Top TIBCO Architects Answer Your Toughest Questions #AskTIBCO

February 7, 2013 by 2 Comments
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Screen Shot 2013-02-07 at 12.34.01 PMWe live in a world where information is flowing fast, and catching the attention of the people in the know has never been harder. As much as the Web is democratic, it isn’t the easiest way to fight through the noise and get your most important questions answered.

TIBCO is offering exactly this opportunity. Today we launch #AskTIBCO, a chance for you to invest a small effort and have a shot at being the featured question for our response to your curiosity.

February is integration month and we’ll be using that opportunity to choose the best questions about integration. We’ll answer those questions with a video response from our top people, including TIBCO’s top architect, Paul Brown.

How it works

Tweet us your questionWe want you to send us your burning architecture questions by tweeting them to us at @TIBCO using the #AskTIBCO hashtag and we will post a video response to the best questions in the TIBCOmmunity online.

Remember, you have to submit your questions via Twitter.

We look forward to hearing from you and engaging in great conversations around your most important questions.

Filed Under: Integration Tagged With: , , , , ,

Connecting ActiveSpaces and Hadoop

September 25, 2012 by 4 Comments
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TIBCO is pleased to announce that we’re making code available that connects TIBCO ActiveSpaces to Apache Hadoop MapReduce, Apache Pig, and Apache Hive.  Hadoop and ActiveSpaces both support data-intensive distributed applications, and so they’re the perfect match!

Hadoop’s HDFS™ is designed to hold files of petabyte size, but the architectural tradeoff that was made is that you don’t get random access to that file – you have to read the whole thing.  Hadoop has some other storage technologies like HBase™ that have some more random access, but they have nothing like the in-memory data grid that ActiveSpaces provides.  With ActiveSpaces, you get random access to all of your data, without ever having to hit a disk, and with peer-to-peer replication, keeping Big Data in memory is quite feasible.

The code that we’re making available comes in three parts.  The first part integrates ActiveSpaces into the core MapReduce functionality, and provides an InputFormat and an OutputFormat for ActiveSpaces.  Take your class that describes your Tuples, and inherit from the simple provided interface ASWritable.  Now you can use MapReduce to create operations on all the Tuples in your space, and chain them together in all the standard MapReduce ways.

If, on the other hand, you’d prefer not to write Java™ code, but would prefer to script your data flows in Pig, the code supplies a LoadFunc and a StoreFunc that allow full interoperability between Pig and ActiveSpaces.  Now the dataflows that you’ve designed in Pig can read from, and write to ActiveSpaces, taking advantage of all ActiveSpaces has to offer. [Read more...]

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