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    <p>Before you settle on a storage method, the question is what type of analysis you want to do.</p> <p>For aggregation oriented workloads and the volume you're talking about, a traditional rdbms like oracle, sql server or postgresql running on a beefy server should do. They have native support for partitioning and other DWH techniques (such as materialized views) which will save you the time of cobbling it together yourself. For example the oracle query optimizer will take into account partitioning when generating a new query plan.</p> <p>As reporting front-end you can go for one of the commercially available ones or create your own. Some options are obiee, SQL server reporting services, cognos and pentaho (free) They all support cross-db reporting (combining DWH + operational store) to some extent.</p> <p>If you need instant answers for arbitrary queries involving aggregations on large volumes (billion row datasets) you could look into teradata, netezza, vertica and the like. These tend to cost quite a lot.</p> <p>If you often want instant answers for arbitrary queries involving aggregations on smaller datasets, look into <a href="/questions/tagged/qlikview" class="post-tag" title="show questions tagged 'qlikview'" rel="tag">qlikview</a>. They have a powerful in-memory analysis tool. I believe it's free for single-person usage.</p> <p>If it's not simply a matter of adding up numbers but analyzing complex relationships (graph like analysis) on large volumes, you're out of luck. Old solutions don't scale well or are expensive, new ones are often hit and miss. It's going to be expensive either way. Without knowing how you want to correlate events, it's hard to recommend anything. I'm not aware of any general solution.</p> <p>Personally, I'd go with postgres (backend) + pentaho and <a href="/questions/tagged/qlikview" class="post-tag" title="show questions tagged 'qlikview'" rel="tag">qlikview</a> (both front-end) with kettle for traditional ETL and hadoop or custom code to precalculate results for more complicated analysis. In postgres split up your data in an operational store and a DWH. </p>
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