James A. Scott (prefers to go by Jim) is Director, Enterprise Strategy & Architecture at MapR Technologies and is very active in the Hadoop community. Jim helped build the Hadoop community in Chicago as cofounder of the Chicago Hadoop Users Group. He has implemented Hadoop at three different companies, supporting a variety of enterprise use cases from managing Points of Interest for mapping applications, to Online Transactional Processing in advertising, as well as full data center monitoring and general data processing. Jim also was the SVP of Information Technology and Operations at SPINS, the leading provider of retail consumer insights, analytics reporting and consulting services for the Natural and Organic Products industry. Additionally, Jim served as Lead Engineer/Architect for Conversant (formerly Dotomi), one of the world's largest and most diversified digital marketing companies, and also held software architect positions at several companies including Aircell, NAVTEQ, and Dow Chemical. Jim speaks at many industry events around the world on big data technologies and enterprise architecture. When he's not solving business problems with technology, Jim enjoys cooking, watching-and-quoting movies and spending time with his wife and kids. Jim is on Twitter as @kingmesal.
Within this post you will see mention of message-driven architectures. This is in short a subset of a service oriented architecture (SOA). This has been around for many years and is a very popular model. What you will find going through this post is that the foundational message-driven architecture is more competitive to the concepts of the enterprise service bus (ESB).
Just a few years ago, using a fingerprint to sign on to your phone seemed futuristic. Today, it’s everywhere and just the beginning of how biometrics will be woven into our lives.
Perhaps you’re old enough to remember when the library was the place we went to learn. We foraged through card catalogs, encyclopedias and the Reader's Guide to Periodical Literature in hopes that we’d be able to understand what was going on in other people’s minds when they decided what went where.
With all the talk about Big Data, most organizations are barely out of the starting blocks when it comes to exploiting it for business benefit. Gartner estimates that 85% of Fortune 500 companies are yet unable to exploit Big Data for competitive advantage.
In some circles today there is a sort of ‘Hadoop vs. RDBMS’ debate ongoing. Often the discussion casts Hadoop as the obvious heir apparent in the data processing world, with RDBMS cast as your father’s Oldsmobile.
The distributed computation world has seen a massive shift in the last decade. Apache Hadoop showed up on the scene and brought with it new ways to handle distributed computation at scale. It wasn’t the easiest to work with, and the APIs were far from perfect, but they worked.
For the past 25 years, applications have been built using an RDBMS with a predefined schema that forces data to conform with a schema on-write. Many people still think that they must use an RDBMS for applications, even though records in their datasets have no relation to one another.
Processing data from social media streams and sensors devices in real time is becoming increasingly prevalent, and there are plenty of open source solutions to choose from. Here is the presentation that I gave at Strata+Hadoop World, where I compared three popular Apache projects that allow you to do stream processing: Apache Storm, Apache Spark, and Apache Samza.
Are you ready to start streaming all the events in your business? What happens to your streaming solution when you outgrow your single data center? What happens when you are at a company that is already running multiple data centers and you need to implement streaming across data centers?
Getting from point A to point B has been one of humanity’s greatest preoccupations throughout history. While we’ve developed new methods of transportation such as railroads, cars, trucks, and airplanes, they never seem to be fast enough.
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