George Anadiotis

George's got tech, data, and media, and he's not afraid to use them. Coming from an IT background, he's had the chance to learn to play many instruments on the way to becoming a one man band and an orchestrator: being a Gigaom analyst, serving Fortune 500, startups and NGOs as a consultant, building and managing projects, products and teams of all sizes and shapes, and getting involved in award-winning research among others. George runs Linked Data Orchestration: http://linkeddataorchestration.com

Andrew Brust

Andrew Brust has worked in the software industry for 25 years as a developer, consultant, entrepreneur and CTO, specializing in application development, databases and business intelligence technology. He has been a developer magazine columnist and conference speaker since the mid-90s, and a technology book writer and blogger since 2005. Andrew serves as Senior Director, Technical Product Marketing and Evangelism at Datameer, a big data analytics company.

Tony Baer (Ovum)

Tony Baer leads Ovum's Big Data research area. Over his 25 years in the industry, he has studied issues of data integration, software and data architecture, middleware, and application development. Having tracked the emergence of BI and data warehousing back in the 1990s, Baer sees similar parallels emerging in the world of Big Data today. His coverage focuses on how Big Data must become a first-class citizen in the data center, IT organization, and the business. Baer has a multi-disciplinary background touching the different tiers of enterprise software. His expertise in data management is complemented by deep background in software development platforms and middleware. Prior to joining Ovum, he was an independent analyst whose company onStrategies delivered market assessments and messaging advisory services to vendor clients. He co-authored some of the earliest books on the Java and .NET frameworks including Understanding the .NET Framework and J2EE Technology in Practice. He has spoken at numerous industry events covering data management, and has been chronicling the industry in his blog for over 15 years. His career began as a journalist with publications including Computerworld, Application Development Trends, Computergram, Software Magazine, Information Week, and Manufacturing Business Technology.

Latest Posts

Oracle to market: We're Cloud 2.0

Oracle to market: We're Cloud 2.0

Oracle is using its later start to position its cloud as the state of the art platform that delivers guaranteed service levels. It is also aggressively positioning its public cloud as the best place to run Oracle databases, and yes, the bulls eye is on Amazon.

5 hours ago by in Big Data Analytics

Hybrid transactional analytical processing

Hybrid transactional analytical processing

Traditionally, operational databases and platforms for data analysis have been two different worlds. This has come to be seen as natural, as after all the requirements for use cases that need immediate results and transactional integrity are very different from those that need complex analysis and long-running processing.

December 18, 2017 by in Data Management

Insight Platforms as a Service

Insight Platforms as a Service

Remember how we noted data is going the way of the cloud? While there are no signs of this slowing down, there's another interesting trend unraveling, the so-called Insight Platforms as a Service (IPaaS). The thinking behind this is simple: if your data is in the cloud anyway, why not use a platform that's also in the cloud to run analytics on them, and automate as much of the process as possible?

December 18, 2017 by in Data Management

Streaming becomes mainstream

Streaming becomes mainstream

The endless streams of data generated by applications lends its name to this paradigm, but also brings some hard to deal with requirements to the table: How do you deal with querying semantics and implementation when your data is not finite, what kind of processing can you do on such data, and how do you combine it with data from other sources or feed it to your machine learning pipelines, and do this at production scale?

December 18, 2017 by in Data Management

The machine learning feedback loop

The machine learning feedback loop

The pace of change is catalyzed and accelerated at large by data itself, in a self-fulfilling prophecy of sorts: data-driven product -> more data -> better insights -> more profit -> more investment -> better product -> more data. So while some are still struggling to deal with basic issues related to data collection and storage, governance, security, organizational culture, and skillset, others are more concerned with the higher end of the big data hierarchy of needs.

December 18, 2017 by in Data Management

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