Decoding The Roles Of OLAP And OLTP

Every second, someone makes a payment, checks into an app, or resets a password. These routine events rely on systems that must be both lightning fast and reliable.

Behind the scenes, two types of data operations; OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing), mainly power everything from real-life transactions to long-term trend forecasting.

“Understanding the differences, trade-offs and how they integrate is essential for building a robust data platform,” says Tafida Balarabe, a Data Analyst at Revolut.

 

OLTP Explained

 

OLTP operations are everywhere, whether you are booking a cab, checking out at a store or transferring money through an app. “Most people interact with OLTP systems daily without even realising it,” says Tafida Balarabe.

These systems are built to handle large volumes of short atomic transactions that must happen quickly and accurately. At their core, OLTP systems capture and process real-world events.

He explains that OLTP systems are designed to support basic operations like Create, Read, Update, and Delete (CRUD). These are simple in structure but happen at extremely high volumes.

“Speed is critical. You’re talking about systems that need to process transactions in milliseconds, often under heavy load,” he says.

To achieve that level of performance, Tafida notes, OLTP systems typically rely on relational databases like PostgreSQL or MySQL. “They’re optimised to keep data clean and compact, and to avoid redundancy.”

However, just being fast is not enough anymore. Data also has to be correct. “OLTP systems must be ACID-compliant, as it ensures that each transaction is processed reliably, even when there are thousands of them,” the expert adds. “Atomicity, consistency, isolation, and durability are why you can trust the correctness of your account balance after a transfer.”

Tafida also clarifies why OLTP operations are relevant for data analysts. “They serve as a source of truth, providing raw transactional data,” the expert says. Data analysts frequently query these systems to monitor operations and quickly identify and troubleshoot issues that arise.

However, OLTP operations have significant limitations. For instance, running complex analytics queries on them can be slow, and OLAP operations are better for this purpose.

OLAP Explained

 

They are designed for complex data analysis, reports, and decision support. The main difference between OLTP and OLAP, as the data analyst says, is that the latter can process large volumes of data from multiple sources. OLTP and OLAP help understand customer behavioural patterns over time, identify market trends, and predict financial risks.

“There is a large difference between OLAP and OLTP operations that data analysts need to keep in mind,” Tafida points out. These are the characteristics of the latter:

1. They can perform more complex queries like drill-downs, roll-ups, slicing, and dicing.
2. Multidimensional models are used, including those that consider time, region, and product.
3. For speed, query performance depends on the engine (e.g., MPP databases, in-memory cubes).
4. Substantial data storage – terabytes and petabytes. OLAP systems often use columnar or multidimensional storage, which can be very large.

Similar to OLTP, OLAP are crucial for data analysis. “I apply them to deepen the analysis of customer segments, product usage, and market trends,” the data specialist says. OLAP operations are also helpful for building dashboards and creating predictive models.

Building on the previous points, OLAP analysis directly impacts the company’s long-term strategy, particularly in product development, marketing campaign efficiency, and overall business growth.

 

How They Complement Each Other

 

“The operations complement each other and are essential to my work”, the expert notes, highlighting the necessity of both. Data analysts use OLTP systems for daily transactions (payments, transfers, account updates).

They perfectly fulfil the need for speed, accuracy, and high concurrency. Then, the data from OLTP systems is extracted, transformed, and loaded into an OLAP system, such as a data lake, to structure it for further analytical queries.

OLTP systems often have stricter access controls and are more sensitive to data consistency, whereas OLAP may have aggregated/anonymised data.

 

Emerging Trends

 

New databases combine transactional and analytical processing into a single hybrid model, one of which is SAP HANA. They are designed to provide analytics on live transactional data and do not require a separate OLAP storage.

However, the system is highly complex and costly and has yet to be widely deployed.

 

Implementation Tips

 

Tafida also shares tips for implementing OLAP and OLTP databases into workflow:

 

1. Choose the proper tooling – Transactional DBs, such as PostgreSQL and MySQL, are suitable for OLTP. For OLAP, choose warehouse options such as Snowflake or Redshift.
2. Choose the proper hardware – low-latency SSDs for OLTP, and storage and compute clusters for OLAP.
3. Consider Hybrid Transaction/Analytical Processing (HTAP) – for real-time insights without needing separate systems, but weigh the costs and complexity beforehand.

Next time you design your data architecture, consider the distinct purposes of OLAP and OLTP systems. OLTP systems record and manage individual transactions instantly as they occur.

Data analysts need it for immediate problem-solving. On the other hand, OLAP is an analytical tool for receiving insights over time. This contributes to growth and long-term decision-making.

Tafida adds, “Revolut’s data analysts are proficient in SQL for querying both systems and know when to query each.” With this knowledge, he concludes that they can select the most suitable approach for their analytical needs.