Column database vs OLAP
OLAP (Online Analytical Processing), Reporting, Data mining related tasks are usually done by Business intelligence products. They do powerful Extraction, Transformation and Loading (ETL) the data and provides various reports. They use relational database as its back end. How could they generate better reports? Will column DB do a better job?
Business Intelligence and data mining products are available over the years. They are proven and use different techniques to solve the problem. Basically they aggregate data and store in such a way that they could able to perform multi dimensional (MDX) queries. The only drawback is they aggregate the data and it requires more disk space and processing power. To get scheduled reports, the aggregation task should be carried out at scheduled interval (frequent interval). They have good GUI where drag and drop fields will produce good charts.
Column database stores every single column separately. This helps to do better analytics without doing aggregation. It is capable to produce real time reports. Column database is more flexible and dynamic. Developers need to write some code to give better visualization reports where as in case of any BI tools this would be automatic. Not much programing skills or SQL knowledge required.
The best solution would be combining both the ends. An OLAP sever with column database as back end will give benefits of both the worlds.
See also: Open source column-oriented database
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Column oriented database or datastore as the name sounds it stores the data by column rather than by row. It has some advantages and disadvantages over traditional RDBMS. Developer should know the typical situation to choose column oriented database.
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