Diginomica – RichRelevance crafts recipe for ingesting big data

Analytics has always been the sexy bit of data management. That’s where the nuggets of insight are teased to the surfaced and millions made by understanding why diapers sell beer or who is newly pregnant or how to route a jet so it burns 25% less fuel. But, behind that, there has always been the grunt work of extracting data from multiple, disparate sources, cleansing it of partial or bogus records, transforming it into a consistent and usable format, and loading it into the target analytics engine.

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TechTarget – Big data challenges include what info to use—and what not to

RichRelevance Inc. faces one of the prototypical big data challenges: lots of data, and not a lot of time to analyze it. For example, the marketing analytics services provider runs an online recommendation engine for Target, Sears, Neiman Marcus, Kohl’s and other retailers. Its predictive models, running on a Hadoop cluster, must be able to deliver product recommendations to shoppers in 40 to 60 milliseconds — not a simple task for a company that has two petabytes of customer and product data in its systems, a total that grows as retailers update and expand their online product catalogs.

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Scaling the peaks of Black Friday 2014: Shopping Insights and Metrics

We’ve finally slowed down enough to reflect on how we did in 2014 and the final holiday push: pretty darn great if I may say so myself. We ended another year at 100% uptime all year with record loads and HUGE growth.

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RichRelevance Expands Global Data Footprint with New Data Centers in Singapore and Stockholm

Eleven data centers enable the company to maintain uptime and performance at global scale, while increasing speed to delivery, for the world’s largest retailers 

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Tech Republic – Hadoop success requires avoidance of past data mistakes

To reach its full potential, Hadoop implementations should avoid the data warehouse infrastructure mistakes of the past.
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Twenty-one years ago, a year before the first web browser appeared, Walmart’s Teradata data warehouse exceeded a terabyte of data and kicked off a revolution in supply-chain analytics. Today Hadoop is doing the same for demand-chain analytics. The question is, will we just add more zeros to our storage capacity this time or will we learn from our data warehouse infrastructure mistakes?These mistakes include:

  • data silos,
  • organizational silos, and
  • confusing velocity with response time

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Staying Strong—And Safeguarding Your Data—During Sandy

We continue to think about our customers, friends and families on the east coast as they fight through yet another storm. We’re built to serve you and are here to help in any way you need. You can also make a donation to the Red Cross here.

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