With the recent release of Pivotal HD, I wanted to check the current state of Hadoop SQL engines. SQL integration is growing in the Hadoop landscape and it’s a good thing for productivity and integration. Despite old and bloated, SQL has still no competition when it comes to data manipulation. I’ve downloaded the latest single node VM from Cloudera and Pivotal and made some tests. Results were quite interesting.
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Price deal is a common method to boost sales. Steam push it to an art. There is always a lot of stuff to buy at -50% or even -75%. But is it really so easy? Should you just cut price to increase profitability? How can you really asses the impact of such price deal campaign? Well it’s not that easy and you should rely on analytics to really understand what is happening.
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In those day of huge focus on the Big Data mouvement, it seems that nobody needs a data warehouse anymore but a huge cloud of Hadoop instead. That’s far from the truth. The data warehouse is here to stay as the center of the decision system of any company. Nevertheless, it need to evolve and meet the new challenges. In this post I will introduce a new data warehouse methodology : the modular data warehouse.
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For the end of 2011, many around the blogosphere are forecasting what will be on hype next year. I often read that big data and mobile BI are on the hype. In this article, I will discuss about those technologies. Don’t believe the ads, those technologies aren’t game changer at all.
When deploying a model, one very important thing is to monitor the results. Does it work like you’ve expected? I’m not talking about pre production tests but following the life of your model. I use two kind of reports to do that : preventive reports and corrective reports. As you expect the first one is created just after the prediction and the second is created after the consequence of the prediction is known.
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