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Big Data Analytics

The V's (Volume, Variety and Velocity) of data has been reaching to unprecedented level that means the big data is a reality now. This phenomenal growth of data doesn’t mean that one must understand big data in order to decipher the information, but we must understand the possibilities of what can be done with big data analytics. At Nextogen, we have developed a range of competencies and technological capabilities across its business to develop a set of innovative data analytics solutions. These solutions will assist our client to improve the quality of critical information in their business.  It’s not about Data. It’s about Insight and Impact. Big data is massive and messy, and it’s coming at you fast builds within multiple data stores in abundant formats. In process you may accumulated billions of rows of data with hundreds of millions of data combinations. So the solution to the big data challenge requires high-performance analytics to process and figure out what's important and what's not. Our solutions are either of a “packaged” or “customized” nature, depending on the nature of your requirements. As part of packaged solutions we deliver standard, routine assignments, while with customized solutions you will get complex and specialized assignments. With the information explosion and the mercurial growth of social media and content, the appetite for intelligence is growing at an alarming rate. Every organization is on a mission to make sense of the explosion of data through Web traffic, twitter tweets, mobile SMS, social network comments, geo-spatial data, as well as software and sensors that monitor shipments, air traffic, logistics, suppliers and customers — to guide decisions, trim costs and lift sales. The key to reducing cost is in the ability to leverage cloud. The nature of cloud computing lends itself to elastic scaling and pay for what you use. With cloud computing the guesswork is reduced and often the quality of the implementation is often driven by the SLA of the cloud service providers.

Nextogen Hadoop Solutions:

Nextogen Hadoop Solutions help businesses overcome the challenges of huge volume of data collection, processing at very speed and handling variety of data integration.

Hadoop ETL (Extract Transform Load):

Nextogen Hadoop Solution extends capabilities of Hadoop for ETL processes, turning it into a highly scalable, affordable, and easy-to-use data integration environment. Unlike current ETL framework which no longer provide the scalability required by the business at an affordable cost.

  • Source: Connect to any data source or target – Structure and Un structure Data (Facebook, Twitter, Blogs, etc.)
  • Develop MapReduce ETL jobs
  • Use case preparation
  • Predictive Analyzing capabilities
  • Optimize performance and efficiency of each individual node

Hadoop MapReduce Acceleration:

Nextogen Hadoop Solution helps instantly accelerate the performance and efficiency of MapReduce applications running in Hadoop. Increasingly, organizations are building critical applications running in Hadoop to turn massive data sets into valuable business insights.

Hadoop Smart Connectivity:

Nextogen Hadoop Solution makes it easy to get much-needed data in and out of Hadoop within the timelines required by the business. The solution leverages Hadoop’s unmatched scalability to uncover valuable insights from all kinds of data.

Nextogen's Approach:

​Nextogen’s deals with Big Data projects in a disciplined manner. First our approach to management of data volume in terms of how we store, scale and archive along with security, governance and data quality then comes the rate of data velocity and finally how we optimize and infer intelligence from variety of data. Hadoop's MapReduce involves distributing a dataset among multiple servers and operating on the data which is the "map" stage. The partial results are then recombined into the "reduce" stage. To store data, Hadoop utilizes its own distributed file system, HDFS, which makes data available to multiple computing nodes. A typical Hadoop usage pattern involves three stages:

  • Load Data into HDFS
  • ​Map reduce operations and,
  • ​Retrieve results from HDFS

Hadoop is typically useful in drawing correlations, understanding behaviors or identifying patterns like the social media sites who are the major consumers of Hadoop – store the core data in a database and search Hadoop for behaviors, likes, interests and then transfer back the results to reflect in your social media pages as statistical numbers. Google, Facebook, Myspace, etc all use Hadoop.​​