Almost all OLAP products provide this simple functionality, but it's almost impossible to find where it is because it is hidden under layers and layers of "added value" in the form of tools to extract, transform and present that data in flashy executive dashboards.
My first advice would be, don't try to build the fancy executive level dashboards, that's the component of the system with the least ROI, in spite of being the most visible for upper level management and thus likely to be the one that is easier to get funding for. The dashboard should be the top of the pyramid, but without a solid foundation it will be an exercise in frustration each time anyone wants to go deeper into any of the numbers.
The problem with these tools is twofold: first, they are targetted at big enterprise buyers that are not the end users. They are strong on fancy and simple features that do not scale well when your analysis becomes real-world complex. Second, all those fancy tools break down under heavy data loads. So in the end "these are my dimensions, these are my facts - now generate my Cube here so I can go datamining" works only during the executive demo.
You're usually better cherry picking up components from these suites that work well enough for you, and building the missing pieces on your own than trying to fully try to shoehorn one of these suites into your needs. Or maybe your needs are simple enough that one of these is good enough for you.
And finally there is the problem with data quality. Perhaps in the startup web-based application world this problem is less relevant, but it's not unusual for large organizations to invest heavily in large OLAP efforts only to find later that they should first have invested heavily in large proces, application and data cleanups. Garbage in, garbage out, no matter how fancy or cube structured the garbage out is.
Which by the way, I think that the cube is a fairly good structure for end users to work with. The only caveat is that have to make sure that they are able to link the data in the cube with the underlying transaction-level entries that make it up.
Let me begin by saying that I likely do not understand even half of what HFT is.
For me, they key part that I fail to understand is exactly what value HFT adds to non speculative market participants. The only explanation I've heard so far is that HFT adds "liquidity" to the market, but I don't get how. Let V be the value HFT adds to non speculative players.
If V is above zero, then allow HFT to continue. If V is zero or negative, my proposal would be not to ban HFT, but to restrict it to transactions made between HFT players. Then let them play in their casino all they want until one player is left. If this suggestion cannot be implemented because HFT needs the rest of the market to make profits, then I highly suspect that V is less or below zero and that HFT is really one of the Casino owners.
My first advice would be, don't try to build the fancy executive level dashboards, that's the component of the system with the least ROI, in spite of being the most visible for upper level management and thus likely to be the one that is easier to get funding for. The dashboard should be the top of the pyramid, but without a solid foundation it will be an exercise in frustration each time anyone wants to go deeper into any of the numbers.
The problem with these tools is twofold: first, they are targetted at big enterprise buyers that are not the end users. They are strong on fancy and simple features that do not scale well when your analysis becomes real-world complex. Second, all those fancy tools break down under heavy data loads. So in the end "these are my dimensions, these are my facts - now generate my Cube here so I can go datamining" works only during the executive demo.
You're usually better cherry picking up components from these suites that work well enough for you, and building the missing pieces on your own than trying to fully try to shoehorn one of these suites into your needs. Or maybe your needs are simple enough that one of these is good enough for you.
And finally there is the problem with data quality. Perhaps in the startup web-based application world this problem is less relevant, but it's not unusual for large organizations to invest heavily in large OLAP efforts only to find later that they should first have invested heavily in large proces, application and data cleanups. Garbage in, garbage out, no matter how fancy or cube structured the garbage out is.
Which by the way, I think that the cube is a fairly good structure for end users to work with. The only caveat is that have to make sure that they are able to link the data in the cube with the underlying transaction-level entries that make it up.