The Future of Big Data and Hadoop

In 2013, Hadoop took a great leap forward with Hadoop 2.0(YARN), as this converted Hadoop from a batch-only file-based stack into a set of interactive capabilities with multiple optional databases. But as the Hadoop technology embarks on its second stage of evolution, how will it influence the Big Data world going forward?

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A study of over 200 IT professionals reveals that 32% of companies polled have existing Apache Hadoop deployments. 31% indicated having plans to deploy it within 12 months and 36% said their Hadoop deployment schedule would go beyond 12 months. Of those organizations that already use Hadoop, nearly 39% reported using Hadoop for service innovation including analysis of secondary data for modeling of “if-then scenarios” for products and services.

BigData

Hadoop for sensor Data Analytics

Hadoop is expected to drive business efficiency by slicing, dicing and analyzing sensor data coming from the Internet of Things. It provides business owners a better understanding of what is happening with their processes and tools.

In today’s world, almost everything that can be tracked has a sensor attached. Hadoop is likely to become one of the best tools to monitor the moment-to-moment status of performance and/or operations based on the analysis of massive amounts of historical data andhelp businesses increase operational efficiency, minimize downtime and cut costs. That said, in the near future Hadoop has a good chance to become an attractive candidate for sensor data storage solutions.

More SQL on Hadoop Initiatives

Innovative Hadoop platforms such as Pivotal HD , Impala or Presto are revamping the Hadoop technology to operate more like a relational database, thus enabling users to rapidly ask data related questions using SQL. Recently, most of the databases within the Hadoop ecosystem such as MongoDB, Cassandra or HBase have been non-relational and NoSQL-based. These platforms let you query the same data in a real-time manner, i.e. in seconds

Five years ago, a company had to pay $100K per terabyte of data for a perpetual software license as well as $20K per annum for support and maintenance. Now the Hadoop technology basically enables businesses to store, manage and analyze the same amount of data with a $1,000 / year subscription. Hadoop will make Big Data storage and management pretty low-cost, which will inevitably lead to vendor market fragmentation.

Hadoop to become the De-facto standard for building enterprise level Big Data Applications

According to Hadoop creator Doug Cutting, “more and more types of workloads will be supported on top of Hadoop” in the future. Beside network monitoring, fraud prevention, and targeting or risk modeling, the Hadoop technology is expected to be used in eCommerce for processing purchasing transactions and provide new tools for data aggregation, analysis and interpretation. As such, it is likely to become an operating system kernel for any data-centric platform, i.e. the de-facto standard for developers to build their big data applications.

Big Data will revolutionize learning and is one of the hottest career option

Big data is reshaping IT business. Thanks to cheap storage, massive processing power, and tools like Hadoop, organizations are now able to mine terabytes of information and derive useful business intelligence from it. The data revolution is also creating a new breed of hybrid business-IT jobs, ones that blend business knowledge and powerful IT tools to the benefit of tech-savvy line-of-business professionals — and the possible detriment of IT pros oblivious to the big data trend. The hottest careers right now have to do with using data effectively. Data analysts scrutinize data-based information to provide recommendations that assist senior leadership in making decisions.

Traditionally business analysts have been concerned with structured data from a single database source; however the use of unstructured data from multiple sources – “Big Data” – has given new depth and complexity to data analysis. Given the current talent crunch, a salary is really as much as a company is willing to spend. A Hadoop engineer making $110,000 might easily be valued by another company at $145,000. Both are reasonable salaries, but the second company may be in a situation where big data tech development has a larger impact, and thus is willing to pay more for the hire. Many of the jobs in big data tend to have high-variance compensation, as there always seems to be a company out there willing to outbid.

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