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Big Data Warehousing: Building a Relevance Engine using Hadoop, Mahout, and Pig

by on Dec 27, 2012

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Over the past few years, relevant recommendations have become expected and essential as part of the customer experience. From the customer’s perspective, marketing interactions are becoming helpful ...

Over the past few years, relevant recommendations have become expected and essential as part of the customer experience. From the customer’s perspective, marketing interactions are becoming helpful and time saving, instead of being generic, out of context, and annoying. If you shop at any of the major online retailers such as Amazon or Bluefly you may think they somehow have gotten inside your head as they present and recommend products relevant to you. This is an exponential improvement of the traditional psych-demographic profiling and targeting of the “old world”.

We talked about how Mahout can be leveraged to build a Recommendation Engine with a minimum of coding. We discussd how the open source search and machine learning capabilities of Apache Solr and Mahout can be combined to power large scale data driven applications that effectively combine real time access with large scale enrichment and discovery.

Caserta Concepts has grown beyond its roots as a provider of traditional data warehouse and BI consulting to also offer big data warehousing. If you’re a developer and are experienced in Hadoop, Hive, HBase, Mahout, Datameer or other Big Data technologies, we want to get to know you!

For more information, visit http://www.casertaconcepts.com/.

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Big Data Warehousing: Building a Relevance Engine using Hadoop, Mahout, and Pig Big Data Warehousing: Building a Relevance Engine using Hadoop, Mahout, and Pig Presentation Transcript