The Appalling Ratio of US CEO/Worker Pay

My colleague Darryl Carlton and I recently discussed the obscene ratio between CEO pay and average worker pay in the US. And this IS about the US – we are supporting an astonishing gap compared to the rest of the world, and high tech vendors like Oracle are not the only ones at the top of the list – Larry Ellison comes in only number 4 on this Bloomberg list, pulling down 1,287 times what an average Oracle worker (not impoverished at nearly $75K per year) collects.

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What, Exactly, Is “Proprietary Hadoop”? Proposed: “distribution-specific.”

Many things have changed in the software industry in an era when the use of open source software has pervaded the mainstream IT shop. One of them is the significance – and descriptive adequacy – of the word “proprietary.” Merriam-Webster defines it as “something that is used, produced, or marketed under exclusive legal right of the inventor or maker.” In the Hadoop marketplace, it has come to be used – even by me, I must admit – to mean “not Apache, even though it’s open source.”

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Hadoop Summit Recap Part Two – SELECT FROM hdfs WHERE bigdatavendor USING SQL

Probably the most widespread, and commercially imminent, theme at the Summit was “SQL on Hadoop.” Since last year, many offerings have been touted, debated, and some have even shipped. In this post, I offer a brief look at where things stood at the Summit and how we got there. To net it out: offerings today range from the not-even-submitted to GA – if you’re interested, a bit of familiarity will help. Even more useful: patience.

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That Exciting New Stuff? Yeah… Wait Till It Ships.

A brief rant here: I am asked with great frequency how this RDBMS will hold off that big data play, how data warehouses will survive in a world where Hadoop exists, or whether Apple is done now that Android is doing well. There is a fundamental fallacy implicit in these questions.

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Hadoop Summit Recap Part One – A Ripping YARN

I had the privilege of keynoting this year’s Hadoop Summit, so I may be a bit prejudiced when I say the event confirmed my assertion that we have arrived at a turning point in Hadoop’s maturation. The large number of attendees (2500, a big increase – and more “suits”) and sponsors (70, also a significant uptick) made it clear that the growth is continuing apace. Gartner’s data confirms this – my inquiry rate continues to grow, and my colleagues covering big data and Hadoop are all seeing steady growth too. But it’s not all sweetness and light. There are issues – and here we’ll look at the centerpeice of the technical messaging: YARN. Much is expected – and we seem to be doomed to wait a while longer.

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Hadoop 2013 – Part Four: Players

The first three posts in this series talked about performance projects and platforms as key themes in what is beginning to feel like a  watershed year for Hadoop. All three are reflected in the surprising emergence of a number of new players on the scene, as well as some new offerings from additional ones, which I’ll cover in another post. Intel, WANdisco, and Data Delivery Networks recently entered the distribution game, making it clear that capitalizing on potential differentiators (real or perceived)  in a hot market is still a powerful magnet. And in a space where much of the IP in the stack is open source, why not go for it? These introductions could all fall into the performance theme as well – they are all driven by innovations intended to improve Hadoop speed.

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Hadoop 2013 – Part Three: Platforms

In the first two posts in this series, I talked about performance and projects as key themes in Hadoop’s watershed year. As it moves squarely into the mainstream, organizations making their first move to experiment will have to make a choice of platform. And – arguably for the first time in the early mainstreaming of an information technology wave – that choice is about more than who made the box where the software will run, and the spinning metal platters the bits will be stored on.There are three options, and choosing among them will have dramatically different implications on the budget, on the available capabilities, and on the fortunes of some vendors seeking to carve out a place in the IT landscape with their offerings.

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Hadoop 2013 – Part Two: Projects

In Part One of this series, I pointed out that how significant attention is being lavished on performance in 2013. In this installment, the topic is projects, which are proliferating precipitously. One of my most frequent client inquiries is “which of these pieces make Hadoop?” As recently as a year ago, the question was pretty simple for most people: MapReduce, HDFS, maybe Sqoop and even Flume, Hive, Pig, HBase, Lucene/Solr, Oozie, Zookeeper. When I published the Gartner piece How to Choose the Right Apache Hadoop Distribution, that was pretty much it.

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Hadoop 2013 – Part One: Performance

It’s no surprise that we’ve been treated to many year-end lists and predictions for Hadoop (and everything else IT) in 2013. I’ve never been that much of a fan of those exercises, but I’ve been asked so much lately that I’ve succumbed. Herewith, the first of a series of posts on what I see as the 4 Ps of Hsdoop in the year ahead: performance, projects, platforms and players.

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Diary of an Asian Swing: Day 4

Halfway across the world you go to breakfast and see a neighbor is in your hotel too. How often does it happen? Today I saw an SAP colleague I worked with two decades ago at Sybase – and his colleague, with whom I’ll meet while in Singapore. Great start to the day.

This day was all business. Met several Gartner clients to talk Big Data (since that was my billing.) Interest is high, and like North American firms, one of the key questions, as always, is Value. “What are people doing? What is proving useful from a business perspective?”

Gartner’s local office is beautiful – two floors in a thriving business neighborhood in one of the world’s most vibrant cities. I was told per capita income here is the second highest in the world, and the way the city is kept continues to impress: clean, efficient, beautifully designed and planted with fabulous flora everywhere. Our people here are professional, motivated, friendly and prepared for all our meetings, making sure I know who we’re meeting with and why.

It was a busy, stimulating day capped with dinner with my colleague Arun Chandrasekaran in the Pan Pacific Hotel’s restaurant. Multiple serving stations with different cuisines: Indian, Cantonese, Japanese…. that marvelous Singaporean polyglot cuisine I love. And if the food was good, the conversation was even better. Arun and I talked about how his infrastructure research and my software focus converged in big data and what our next collaboration should be after the Hadoop pilots piece we’re nearing completion on now.

Closing the day with a little BBC World in my room, I watched the pre-election coverage, amused by the overloading of the “battleground states” metaphor when I switched to CNN. They even referred to reporters “embedded” there. Please. Thank goodness this overpriced, overheated exercise will soon be complete. And after all the sound and fury, I don’t expect much will have changed.

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