Just a few weeks ago, Apache Hadoop 2.0 was declared generally available–a huge milestone for the Hadoop market as it unlocks the vision of interacting with stored data in unprecedented ways. Hadoop remains the typical underpinning technology of “Big Data,” but how does it fit into the current landscape of databases and data warehouses that are already in use? And are there typical usage patterns that can be used to distill some of the inherent complexity for us all to speak a common language?
Common patterns of Hadoop use
Hadoop was originally conceived to solve the problem of storing huge quantities of data at a very low cost for companies like Yahoo, Google, Facebook and others. Now, it is increasingly being introduced into enterprise environments to handle new classes of data. Machine-generated data, sensor data, social data, web logs and other such types are growing exponentially, but also often (but…
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