Data Management

From hot information to in-memory to tape drives, you need to manage your data.

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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

Moving up the analytics stack

Moving up the analytics stack

As descriptive and diagnostic analytics are getting commoditized, we are moving up the stack towards predictive and prescriptive analytics. Predictive analytics is about being able to forecast what's coming next based on what's happened so far, while prescriptive analytics is about taking the right course of action to make a desirable outcome happen.

December 18, 2017 by in Data Management

M2M makes sense for DBS Bank

[CASE STUDY] Singapore bank is expanding its machine-to-machine infrastructure to capture customer interactions on mobile devices and social media in real-time, which it hopes will improve service quality and customer experience.

January 4, 2013 in Data Management

PDF-eXPLODE

PDF-eXPLODE

For standard invoices and reports requiring efficient delivery, PDF-eXPLODE could well be a lifesaver. It can be a bit touchy on occasion, but once your document templates are set up properly it should be plain sailing.

March 30, 2007 by in Data Management

IBM DB2 9

IBM DB2 9

IBM's DB2 database adds several powerful new tools in version 9 including native XML support and DB2 Developer Workbench, and offers serious competition to Oracle and Microsoft.

August 31, 2006 by in IBM

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