Gartner Blog Network

Nick Heudecker
Research Vice President
5 years at Gartner
19 years IT Industry

Nick Heudecker is an Analyst in Gartner's Research and Advisory Data Management group. Read Full Bio

Spark Summit West Recap

by Nick Heudecker  |  June 8, 2017

This year’s West Coast edition of Spark Summit continued the transition from a data science and data engineering event to an event focused on machine learning and, to a lesser extent, artificial intelligence. The summit content was accompanied by product announcements from Databricks (Serverless was the most notable) and updates to the Apache Spark project, […]

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Webinar FAQs for Hadoop & Spark: Understanding Open Source Opportunities and Risks

by Nick Heudecker  |  April 16, 2017

Merv Adrian and I received numerous questions during our April Hadoop webinar with several hundred attendees, and we have summarized and answered them below. (If you missed the webinar, you can watch it on-demand here.) How can Hadoop-Spark interface with conventional RDBMS such as Oracle/UDB/Teradata vs NoSQL DBs? Data can be moved to and from […]

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2016 Research Retrospective

by Nick Heudecker  |  December 25, 2016

Over the course of 2016, my publish research diversified quite a bit from previous years. When I first started working at Gartner, I focused on information infrastructure topics: DBMS, Hadoop related topics and big data. This year I dug into a range of topics related to data management, including blockchain, web-scale IT and bimodal. I […]

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China Trip Report

by Nick Heudecker  |  April 23, 2016

Scaling technology offerings in China is staggering. Even the most mundane product or service can have a couple hundred million monthly users. Massive amounts of data are generated, creating a need to take advantage of it. I had an opportunity to learn how data and analytics are being used in China when I toured Beijing […]

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Data Management for 100 Year Lifecycles

by Nick Heudecker  |  January 25, 2016

What do you do when you need to ensure data can be stored, shared and retrieved not for just the next five years, but for the next one hundred? I recently stumbled across a system called iRODS (integrated rule-oriented data system) that’s currently in use at multiple government and research organizations to solve this long-term […]

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Research Retrospective for 2015

by Nick Heudecker  |  December 16, 2015

With another year coming to a close and the focus on 2016, it’s always interesting to look back at the research I was able to publish this year with my outstanding colleagues. I have to thank my frequent collaborators/conspirators Merv Adrian, Mark Beyer, Roxane Edjlali, Ted Friedman and Lisa Kart. (This research is for Gartner […]

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Gold Coast Symposium Recap

by Nick Heudecker  |  October 30, 2015

Gartner’s CIO and IT Leader Symposium in Gold Coast is (probably) the third largest Symposium behind Orlando and Barcelona. This means I had fewer 1:1s with attendees, but they were no less intensive. And unlike Andrew, I didn’t have to give away any silver bullets. I had a total of 19 1:1s over the course […]

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Apache Flink Offers a Challenge to Spark

by Nick Heudecker  |  September 13, 2015

While Apache Spark has been hogging most of the data processing and analytics (DPA) spotlight over the last year, Apache Flink has managed to turn a few heads for real-time use cases. If you’re unfamiliar with Flink, it’s a memory-centric stream processing engine that can also do batch processing. Spark, on the other hand, is […]

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Big Data Isn’t Obsolete. It’s Normal. 

by Nick Heudecker  |  August 20, 2015

Each July and August sees the publication of the Gartner Hype Cycles. Hype Cycles offer an overview of the relative maturity of technologies, services and business disciplines in a certain domain. They provide not only a scorecard to separate hype from reality, but also a model that helps enterprises decide when they should adopt a […]

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Big Data Tells Us What We Want to Hear

by Nick Heudecker  |  March 20, 2015

This is a guest post from Mark Beyer. In the era of Big data, the theory goes that with more data, more inputs and more information, we can learn more, discover more and develop new insights. What if that is wrong? A “theory of everything” is a complex physical theory that all things in the […]

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