Have you ever heard of terms like Snowflake, Star Schema, or Data Vault in relation to OLAP modeling? If so, you’re on the right track. But what exactly is OLAP, and why is it so important for your business data management?
OLAP, or Online Analytical Processing, is a data processing approach that enables complex analyses for informed strategic decisions. On the flip side, OLTP, or Online Transaction Processing, is designed to manage a company’s day-to-day operations and transactions.
Now, you might be wondering how to navigate between OLAP and OLTP for your data processing requirements and when to employ each. Our experts are here to guide you through these considerations in the following paragraphs.
Understanding the Differences Between OLAP and OLTP for Data Optimization
The objective of Business Intelligence (BI) is to capitalize on a company’s data to extract valuable, decision-making insights. This follows a well-defined process, which you can explore in another article.
Selecting the Right Approach: OLAP vs. OLTP
All data resides in database systems, but its usage determines its typology. For example, sales software, internal ERPs, or personal banking apps rely on OLTP databases for Online Transaction Processing.
On the other hand, data scientists or Business Intelligence analysts prefer OLAP databases for Online Analytical Processing.
The main difference between the two lies in the way the data is processed. An OLTP database will be very efficient for managing fast transactions. For example, when you make a bank transfer, your priority will be to ensure that the amount you send arrives on time and to the right recipient.
In contrast, OLAP databases shine in providing aggregated information quickly, requiring minimal manipulation. For example, you can quickly determine the total amount your customer X generated during the month of January, specifically in the European geographic zone.
Distinguishing between these two approaches couldn’t be simpler. If your priority is rapid transaction processing without analytics, OLTP is your choice. On the other hand, if you aim to extract aggregated information quickly, OLAP is the solution.
Technical Comparisons Between OLAP and OLTP
Beyond their primary objectives, OLAP and OLTP databases diverge in their internal mechanisms. Here’s a breakdown of the key components of each system across various axes:
| Axis | OLTP | OLAP |
| Objective | Fast transaction processing | Analysis of complex aggregated data |
| Data Volume | Gigabyte of data | Terabyte or petabyte of data |
| Data Source | Operational data, custom software, ERP | OLTP, other internal sources such as images, sounds, videos |
| Response Time | Milliseconds | Seconds |
| Data Structure | Relational | Multidimensional |
| Schemantic | Normalized | Highly denormalized (Snowflake, star schema) |
These are the primary differences between OLAP and OLTP databases. As highlighted, each system serves distinct purposes: OLTP excels in processing daily transactions quickly and efficiently, while OLAP specializes in delivering detailed analysis capabilities.
Efficient use of OLAP and OLTP
You now have a thorough understanding of both concepts. Let’s explore a few examples that highlight the importance of choosing the right approach based on your context and current data management objectives.
Note that these examples are for informational purposes only and may not accurately reflect your specific situation. We recommend consulting a qualified professional for personalized advice tailored to your individual needs.
Real-Life Application of OLTP
Let’s take the example of a supermarket that uses PoS (Point of Sales) management software to process customers purchases. This PoS system would maintain a database with the following attributes (tables):
- Clients: Contains detailed customer information.
- Products: Lists all products sold by the supermarket.
- Product Category: Classifies products into different categories.
- Orders: Records every purchase made by customers.
- Order Details: Provides detailed information on customer orders, such as all the specific items purchased in a customer’s first order.
- Addresses: Stores customers’ current addresses to facilitate delivery and invoicing.
The OLTP database for this supermarket would therefore look like this:
Example of OLAP Analysis
Let’s imagine that this same supermarket now wants to analyze its customers’ transactions to understand their spending habits and better meet their product needs. Intuitively, one might ask: why not access the OLTP database directly to perform this analysis?
However, if this supermarket has a portfolio of tens of thousands of customers, thousands of products, and processes a few thousand orders daily, that’s a vast amount of data. If business intelligence analysts decide to fetch information directly from the OLTP database, they risk significantly slowing down the point-of-sale (PoS) system due to the sheer volume of data involved. This slowdown could impact cashiers’ operations.
Moreover, the structure of OLTP databases does not facilitate quick information extraction without substantial data processing. This is partly because of the need for data cleansing and the complexity of relationships between various attributes, which can lengthen query times. For example, to obtain details of a customer’s purchases, you need to navigate through four different tables: customers, orders, order details, and products.
Here are just a few of the reasons why an OLAP model would be more appropriate:
- ETL: Before we get to OLAP, we typically perform an ETL (Extract, Transform, Load) process. This involves copying data from OLTP to dissociate the two systems.
- Data Modeling: This copied data can then be used for modeling to overcome the second problem. It involves grouping entities such as orders and order details, customers and customer addresses, and products and product categories.
- Model Simplification: The result is a simplified model that is more optimal for analyzing large volumes of data.
This transformation is called denormalization. The final diagram above is an example of a star schema. However, other schemas exist, such as Snowflake or Data Vault, depending on the problem you’re trying to solve.
Optimize Your Data Analysis with OLAP
Transitioning from OLTP to OLAP is more complex than the example used in this article. It is a lengthy process that requires in-depth analysis to clearly define the objectives of the change.
At Uzinakod, our business intelligence experts can support you throughout the entire BI value chain: from requirements gathering and definition to ETL, data modeling, and reporting. Contact them to start your shift to OLAP today.
