IBM has a long and successful history with open source, from running Linux on IBM PCs to contributing initial codebase for Eclipse. We believe a mix of open source and closed source is the best way to drive adoption in the marketplace. Having the full support of a vendor like IBM can lower risk
This month’s articles show the promise of big data technology in action. Explore the tools that help you digest the data, filter out the noise and make your next business decision based on the trends it reveals. Grab your safety goggles—it’s time to put theory to the test.
IBM Analytics Warehouse for Bluemix is now generally available to all customers for your agile data warehousing and analytics needs. This pay-as-you-go cloud service, leveraging in-memory BLU technology, is designed to provide a single, agile in-memory platform for all applications required for
Developer resources for InfoSphere Streams are abundant. Review the top five resources to get started and join a thriving and growing community of InfoSphere Streams users across healthcare, energy and utilities, telecommunications and more.
Video content analytics tools are humanity's unblinking eyes, capable of continuously filtering the world's media streams at scale. Video content analytics algorithms can parse the fine details within and between successive frames of specific streams, supporting pattern recognition, gesture
You don’t want to miss the next TDWI Solution Spotlight on “Maximizing the Value of Your Data through Analytics.” Claudia Imhoff, president and founder of Intelligent Solutions Inc., will share everything from modern data architectures and use cases to data scientists and “things to ponder” when
Prioritizing data mining projects is a delicate art, equivalent to the decisions that R&D managers face every single day. How should you prioritize your data mining efforts and allocate your limited resources most effectively? Most important, how do you decide what NOT to work on?
Data science is extremely important in today’s data-driven world, but is only effective if it can be efficiently executed in a production environment. Find out about an essential best practice to make your data science effective.
If you take a quick glance at any technology publication (and many business publications as well) you will likely see some reference to real-time. There’s real-time customer service, real-time marketing, real-time analytics and the list goes on. But what does real time mean? Is there a standard
Real-world experimentation of a very personal and hyper-analytical nature is what the quantified-self (QS) movement is all about. QS practitioners are playing with approaches that behavioral scientists have traditionally applied to third-party subjects within controlled laboratory experiments. The
Many make out the data scientist to be a Renaissance woman or man who can single-handedly elevate the organization’s analytics savvy. However, preparing students for corporate roles in data science means training them for many positions on a team. At Arizona State University, we work closely with
With big data financial and transactional data no longer in silos, we can now look at them together. Vince Walden, Ernst and Young partner, says that big data technologies allow them to look at data from all angles.
SQL-on-Hadoop offerings, such as IBM InfoSphere BigInsights with Big SQL, can help IT departments leverage their existing expertise to move into the big data world. A new Big SQL technology preview is available so you can try it out for yourself.