Big Data Solutions: Building a Recommendation Engine using Hadoop, Pig and Mahout

With time and over the years, recommendations have become expected and important as an integral part of the user experience worldwide. From a consumer’s point of view, marketing interactions could be favorable and useful and even time saving, rather than just being generic, annoying and out of the context. If you shop from leading and pioneering vendors and retailers like Amazon or Flipkart or Jabong or Bluefly, you might think that somehow they have gotten inside our mind while presenting and showing recommended items relevant to your search results. It is a consequential improvisation of the conventional psych demographic profiling as well as targeting of the old world. It has always been a point of discussion how Mahout can be used to build a recommendation engine with minimal coding and programming needs. Moreover, it has also been discussed a lot how the machine learning capabilities and open search potential of Mahout and Apache Solr can be blended to empower huge scale data driven applications that efficiently combine real time access with global scale discovery, enhancement and enrichment.

Recommendation engines are everywhere assisting firms and enterprises all across the world to provide a kind of ‘artificial intelligence’ to help put down filters through numerous options to pick handful of selections most apt and authentic for you. For example, when Netflix advises you movies based on the reviews and your preferences, after thoroughly searching history of you and zillions of other subscribers, you are actually benefitting from their recommendation engines. Many other big internet marketing companies makes use recommendation engines to boost their online services powered by big data solutions. In fact, for your record and knowledge, Datameer Company is going to have a seminar on the same specialized in offering data analytics solutions by making use of Hadoop technology.

Business problems being targeted and focused – A new suite of business issues were difficult to think and handle before including customer churn analysis, modeling true risk, recommendation engines, ad targeting, loyalty pricing, threat analysis, search quality fine tuning, trade surveillance etc. To address and solve above mentioned issues, a flexible infrastructure for data warehousing and big data analytics emerged heading to below mentioned endeavors and support public and private cloud deployments.

·         Capability to determine structured, unstructured and transactional data at a single platform

·         Solid state or lower latency in-memory devices for high chunks of web and real time applications

·         Slice out low cost commodity software, workloads and distributed processing

The interesting part is that the fundamental things of big data have been drastically changed from the core endeavors of BI and analytics to the ability that end users perform while analyzing, reporting and handling tasks over consistently growing chunks of structured as well as unstructured information like sensor data, log files, sales transactions, streaming data, emails, research data and images collectively known as ‘big data’.

Big data recommendation engine for e-commerce retailing is helpful in multiple things. Quite favorable and beneficial in raising average order size by recommending complementary items based on the predictive and foreboding analysis for up selling and cross selling of products, the cross channel analytics cover different attributes like average order value, sales attribution, lifetime value and event analytics cover a vast series of steps induced to a desirable outcome including registration and purchase.

Big data Existing and Emerging Firms to Watch –

·         Pioneering Leaders - Few of the leading firms catering big data solutions to medium and large-sized enterprises all over the world include HP Vertica, IBM, Microsoft, Teradata, Oracle, Netezza etc.   

·         Evolving Players – Few well known emerging leaders catering big data recommendation engine solutions include IB Technology, Splunk, Cloudera, Hortonworks, Datameer, DataStax etc.

All the enterprises are following two distinct approaches in general, including serving big data solutions in the public cloud or in the private cloud. It is becoming clear with time that there is going to be a tremendous and growing push towards an adaptive and flexible infrastructure for big data, master data management, data warehousing, and data analytics. All in all, with the increasing and consistent focus on mobility and better decision making, the businesses are going to push faster and quicker than corporate IT can ever react and respond.