A good portion of the startups I meet and advise want to use the newest, hottest technology to build something that’s cool, but not technologically groundbreaking. I have yet to meet a startup building a time machine, teleporter or quantum social network that would actually require some amazing new tech. They have awesome new ideas with down-to-earth technical requirements, so I kept wondering why they choose this shiny (and risky) new stuff when all they need is a good ol’ trustworthy database. I think it’s because many assume that building the latest and greatest needs the latest and greatest!

It turns out that’s only one of three bad reasons (traps) why people go for the shiny and new. Reason two is people mistakenly assume older stuff is slow, not feature rich or won’t scale. “MySQL is sluggish,” they say. “Java is slow,” I’ve heard. “Python won’t scale,” they claim. None of it’s true.

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Message brokers are not regularly covered here but are, nonetheless, important web-related technologies. Some time ago, I was asked by one of our customer to review a selection of OSS message brokers and propose a couple of good candidates. The requirements were fairly simple: behave well when there’s a large backlog of messages, be able to create a cluster and in case of the failure of a node in a cluster, try to protect the data but never blocks the publishers even though that might imply data lost. Nothing fancy regarding queues and topics management. I decided to write my findings here, before I forget…

I don’t consider myself a message broker specialist and I spent only about a day or two on each so, I may have done some big mistakes configuration wise. I’ll take the blame if something is misconfigured or not used correctly.

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High-Velocity Data—AKA Fast Data or Streaming Data—seems to be all the rage these days. With the increased adoption of Big Data tools, people have recognized the value contained in this data and they are looking to get that value in real-time instead of a time-shifted batch process that can often introduce a 6-hour (or more) delay in time-to-value.

High-velocity data has all of the earmarks of a big technological wave. The technology leaders are building stream processors. Venture firms are investing money in stream processing companies. And existing tech companies are jumping on the bandwagon and associating their products with this hot trend; making them buzzword compliant.

Some have asked whether high-velocity data will complement or replace Big Data. Big Data addresses pooled data, or data at rest. History tells us that there are …

High-Velocity Data—AKA Fast Data or Streaming Data—seems to be all the rage these days. With the increased adoption of Big Data tools, people have recognized the value contained in this data and they are looking to get that value in real-time instead of a time-shifted batch process that can often introduce a 6-hour (or more) delay in time-to-value.

High-velocity data has all of the earmarks of a big technological wave. The technology leaders are building stream processors. Venture firms are investing money in stream processing companies. And existing tech companies are jumping on the bandwagon and associating their products with this hot trend; making them buzzword compliant.

Some have asked whether high-velocity data will complement or replace Big Data. Big Data addresses pooled data, or data at rest. History tells us that there are different use cases and …