There are three different types of cubes. The first one is MOLAP, ROLAP, and HOLAP. We also called M-OLAP MOLAP; this stands for multi-dimensional online analytic processing. ROLAP stands for relational online analytical processing, and HOLAP stands for hybrid online analytical processing.
OLAP cubes
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OLAP cubes where it will store your data. Now the analysis that will do the kind of queries you run there will be OLAP queries, which will all be on the multidimensional data. So, the data that is going to be stored inside your cube is going to be multifaceted. But the other multi-dimensional data where they want to get kept.
You can sort your multi-dimensional data in three different types of places. So, there are three different types of OLAP cubes.
MAP
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So the first type is a multi-dimensional OLAP cube. When we say multidimensional OLAP, this is your default type of OLAP cube. So, here MOLAP is the form of OLAP that processes and stores data directly into a multidimensional database. So, you have a multifaceted cube that will store your data inside that database. The advantage here is that it will give you good performance and can perform complex calculations, but the problem is that it can handle only a limited amount of data in your MOLAP. But then there is a difference between MOLAP and ROLAP. Now that is where ROLAP scores MOLAP.
ROLAP
ROLAP stands for relational online analytical processing. Now ROLAP is the form of OLAP that performs dynamic multi-dimensional analysis of data stored in a relational rather than a multidimensional database. This means that in your MOLAP, you have your multidimensional data you will be stored inside a multidimensional database; this will be your OLAP cubes. You have your data that we hold inside of a multifaceted database. Thus, making it multidimensional data, and that is one type of OLAP that is multifaceted OLAP. But relational OLAP converts that multidimensional data into relational data and then store it inside a relational database. So that is what it says here the process of storing data in a relational database, so in your ROLAP, you will have your multidimensional data stored inside your relational database. Then you will run multidimensional analysis and multidimensional queries on a relational database.
So, queries that will be running your data are going to be multi-dimensional. Your queries that will be running will be the same OLAP queries. But the difference is the place where it’s stored. The data in the case of a ROLAP is stored in a relational database. But unlike in the case of multidimensional OLAP, it is stored inside the multidimensional database.
Why ROLAP can be used instead of MOLAP
Because It can process a more significant amount of data in this case, but in the case of MOLAP, there can handle only a limited amount of data at any point in time. But the problem is it requires more processing time and a lot of disc space. Now it needs more processing time because you will convert your multidimensional data into relational data. Once you reverse that, you must store it in a relational database. Now, this is certainly more time-consuming than you’re a multidimensional OLAP.
So, that is one disadvantage with your relational OLAP and the amount of disc space that OLAP will occupy because all these processes will be more significant. Now that is the disadvantage, but that comes with the benefit, so you’re getting something out of using this. So that is the difference between multidimensional OLAP and relational OLAP, which are the two fundamental differences.
HELP
Then you have a third one, which is called a hybrid OLAP. So, your hybrid OLAP is a combination of MOLAP and ROLAP. So, using your hybrid overlap is favourable and advantageous for a fourth. So, the benefit of HOLAP, it can drill through from the cube into the underlying relational data. So, it means you will have your cube here and the underlying relational data. So, you can drill into the relational data using your cube using HOLAP. So that’s the thing about the HOLAP, which uses the best features of both your multi-dimensional OLAP and your relational OLAP. These are the three different types of overlap cubes.
OLAP Operations
Roll up, drill down, slice, dice, and Pivot. We can do these five different overlap operations on our dimensional data.
Roll Up:
This is to know what kind of operations you can do on your warehousing and what you can do on your database. So, it will happen that since data is stored in such a multi-dimensional fashion, it can perform these kinds of options. Roll-up is something that forms aggregation or data cubes by either climbing up a concept hierarchy for a dimension or dimension reduction. It means in a particular measurement.
Drilldown:
Drill down is something that is just the reverse of rolled up. So, what we did in Roll up was we aggregated a set of attributes. So, let us break down the entire feature into more minor details. So, we can do that by stepping down a concept hierarchy for a dimension and introducing a new dimension.
Slice:
Slice operation provides a new sub-cube from one dimension in each cube. What this means is that any cube of ours will have three dimensions. So, we have a Z-axis, have Y-axis and all X axes. So, three different sizes and what the slice operation means is with the help of three different dimensions, and we can use one of the dimensions and break it down into a two-dimensional cube.
Dice:
Dice operation provides a new sub-cube from two or more dimensions in each cube. In the earlier example, we saw slice, slice. It gives us a new sub-cube from one measurement in each cube. But Dice here gives us new sub-cubes from two or more dimensions in each cube.
Pivot:
Then finally, we have one operation called pivot operation. So, the pivot operation is also known as the rotation operation. It transposes both the axis, whether the X&Y axis transposes them to provide an alternative presentation of data.
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