A data warehouse is like a relational database designed for analytical needs. You have your database. You will be storing large amounts of data either in your database or even in the form of flat files. But Users cannot use flat files like excel for storing a large amount of data. There is a limit on how much you can keep in excel. So that is why you store data in some form of the database; for example, a few databases are those of Oracle, my SQL, and Microsoft SQL Server, all these things. So, you will store large amounts of data with all these databases. But the problem is they cannot use for analytical purposes.
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So that is why we must convert or get the data inside a database and get that inside a data warehouse, and when it is inside the data warehouse, then we can perform analysis; that is the advantage. So, the data that once has come into the data warehouse can be used for research to visualise these things because you can look at the data from a different angle. Because the data that comes into the data warehouse is more information than just data.
So, what you have been just data which you can probably view, and you can write data into these places, and you can just view it on a high level, you can view it from the level of every transaction that happened the entire table there are database will get updated. But then, when you want to perform analysis like what happened on this day and how many products were sold during this period, users cannot gain all these things from a database.
So that is why we need to put them all insert the data warehouse. Once it is here, we can form this kind of analysis and get insights from the data warehouse and your business analysts; your data analyst can only use it for research and visualisation. So that is the thing about databases, which is a valuable data warehouse that functions based on OLAP.
OLAP stands for online analytical processing. Analysing queries on Datawarehouse is done based on online analytical processing. If you are running questions on the database, it is called OLTP online transaction processing. Any activity or querying of all these things happens in the data warehouse; it is called OLAP, which is what OLAP is.
Data warehousing
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The central location is where consolidated data from multiple locations are stored. So, you get data in here from various sites from multiple sources. You can get it from one or two databases; you can get it from one or two flat files; you can get all this data if you know you are putting it inside the same table or the same columns and dimensions, then you will be fine because you can get any amounts of data from any source. You can get them all answered inside your data warehouse. The process of converting data inside the database, transforming it into information, storing it in a data warehouse, and then performing analysis. So, these are the entire set of activities that are involved. Data warehousing is the act of organising and storing data in a way to make it retrieved, efficient, and insightful. So, this is the important term here. To make its retrieval efficient and insightful. So, you organise the data and store the data in a data warehouse in a way you can access data at a later point in time. That kind of access should be an easy and efficient way to retrieve that data, and there should be meaning. It should not be the same way you store it inside your database. So, inside the database is the data you collect to get stored inside a database. But the information you get from a data warehouse should not be the same. So, it should serve some purpose, have a meaning, and be for your benefit. So that is the whole point of our data warehouse, so that’s the difference between a data warehouse and a database.
Data warehousing is also called a process of transforming data into information, so relevant data or more processed data is also called information. So, that is what is stored in the central data warehouse, and there is a lot of difference between the data warehouse and the data that is stored insert the database.
OLAP
OLAP is a flexible way for you to make a complicated analysis of multidimensional data. So, when we say multidimensional data, whatever data warehouse has multiple views. So, they will be stored so that you can see from the analysis that we held all the different tables would be linked with each other. So, we differ have rent views are various categories of data and, all these things to perform analysis then you use the OLAP activities. So, the OLAP queries you run on that data and whatever data is stored in a data warehouse is called multidimensional data, and the act of keeping it is in the form of OLAP cubes.
OLTP systems use data stored in two-dimensional tables. So, you will have your rows, and you will have your columns. This is your OLTP system, and you can run on them. But the overlap is it will sort them into OLAP cubes.
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So, there be multiple dimensions, then be numerous views that you can get on the same data concerning the year, to the number of sales, the different products, all these things you can get a single view. So, we call it OLAP cubes and whatever data is stored inside your data warehouse and whichever information, with the help of activities, then that data has called multi-dimensional data.
Data warehousing is modelled on the concept of OLAP. It is stored in a multi-dimensional form and in the form of cubes, and to run your queries on your cubes, you run OLAP queries. The entire process is called OLAP cubes, processing multi-number dimensional data on your OLAP cubes. Databases are modelled on the OLTP, and your data warehouse is modelled on the concept of OLAP, so that’s the critical difference between the base on which these two are modelled. Your OLTP systems use data stored in two-dimensional tables with rows and columns, and your OLAP will have multiple views.
