data-warehouse

I was asked by a customer what the term "data warehouse" really means. I thought about ETL, details of the data model, differences to NoSQL, Clouds, 'normal' DBMS, MDM (Master Data Management) etc. but wasn't able to describe the term in a few words to him... (In fact I did some talking and left him un-illuminated.) How can "data warehouse" described in 1-3 (or a bit more) sentences? For non technical guys the best is to describe it as "Huge ammount of data stored in a specialized computer system. Data is usually related to some specific domain and whole system is designed to be fast and

Can anyone explain what is really distinction between Data Warehouse and OLAP Cubes? Are they different approach for same thing? Is one of them deprecated in comparison with other? Are there any performance issues in one of them? Any explanation is welcomed A data warehouse is a database with a design that makes analyzing data easier† (often with data from multiple sources). It is usually composed of fact tables and dimension tables, and often aggregate tables. OLAP is a set of operations that one can do on a data set, such as pivoting, slicing, dicing, drilling. For example, one can do OLAP

I'm about to start a new project which should have a rather large database. The number of tables will not be large (<15), majority of data (99%) will be contained in one big table, which is almost insert/read only (no updates). The estimated amount of data in that one table is going to grow at 500.000 records a day , and we should keep at least 1 year of them to be able to do various reports. There needs to be (read-only) replicated database as a backup/failover, and maybe for offloading reports in peak time. I don't have first hand experience with that large databases, so I'm asking the ones

How to store records that don't contain any fact? For example, let's say that a shop wants to count how many people have entered inside a store (and that they take info on every person that goes inside the shop). In warehouse, I guess there would be dimension table "Person" with different attributes, but how would fact table look like? Would it contain only foreign keys? As you described it, that would be just a fact table. Actually, there is name for this -- factless fact table ; fact table without any measures. It is quite common for recoding events. Essentially anything that records: who,

I am building an analytics database (I have a firm understanding of the data and the business objectives and only basic-to-moderate database skills). I have come across some references to building similar warehouses which implement the concept of 'calendar tables'. This makes sense and is easily enough done. Most examples I see, however, are calendar tables that limit scope to 'day'. My data will need to be analyzed down to hour-level. Possibly minutes. My question: would an implementation of calendar tables for hour/minute-level granularity be of value in terms of space-efficiency and query

I think the question is clear enough. Some of the columns in my datawarehouse table could have a relationship to a primary key. But is it good practice? It is denormalized, so it should never be deleted again (data in datawarehouse). Hope question is somewhat clear enough. I have no idea. But nobody is answering, so I googled and found a best practises paper who seem to say the very helpful "it depends" :-) While foreign key constraints help data integrity, they have an associated cost on all insert, update and delete statements. Give careful attention to the use of constraints in your