Planning a Cluster for Hadoop BigData

This post is about how to plan, for the first time, a cluster for Apache Hadoop and HBase. Hadoop, together with its friends, enable us to elaborate a large amount of data in a cheaply way: by large I mean data large about 100 gigabytes and above.

Hadoop implements the MapReduce framework, that is a way to take a query (or Job) over a dataset, divide it in several queries (or Tasks), and the run these queries in parallel over multiple node of a cluster. Nothing new until now, this looks like the divide-et-impera paradigm: the innovation lies in the fact that the cluster node that is in charge of executing a task has already the data on which process the query. So we are not moving data in order to elaborate them, but we’re assigning task on the right cluster node that already has the data!

Continue reading “Planning a Cluster for Hadoop BigData”