It seems any time any one mentions Big Data on the web these days, the conversation inevitably turns to Hadoop. And why not, there’s a lot of [elephant poop](https://www.sandtechnology.com/hadoop-elephant-room/) and while the [philosophy and approach can be argued](https://www.sandtechnology.com/hadoop-revisited/), it’s important to remember that the reason you have so many choices…

It would be fantastic if there were only one marvelous multipurpose database to fit each and every job, one super application that was the best at transactions, operations, warehousing, reporting, and analytics, with beautiful eye candy, reality paints a different picture. But in the real world, real users know it…

Denormalized data models provide increased performance and ease of use. Most documentation about denormalization is taken from the view of the row-based data store, not the column-based data store. Row-based data store environments may encounter negative aspects when using denormalization, such as – Using more disk space – Being required…

The first item you need to consider is why choose a column store over the standard row databases such as Oracle or DB2, or even the data partitioning varieties such as Teradata or Netezza. ###1. Do you need a column store? Generally, row-oriented architectures are geared towards OLTP-type workloads which,…