Data seems to have engulfed the world. The fast proliferation of data (especially unstructured) is one of the driving forces behind the growth of big data analytics. Several studies indicate that the amount of data produced every 10 minutes today is similar to the amount that was created from the beginning of recorded time through the year 2003. According to IDC, most of this data (up to about 90%) comes in unstructured form.

Big data analytics is not just about being able to capture a wide variety of unstructured data, but also about processing and combining that data with other information to gain new insights which can be used improve the performance of your business.

Now, let’s understand the term ‘unstructured data’ and comprehend how it is different from other forms of data.

What is Unstructured Data?
Data, which also contains meta-data, are generally categorized as structured or semi-structured data. Relational databases that have schema of tables, XML files containing tags, simple tables, etc. are a few examples of structured data.

While data from a blog content, or email messages, or a comment, or any text document, or an audio file, or video file or images, which account for about 80 to 90% of data available for analysis, do not conform to any specific structure. These forms of data are classified as unstructured data.

How is Hadoop suitable for analyzing unstructured data?
Big data is changing our lives in significant ways. For big data to unleash its true potential, technology should be in place to enable enterprises to capture and store the huge amounts of unstructured data in its native format. This is where Hadoop comes in picture.

Hadoop has become one of the most popular data processing technologies for big data analytics. Hadoop allows for addressing the bigger business questions that can benefit a business hugely.

  1. Hadoop possesses a distributed storage system and has a distributed processing framework. This is useful for the analysis of unstructured data, owing to its size and complexity.
  2. Hadoop is meant to support Big Data needs of enterprises as it is too big for traditional database technologies to accommodate such a massive amount of data.
  3. Data in Hadoop Distributed File System (HDFS) is stored as files. Hadoop does not mandate the presence of a schema or a structure to the data that has to be stored.
  4. Hadoop also comes with applications like HBASE, Sqoop, HIVE, etc. to import and export data from other traditional and non-traditional databases.
  5. Hadoop is also a powerful tool for writing customized codes. Analyzing unstructured data requires complex algorithms. Hadoop allows programmers to implement algorithms of any complexity while exploiting the benefits of the framework for efficiency and reliability.

Key Characteristics of Hadoop That Big Data Professionals Should Know
We have enlisted the five key characteristics of Hadoop that IT professionals should know so that they can maximize its potential in managing unstructured data and advancing the cause of big data analytics.

  1. Hadoop is cost effective. Being an open-source framework, Hadoop runs on standard servers. It allows adding hardware and operational costs are comparatively low because it has a common software across all infrastructures.
  2. Hadoop offers an efficient framework for processing large data sets. Hadoop includes MapReduce as the software programming framework. In other words, MapReduce provides a framework to move the processing software to the data rather than moving data across a network for being processed. MapReduce also offers programmers a common way to define and orchestrate complex processing tasks across various clusters.
  3. Hadoop works on your existing database and analytics infrastructures. Hadoop can handle sets of data and tasks that may pose a problem for legacy databases. It does not require you to displace your existing infrastructures rather it supports them.
  4. Hadoop offers the best value where it is implemented in combination with the right infrastructure. The Hadoop framework is capable of running on mainstream standard servers that use common Intel server hardware. However, newer servers with the latest computing capabilities, more cache, and larger memory footprint typically provide better performance where Hadoop offer a better value with faster in node storage. Therefore, when systems contain some amount of solid-state storage that is optimized with the latest advances in automated tiering, compression, deduplication, encryption, erasure coding and thin provisioning, Hadoop can be scaled to unprecedented levels.
  5. Hadoop is supported by a large and active ecosystem. Hadoop has a large and active ecosystem developed around itself, as it usually does around open-source solutions. Several vendors are available in the market to provide all or parts of the Hadoop stack, including third-party applications, management software, and a wide range of other tools to facilitate the deployment of Hadoop.

Unstructured data is growing at a rapid pace across a variety of applications, in different formats. The organizations that are best able to harness it and leverage it for competitive advantage are ripping the optimum results and benefits.

Clearly, Hadoop has all the capabilities to manage unstructured data. However, in many cases, processing unstructured data, particularly video/audio analysis, requires designing optimized algorithms to extract insights for analysis. But with the innovations in the field of big data, this is definitely not an issue to be bothered.


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