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 form of flat files. But flat files like excel cannot be used for storing a large amount of data there is a limit on how much you can store in excel. So that is the case why you store data in some form of database, example a few databases are those of Oracle, my SQL, Microsoft SQL Server all these things. So, you have all these databases you will store large amounts of data. 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 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 analysis for visualisation or 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 rather than just data.
So, what you have you 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 an analysis like what happened on this day and how many products were sold during this period all these things cannot be gained from a database.
So that is why we need to put them all insert the data warehouse and 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 they can only use it for analysis and visualisation. So that is the thing about databases and that is a useful data warehouse, and it functions based on OLAP.
OLAP stands for online analytical processing. So, the whole process of doing analysis or running queries on Datawarehouse is done on the basis of online analytical processing. That is one of the activities that is done. So, if you are running any queries on the database then it is called OLTP online transaction processing. But any kind of activity or querying and all these things when it happens in data warehouse it is called OLAP so that is what an OLAP is.
Basically, the centre location where consolidated data from multiple locations are stored. So, you get data in here from multiple locations from multiple sources. You can get it from one or two databases, you can get from one or two flat files, you can get all this data if you know you are putting it inside the same table or inside the same columns and dimensions then you will be fine because you can get any amounts of data from any source, and you can get them all answered them all inside your data warehouse. The process of converting data inside the database, converting into an information and storing that into 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 retrieval, efficient, and insightful. So, this is the key 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 of time and that kind of an access should be easy and should be an efficient ways table to retrieve that data and there should be a meaning out of that data. It should not be the same way that you store inside your database. So, inside the database is the data that you collect so you can get store inside a database. But the data that you get from a data warehouse should not be the same. So, it should serve some purpose it should have a meaning and it should 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 that 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 is a flexible way for you to make a complicated analysis of multidimensional data. So, when we say multidimensional data than whatever data warehouse has multiple views. So, they will be stored in such a way that you can from the analysis that we stored all the different tables will be linked with each other. So, then we different views are in different 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 storing it is in the form of OLAP cubes.
OLTP systems use data stored in form of two-dimensional tables. An example of this would be any excel. So, you will have your rows and you will have your columns. This is your OLTP systems and any queries you can run on them, and you can do all these things. But the difference with the overlap is they will be sorted form of OLAP cubes.
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So, there be multiple dimensions then be multiple views that you can get on the same data with respect to the year, to the number of sales, with respect to the different products all these things you can get a single view. So, we call it OLAP cubes and whatever data that is stored inside your data warehouse and whichever data with the help of activities then that data has called us multi-dimensional data.
Data warehousing is modelled on the concept of OLAP. It is stored in a multi-dimensional form, and it’s stored in form of cubes and to run your queries on your cubes you run OLAP queries. the entire process is called OLAP cubes and 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 key difference between the base on which these two are modelled and your OLTP systems use data stored in two-dimensional tables with rows and columns and your OLAP will have multiple views.