2.6, focuses on the implementation of certain advanced capabilities such as streaming and YSlow data transformations. In addition, the tutorial covers topics such as machine learning and social media. Machine learning allows programs to learn at a rapid rate and can significantly speed up deployment of applications. In particular, the fourth installment of the tutorial shows how to utilize large scale streaming.

In this wasp tutorial, participants will be introduced to Hadoop’s major features. Users will learn how to implement Map Reduce and the core components it uses. Finally, participants will explore how to best utilize the large amount of data stored in Hadoop using Map Reduce and its supporting frameworks. In this installment, we continue our comparison of Map Reduce to Hadoop. While both are major players in the open source community, they have different strengths.