OLAP and OLTP often confused with one another.
So what's the difference?
Analytical and transaction, as in online analytical processing and online transaction processing.
That's it.
That's the difference.
But hold up!
Don't go just yet!
I have three boxes to fill, because when it comes to using data to make smarter decisions,
it's not a question of choosing between OLAP and OLTP.
It's a question of how to make the best use of both processing times for your situation.
Within the data science field, OLAP and OLTP are two types of data processing systems.
One uses data to gain valuable insights, while the other is purely operational.
So let's start by defining OLAP, or On-Line Analytical Processing.
It's a system for performing multi-dimensional analysis at high speeds on large volumes of data.
And where do these large volumes of data come from?
Typically from a data warehouse, a data mart or some other centralized data store.
OLAP is ideal for tasks such as data mining, business intelligence, and complex analytical calculations.
And is also well-suited to business reporting functions like financial analysis, budgeting, and sales forecasting.
Now, the core of most OLAP databases is the OLAP cube.
The OLAP cube.
Beautiful, isn't it?
The OLAP cube allows you to quickly query, report on and analyze this multi-dimensional data.
And what is a data dimension?
Well, it's simply one element of a particular data set.
So, for example, sales figures might have several dimensions related to region, time of year, and product models.
And the OLAP cube extends the row-by-column format of a traditional relational database schema and adds layers for other data dimensions.
So, for example, while the top layer of the cube might organize sales by region,
data analysts can also drill down into layers for sales by state or city or specific store.
So that's OLAP.
What about OLTP?
That's On-Line Transaction Processing.
And it enables the real-time execution of large numbers of database transactions by large numbers of people.
OLTP systems are behind many of our everyday transactions, from ATMs, to in-store purchases, to hotel reservations.
OLTP can also drive non-financial transactions, including password changes and text messages.
In fact, my very first job involved working with an OLTP system.
OLTP systems use a relational database that can do a bunch of things.
For example, process a large number of relatively simple transactions.
For doing things like insertions, updates and deletions to data.
And to do this with rapid processing with response times measured in milliseconds.
They also enable multi-user access to the same data while ensuring data integrity and provide indexed datasets for rapid searching, rapid retrieval, and querying.
So OLAP does all of the infrastructure work.
Important stuff.
It's just not as pretty as that OLAP cube.
Okay, now can you see how we can combine these two?
In reality, many organizations will use OLTP systems to provide data to OLAP.
And that's the difference between them.
OLAP is optimized for conducting complex data analysis and OLAP systems are designed for use by data scientists, business analysts, and knowledge workers.
OLTP, on the other hand, is optimized for processing a massive number of transactions.
OLTP systems are designed for use by front-line workers like cashiers, bank tellers, and hotel desk clerks, or for customer self-service applications.
Choosing the right system for your situation depends upon your objectives.
Do you need a single platform for business insights?
OLAP can help unlock data from vast amounts of big data that you have stored.
Or do you need to manage daily transactions?
OLTP is designed for fast processing of large numbers of transactions per second.
If you need to do both, well, most of the time organizations use both OLAP and OLTP.
In fact, OLAP systems may be used to analyze data that leads to business process improvements in OLTP systems.
And ultimately, yes, also create more of those fancy looking cubes.
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