The group at Yahoo! that I came from was using Hadoop for data analytics and data warehousing. We had something like 100,000 web servers across the world, and once we collected data from across all these servers, we dumped it into Hadoop, which became the place where we stored all of the data, instead of traditional network storage. Our reasoning for doing that was a matter of economics, given the quantity of hardware. Hadoop lets us scalably process that data, clean it up, and normalize it so we could pass it along to the systems that need it. Hadoop is getting very wide adoption in the data warehousing and business intelligence domains. One of the biggest uses within Yahoo! right now is dealing with all of the log information from servers. Analyzing that information allows for better spam filtering, ad targeting, content targeting, A/B testing for new features, et cetera. It’s not web-specific. For example, everybody does data warehousing, and we see very strong adoption there. Separate from that, your example of oil companies is a very good one, as is the financial sector. Right now, we do have a couple of very large financial institutions working with us on these exact problems, taking huge amounts of data from domains like credit card processing and building predictive models for fraud that enable better decisions, for example, about whether to block or allow a given transaction. In the stock market, Hadoop is being used to do simulations that help predict option pricing and related problems. That’s another very healthy market that we’ve seen growth in. via howsoftwareisbuilt.com Knowing that Yahoo...
First, it’s worth making the important clarifying point that Hadoop is not a database. Hadoop is a data processing system, and in fact, I would even go as far as saying Hadoop is an operating system. The core of an operating system boils down to a file system, the storage of files, and a process scheduling system that runs applications on top of these files. There are many other components that help with devices, credentials and user access, and so on, but that is the core. Hadoop is exactly the same thing. The core of Hadoop is the Hadoop Distributed File System, which is a file system that’s runs across many nodes. It links together the file systems on many local nodes to make them into one big file system. Hadoop MapReduce is really the job scheduling system that takes care of scheduling jobs on top of all those nodes. That is the key distinction between Hadoop’s approach and that of database systems. Hadoop, at its heart, does not require any structure to your data. You can just upload files directly from anywhere, like a web server, RFID device, or cell phone mobile device, directly into Hadoop. They could be images, videos, or just a bunch of bits. They don’t have to have a schema with column types and so on, which gives you tremendous agility and flexibility. Hadoop has a very nice model that I sometimes refer to as schema on read. Whereas defining your schema as you’re writing the data in limits what you can put in by requiring it to be conformant to the schema that...
Mike, Nguyen Dinh Hai, has provided excellent support to Singapore Safety Driving Centre Ltd as our contract vendor programmer. Some of the projects he had completed for us: Recovery of data when our database server failed in 2008 Upgraded two of our servers in 2008 & 2009. Upgraded request changes in program functions according to our specifications Wrote a program and implemented wireless loading and updating of student’s progress Using the IPOD touch. Maintenance of SSDC website (www.ssdcl.com.sg) He has shown great abilities and knowledge to get things done in the most challenging and difficult period even at odd hours. Joe Lim Asst Manager Singapore Safety Driving Centre Ltd Dated: 27 May...
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