Archive for real-time tag

Orc O'Malley of the Yellow Elephant clan says LLAP

Owen O’Malley on the Origins of Hadoop, Spark and a Vulcan ORC

Owen O’Malley is one of the folks I chatted with at the last Hadoop Summit in San Jose. I already discovered the first time I met him that he was the big Tolkien geek behind the naming of ORC files, as well as making sure that Not All Hadoop Users Drop ACID. In this conversation, I learned that Hadoop and Spark are both partially his fault, about the amazing performance strides Hive with ORC, Tez and LLAP have made, and that he’s a Trek geek, too.

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Happy 10 Years Hadoop

Ten Years of Hadoop, Apache Nifi and Being Alone in a Crowd

Hadoop Summit in San Jose this year celebrated Hadoop’s 10th birthday. All of the folks on stage are people who contributed to Hadoop during those 10 years. One of them is Yolanda Davis.

Yolanda and I worked together on a Hortonworks project last year. She was in charge of the user interface design and development team. I caught up with her early in the morning of the last day of Hadoop Summit, and quizzed her on this new project she’s working on that you may have heard of, Apache Nifi. As promised, here is my interview with her on the subject of Nifi and the new HDF (Hortonworks Data Flow) streaming data processing platform, which includes Nifi, Apache Kafka and Apache Storm.

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Metron Eye On Cyber Security

Cyber Security with Apache Metron and Storm

A few weeks ago at Hadoop Summit, I caught up with some friends from the project I worked on last year with Hortonworks, including Ryan Merriman who is now an Apache Metron architect. Since Apache Metron was a project I knew virtually nothing about beforehand, I quizzed Ryan about it. The conversation evolved into a discussion of the merits of Storm versus Flink and Heron, something I’ve been meaning to delve into for months here.

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Hadoop Changes as Fast as Texas Weather

How Do You Move Data Preparation Work from MapReduce to Spark without Re-Coding?

So, is this a situation you recognize? Your team creates ETL and data preparation jobs for the Hadoop cluster, puts a ton of work into them, tunes them, tests them, and gets them into production. But Hadoop tech changes faster than Texas weather. Now, your boss is griping that the jobs are taking too long, but they don’t want to spring for any more nodes. Oh, and “Shouldn’t we be using this new Spark thing? It’s what all the cool kids are doing and it’s sooo much faster. We need to keep up with the competition, do this in real-time.”

You probably want to pound your head on your desk because, not only do you have to hire someone with the skills to build jobs on another new framework, and re-build all of your team’s previous work, but you just know that in a year or two, about the time everything is working again, some hot new Hadoop ecosystem framework will be the next cool thing, and you’ll have to do it all over again.

Doing the same work over and over again is so very not cool. There’s got to be a better way. Well, there is, and my company invented it. And now I’m allowed to talk about it.

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You Keep Using that Word, Real-Time

Four Really Real Meanings of Real-Time

Our director of engineering told me that she had a customer ask if we could do real-time data processing with Syncsort DMX-h. Knowing that real-time means different things to different people, the engineer asked what exactly the customer meant by real-time. He said, “We want to be able to move our data out of the database and into Hadoop in real-time every two hours.”

When she told me that story, I wanted to quote Inigo Montoya from “The Princess Bride.” You keep using that word, “real-time.” I do not think it means what you think it means.

But what does real-time actually mean? And what do you really mean when you say real-time? What do other people usually mean when they say real-time? How can you tell which meaning people are using? And what the heck is near real-time?

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