Key Points
  • Big data management platforms such as Hadoop crunch huge amounts of data and deliver insights that enable organizations to manage, process and harness it for business value

  • However, organizations need to evaluate the quantum of disposable data and its sources, availability of in-house skills or partners, improvement areas in existing processes, and benchmark themselves against competitors before adopting Hadoop

  • When applied accurately, Hadoop or similar Big Data platforms for data mining can help in predicting demands effectively and building superior data products for a smarter economy

The surge in data and data sources has put organizations in a conundrum over how to manage, process and harness it for business value. In such an environment, a Big Data management platform such as Hadoop becomes a lifesaver.

Hadoop uses a programming model to crunch huge amounts of data and deliver insights that accelerate and enhance decision-making. It was designed to address the inadequacies of existing approaches in processing huge amounts of data. In 2004, Google developed MapReduce. The following year, Yahoo! developed Hadoop as an implementation of MapReduce. It was subsequently released as an open source project in 2007. Today, Hadoop has evolved into an effective software for distributed parallel processing of huge amounts of data.

The insights gained from processing and analyzing Big Data enable organizations to have better control over their processes and costs. It also helps in predicting demands effectively and building superior data products for a smarter economy. Hence, for organizations looking for a competitive advantage through analytics, investing in Hadoop can prove to be advantageous.


Implementing Hadoop

However, before rushing to adopt Hadoop or similar Big Data platforms to mine data, it is important for organizations to understand what they are signing up for. A few questions such as those given below can help companies develop a roadmap to the ‘when’ and the ‘how’ part of implementing Hadoop:

  • What is the quantum of disposable data and what are its sources?

  • Is there availability of in-house skills or partners who are knowledgeable for the initial successful deployment? With added information from the large data sets, what kind of decision–making can be improved?

  • Which are the processes that can be improved if the information available was more than what exists today?

  • What are the competitors doing to sharpen their advantage after gaining insights through Big Data?

The Hadoop ecosystem provides comprehensive tools for data movement, processing, warehousing and analysis. While a resource management software such as YARN is designed to support many types of analytical workloads, there are myriads of components built for this purpose such as Apache Pig, Hive, Storm and Spark.

Apache Pig is a high-level data-flow programming language and execution framework for data-intensive computing. Apache Hive is a data warehouse infrastructure built on top of Hadoop for providing data summarization, query and analysis. Apache Storm is for stream processing. Though these tools make data exploration easier, it may be overwhelming for organizations to handle so many Apache projects and decide how to approach the Big Data journey in a phased manner. It thus becomes even more critical to partner with the right service provider to help with the journey.

Hadoop’s impact on the Big Data landscape is best summed up by eBay’s Vice President Hugh Williams: “The world pre-Hadoop was fairly inflexible, but now you can run lots of different jobs of different types on the same hardware. It allows you to create innovation with very little barrier to entry. That's pretty powerful." 

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