Hearing the term OLAP out of the blue can be confusing, but once you understand its meaning and purpose, it becomes much easier to grasp. It is easier to get used to it brings a lot of options and brings new opportunities to get the most out of your data. That’s why I’m writing today about OLAP.
At first, the acronym OLAP means Online Analytical Processing, and here the keyword is Analytical that’s the main difference with OLTP, we will talk about OLTP later in another article. As the name says OLAP is focused on the Analytical part of your data, and that’s not all, the main idea of this is to optimize querying large quantities of Data. Reducing processing time and computational resource consumption. Resulting in saving time and money, and more data availability.
OLAP Cube
One of the main things about using OLAP technologies is using the OLAP cube, which consists on a way or technique to analyze data to look for insights. The cube is a multi-dimensional dataset where some of the data is summarized in a way that brings useful information for businesses.
A main thing in OLAP technologies is having a Fact table that has a star or snowflake schema that has connection with some other tables called dimensions.
OLAP cubes are optimized for complex analytical queries, allowing users to retrieve insights quickly compared to traditional relational databases. Since data is pre-aggregated, queries that would take minutes or hours in a standard database can be executed in seconds.
Analytical Operation
Another important thing in OLAP is the analytical operation you can do. These are:
- Consolidation
- Drill-Down
- Slicing and Dicing
Consolidation
Also called roll-up, this operation aggregates data, making it easier to analyze trends and consume later.
Drill-Down
Lets the user see the details.
Slicing and Dicing
Lets you to specific parts of the data in the OLAP cube and see the data from different viewpoints.
A simple example of this technology that’s even in the Wikipedia Article is grouping all the sales of a store in a table that will be your fact table and having a reference in an id column to another table with the date and time of that sale that would be our dimension table.
By grouping the data in that way, we could see all the sales in general and analyze them, but we can drill down and search and group by periods, by days of the week, etc, which allows us to look for patterns, group them by a way that is useful for us or just present it in a better way in a data visualization tool.
The way that the data is stored in OLAP helps to do this kind of query and bring that sales information, a lot faster and less processing consuming than in an ordinary relational database. Another important thing to have in mind while you are working with OLAP it’s that all the relations that you need between your fact table and your dimensions tables.
Another good thing about OLAP technologies is that it has a better Integration with BI Tools
OLAP cubes are widely supported by Business Intelligence (BI) tools like Tableau, Power BI, and Pentaho. Their structured format enhances data visualization, making it easier to generate meaningful reports and dashboards.
That being said, I really like the OLAP technology and think that could be really really useful for businesses, and it’s a great evolution of the OLTP. Bringing you benefits for your data visualizations, and your final consumers of the data. OLAP is ideal for tracking historical data trends over time. Businesses can compare sales, performance, or customer behavior across different periods, facilitating better forecasting and strategic planning.
Have you worked with OLAP technologies before? Do you think investing in data preparation for OLAP is worthwhile?
Bibliography:
https://www.youtube.com/watch?v=iw-5kFzIdgY
https://en.wikipedia.org/wiki/Online_analytical_processing
https://es.wikipedia.org/wiki/Cubo_OLAP
Top comments (1)
Very interesting reading. Keep up the good work!