Predictive Analytics for Business Advantage

A recent TDWI report sponsored by Alteryx shows that predictive analytics is having a huge impact on the data and analytics landscape within organizations. The infographic below highlights the data types and new use cases that benefit from the increased use of predictive analytics, and why business analysts are best positioned to make the greatest contribution.

Where There’s Spark There’s Fire: The State of Apache Spark in 2014

Matei Zaharia, CTO of Databricks and Creator of Apache Spark

In this special guest feature, Matei Zaharia, CTO of Databricks and Creator of Apache Spark, explores open-source Apache Spark ‘s status in the Hadoop community.

Industry Perspectives from the 2013 O’Reilly Strata + Hadoop World Conference

The O’Reilly Strata + Hadoop World Conference is one of a few conferences that seriously can deliver on the mission of providing a state-of-the-art perspective on the big data industry. Here is a selection of video presentations made by industry luminaries that can guide enterprise thought leaders.

The userR!2014 Conference in Review

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FIELD REPORT Last week I attended the long-anticipated useR!2014 international conference at the UCLA campus, my alma mater. The four day event had something for everyone in attendance – all the brain cycles centered around the use of the R statistical environment. Since R is a primary tool for my work in data science and […]

Leaving Data on the Table: Data Scientists Reveal Obstacles to Big Data

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The huge volume of Big Data produced by sensors, genomic sequencers, electronic exchanges, and connected devices continues to generate headlines but it’s the diverse types of data, not the volume, that’s a bigger challenge to data scientists and is causing them to “leave data on the table.”

In-Memory Database vs. In-Memory Data Grid

Nikita Ivanov, CTO of GridGain

In-memory computing is comprised of two main categories: In-Memory Databases and In-Memory Data Grids. Nikita Ivanov, CTO of GridGain delves into the differences between the two and when to apply each technology.

Big Data Camp 2014 in Review

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It was Saturday, June 14 and I was up at the crack of dawn (which is quite an achievement for a late night data hacker like me) to get over to the Big Data Camp 2014 happening at the DirectTV campus in beautiful El Segundo, Calif. (actually a spartan industrial area just south of LAX). I was anticipating a great day of big data technology focusing on three advertised session tracks: Data Science, Hadoop, and NoSQL.

Big Data Survey Finds 75% of Businesses Yet to Reach Production

Seventy-five percent of businesses have yet to successfully deploy big data analytics solutions to gain business-impacting insights, despite 65 percent increasing their investment in analytic services and technologies in 2014. These findings are part of “Analytics 2014,” Lavastorm’s second annual survey on analytic usage, trends, and future initiatives.

Wearables – A Data Scientist’s Dream Come True

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Everyone knows that data scientists love data and the more of it, the greater the love. As a result, the surging interest in wearables is just what the doctor ordered because these electronic devices collect enormous treasure troves of data. In turn, it is the job of data scientists to make sense of it all, unlock secrets, and assign economic value. As a data scientist, it is a dream come true!

Why Do Hadoop Projects Fail?

I found an interesting discussion going on in the Global Big Data & Analytics group on LinkedIn – “Why do Hadoop projects fail?” Having just returned from the Hadoop Summit 2014 in San Jose, I witnessed plenty of use case examples Hadoop implementations that were wildly successful. I was therefore intrigued by the notion to itemize causes for failed projects.