Top 5 Big Data Trends Of 2014 - InformationWeek

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12/8/2014
11:30 AM
Doug Henschen
Doug Henschen
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Top 5 Big Data Trends Of 2014

As companies move beyond bleeding-edge experiments into production deployments, these trends point to real-world progress in big data analysis.

InformationWeek Chiefs Of The Year: Where Are They Now?
InformationWeek Chiefs Of The Year: Where Are They Now?
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The era of big data analysis is here to stay. Take your pick of 2014 proof points.

Tech watchers might cite the more than $200 million in venture capital raised by the top three NoSQL database vendors, or the $1 billion raised by the top-three Hadoop software distributors. Many took note of the recent declaration by Forrester Research that "Hadoop is no longer optional" for large enterprises, thanks to compelling "Hadooponomics" that make it a must for high-scale storage and data processing.

InformationWeek is more impressed by the testimonials of companies that are getting real value out of big data platforms and analysis techniques. Pfizer and Merck, for example, are developing more effective and affordable drugs thanks to big data techniques that are leading to more targeted treatments and more productive manufacturing processes. GE and others are demonstrating improvements in industrial equipment performance, uptime, and safety thanks to Internet of things-style applications.

[Want more on the top IT achiever of 2014? Read IT Chief Of The Year: Bank Of America's Cathy Bessant.]

And then there are the pioneers like The Weather Company and Facebook that say they just couldn't run their data-driven businesses without new platforms, even if they still have a place for more conventional tools like relational databases.

Here are five trends witnessed over the last year that point to progress in big data analysis:

1. SQL meets Hadoop
Hadoop is here to stay, so every data management vendor worth its salt must have a SQL-on-Hadoop or SQL-access-to-Hadoop option. Here are five of our most-read stories in the SQL-meets-Hadoop vein:

Just remember that SQL is not designed to find correlations among variably structured data sets. Nor does it support machine learning, many advanced analytics techniques, or other approaches often associated with big data analysis. If SQL solved everything, we wouldn't need new platforms.

2. Platforms mature
Every other week in 2014, or so it seems, Hadoop software distributors and NoSQL database vendors announced new management consoles, security systems, data management capabilities, search engines, or high-availability features. Here's a sampling of what we're talking about:

These and other big data vendors are trying to reassure enterprise IT types that these products are secure and reliable as 30-year-old database management systems. Let's just say that more than a few grizzled IT veterans are still used to working with favored and familiar tools and still need some convincing.

3. Educational options proliferate
Nature abhors a vacuum, so into the void of data science and big-data platform knowledge and expertise have rushed vendors,

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Doug Henschen is Executive Editor of InformationWeek, where he covers the intersection of enterprise applications with information management, business intelligence, big data and analytics. He previously served as editor in chief of Intelligent Enterprise, editor in chief of ... View Full Bio
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D. Henschen
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D. Henschen,
User Rank: Author
12/8/2014 | 12:23:16 PM
The "Spark as Hadoop replacement" myth
I'm seeing a lot of misleading and innaccuarate stories about Apahce Spark as an alternative to Hadoop. Yes, Spark can operate in a stand-alone clustered server deployment, but talking to Databricks, they're very clear in saying that they foresee operating on top of Hadoop, for the most part (or in the cloud). Maybe if data volumes are small the stand-alone approach would work, but I don't think Databricks wants to solve the high-scale storage problem. It has its hands full just ensuring solid-performing analysis engines for machine learning, SQL, R, graph, and streaming analysis.
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