Wednesday, April 23, 2014

Challenges of Big Data; Is Hadoop meeting the Big Data Challenge?

Are we living in the era of "Big Data”? Yes, of course. In today's technology-fuelled world where computing power has significantly increased, electronic devices are more commonplace, accessibility to the Internet has improved, and users have been able to transmit and collect more data than ever before. Organizations are producing data at an astounding rate. It is reported that Facebook alone collects 250 terabytes a day.

According to Thompson Reuters News Analytics, digital data production has more than doubled from almost 1 million petabytes (equal to about 1 billion terabytes) in 2009 to a projected 7.9 zettabytes (a zettabyte is equal to 1 million petabytes) in 2015, and an estimated 35-40 zettabytes in 2020. Other research organizations offer even higher estimates!

As organizations have begun to collect and produce massive amounts of data, they have recognized the advantages of data analysis. But they have also struggled to manage the massive amounts of information that they have. This has led to new challenges.

Businesses realize that tremendous benefits can be gained in analyzing Big Data related to business competition, situational awareness, productivity, science, and innovation. 

Apache Hadoop meets the challenges of Big Data by simplifying the implementation of data-intensive, highly parallel distributed applications. It allows analytical tasks to be divided into fragments of work and distributed over thousands of computers, providing fast analytics time and distributed storage of massive amounts of data. 

Hadoop provides a cost-effective way for storing huge quantities of data. It provides a scalable and reliable mechanism for processing large amounts of data over a cluster of commodity hardware. And it provides new and improved analysis techniques that enable sophisticated analytical processing of multi- structured data.

Hadoop is different from previous distributed approaches in the following ways:

    In addition, Hadoop provides a simple programming approach that abstracts the complexity evident in previous distributed implementations. As a result, Hadoop provides a powerful mechanism for data analytics, which consists of the following:
  • Vast amount of storage — Hadoop enables applications to work with thousands of computers and petabytes of data. Over the past decade, computer professionals have realized that low-cost "commodity" systems can be used together for high-performance computing applications that once could be handled only by supercomputers. Hundreds of "small" computers may be configured in a cluster to obtain aggregate computing power that can exceed by far that of single supercomputer at a cheaper price. Hadoop can leverage clusters in excess of thousands of machines, providing huge storage and processing power at a price that an enterprise can afford.
  • Distributed processing with fast data access — Hadoop clusters provide the capability to efficiently store vast amounts of data while providing fast data access. Prior to Hadoop, parallel computation applications experienced difficulty distributing execution between machines that were available on the cluster. This was because the cluster execution model creates demand for shared data storage with very high I/O performance. Hadoop moves execution toward the data. Moving the applications to the data alleviates many of the high performance challenges. In addition, Hadoop applications are typically organized in a way that they process data sequentially. This avoids random data access (disk seek operations), further decreasing I/O load.
  • Reliability, failover, and scalability — In the past, implementers of parallel applications struggled to deal with the issue of reliability when it came to moving to a cluster of machines. Although the reliability of an individual machine is fairly high, the probability of failure grows as the size of the cluster grows. It will not be uncommon to have daily failures in a large (thousands of machines) cluster. Because of the way that Hadoop was designed and implemented, a failure (or set of failures) will not create inconsistent results. Hadoop detects failures and retries execution (by utilizing different nodes). Moreover, the scalability support built into Hadoop's implementation allows for seamlessly bringing additional (repaired) servers into a cluster, and leveraging them for both data storage and execution. For most Hadoop users, the most important feature of Hadoop is the clean separation between business programming and infrastructure support. For users who want to concentrate on business logic, Hadoop hides infrastructure complexity, and provides an easy-to-use platform for making complex, distributed computations for difficult problems.

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