In an increasingly digital world, the number of transactions that companies need to process as quickly as possible to avoid long waiting times for their customers is growing. Online transaction processing, or OLTP for short, is a type of data processing that enables a large number of transactions to be processed in real-time. These systems are essential for various economic sectors, such as e-commerce companies, the financial sector or healthcare. These systems are designed to process numerous and simultaneous user requests while ensuring maximum user integrity.

In this article, we look at the unique requirements of transactions and how they can be processed using OLTP. We’ll dive deep into architectures and their characteristics before we can talk about the pros and cons of these systems.

What is Transaction Data?

Transaction data is information that is generated in the environment of organizations and companies. In many cases, these are so-called business transactions, for example, incoming or outgoing payments, new orders, or the purchase of raw materials. These transactions are created every time a user interacts with an OLTP system and are used to document and track business processes.

Transactions are indivisible and consistent. These properties also coincide with the so-called ACID criteria of databases. Indivisibility (or atomicity in the case of databases) means that the transaction is either executed completely or not at all. In other words, they are either carried out or not carried out at a certain point in time. Consistency (or consistency in the case of databases) describes the property that a transaction always leaves the system in a consistent, i.e. valid, state.

These ACID criteria must also be guaranteed by the databases that store the transactions. For this reason, only relational databases can often be used, as many NoSQL databases are not consistent. Transactions also have the following characteristics:

• Dynamic: The transactions constantly change as new transactions are added in real-time.
• Detailed: Each transaction contains a lot of detailed information, such as a timestamp, the parties involved, the items in the shopping cart, or the amount.
• Short-lived: Transactions are often only in the operational systems for a short time before they are stored in data warehouses or other data archives for the long term.
• Structured Form: The information in a transaction usually follows a predetermined pattern, so it is structured data that can be stored in relational databases.

Transactions occur in a wide variety of companies and are not only found in orders, such as credit card payments, e-commerce orders, or returns. In the healthcare sector, for example, transactions are also used for patient admissions and the dispensing of medication. In logistics, on the other hand, possible stock movements are also referred to as transactions.

What are the Characteristics of Transaction Data?

Transaction data is treated as a special type of data because it has specific characteristics that distinguish it from other data records. Its structure, processing, and storage are subject to high requirements, as it plays a central role in business-critical processes.

These characteristics include

• Normalization: Transaction data is usually highly normalized. This means that it is structured in such a way that redundancies are avoided and inconsistencies are ruled out. This is the central data of companies, which is why errors must not occur, as these can have expensive consequences in the real world. This is implemented in concrete terms by splitting the data into different tables that are related to each other.
• Consistency: The consistency of transaction data is an essential component that ensures that all data within a system remains in a valid and consistent state. For example, the amount of a bank transfer must not be debited twice or even lost. Compliance with the ACID principles ensures that transactions are processed correctly.
• Data Model and Structure: In many cases, transaction data is highly structured so that it can be processed quickly and easily in operational systems. Because of this structure, it can be easily stored in tabular databases, such as relational databases.

Due to these characteristics, transaction data is an essential basis for companies and enables real-time data processing. This allows business processes to be controlled efficiently, customer interactions to be managed reliably and an error-free database to be created for analyses.

What is OLTP?

Due to the peculiarities of transaction data and their high demands on the databases used, Online Transaction Processing encompasses all tools that are specialized for the real-time data processing of transactions. An OLTP system enables several users to carry out small and frequently occurring transactions at the same time, such as booking a flight ticket or making an online payment.

In many cases, these are relational databases, as they meet the high requirements of transactions in terms of consistency and normalization. At the same time, relational databases can usually process many queries simultaneously. However, there are also a few NoSQL solutions that can be used for OLTP.

Online Transaction Processing Systems are characterized above all by the following points:

• Volume: The volume of users who want to query a large amount of data at the same time is very high. Particularly in large companies, orders are processed by a large number of people. The system must be able to cope with these high loads.
• High Availability: The database cannot afford any downtime, as this has expensive consequences in real life, such as lost sales.
• Simple but frequent data modifications: The type of data modification in OLTP systems is usually very similar and new data records are added, such as new orders, or existing data records are changed, for example, because there was a return. However, these modifications can occur very frequently during peak times, especially in e-commerce stores.
• Fast Response Times: The systems are also used in productive operations and must therefore be able to guarantee short response times. For example, if a customer wants to view their previous orders in an online store, the query should be quick so that the website loading time is short. Otherwise, a long loading time will be perceived by the customer as poor website quality.
• Integrity: As we have already explained, transactions require a high degree of consistency to guarantee the truthfulness of the data.

An online transaction processing system is structured in three different levels. The transactions are stored at the lowest level, the so-called data storage level.

The business logic level checks whether the transactions are correct and comply with previously defined rules. This is also where it is determined whether all the necessary requirements have been met for a transaction to be completed. Finally, the presentation level represents the front end that the user sees.

How are OLTP systems structured?

OLTP systems are structured in such a way that they can process a large number of simultaneous queries quickly and reliably. To this end, their architecture consists of several layers that together process the user queries, store data, and execute the business logic.

The main components of an OLTP system include

• Database: The central component in which all data is stored and managed. To offer ACID conformity, relational databases are usually used here and systems such as MySQL or PostgreSQL are used.
• Middleware: This intermediate layer mediates between the database and the user interface. This is where the business logic can be implemented, database queries optimized, and secure communication between the layers established.
• Frontend (User Interface): With this interface, users can interact with the system and carry out transactions. This can be a web application, a mobile app, or desktop software. This is where the transaction data is received and subsequently processed.

As a rule, OLTP systems consist of a so-called client-server architecture, which can consist of two or more layers, depending on whether the system needs to be scalable and what complexity can be mapped:

• 2-Tier Architecture: in this arrangement, the client communicates directly with the database and writes or reads to it. This architecture can be optimal for small companies, but it is not very scalable and can only map simple logic.
• 3-Tier Architecture: In this architecture, an additional middleware layer is added that mediates the business logic between the client and the database. This makes the system more scalable and also more secure, as no direct access to the database is possible.
• Multi-Layer Architecture: In large OLTP systems, additional layers are built so that load distribution can take place and performance is also improved. As a result, availability is higher, and more simultaneous queries can be processed.

Finally, OLTP systems also differ in the way they are hosted:

• On-Premises: To maintain maximum control over their data and to be able to guarantee security, companies operate their OLTP systems on their local servers. This is often the case in banks or other financial companies that have to meet high standards for data security.
• Cloud-Based: More and more companies are also turning to cloud-based databases, such as Amazon RDS or Microsoft Azure SQL, to take advantage of full flexibility. If more performance or storage space is required, more hardware can simply be booked and added and then scaled back again as soon as this peak load is over. This optimizes operating costs, as you only have to pay for what you use.
• Hybrid Models: These models combine an on-premises architecture with cloud-based technologies to take advantage of both infrastructures. This allows sensitive data to be kept locally while at the same time taking advantage of the cloud’s scalability and backup capability.

The architecture of OLTP systems is the key factor influencing performance, scalability, and security. The latest developments mean that cloud-based solutions and distributed databases are increasingly being used.

Which Applications use OLTP Systems?

OLTP systems are indispensable in many industries as they enable transactions to be processed and thus generate profits for the company. In general, they are used whenever user interactions need to be stored, processed, and updated. The most typical areas of application are described in more detail in this section.

Banking and Financial Transactions

In the financial sector, a large number of transactions occur every day, such as bank transfers, card payments, or other account updates. Each of these processes must not only be carried out quickly but above all securely to avoid errors, which can quickly become very costly. In addition, high-security standards and data integrity must be guaranteed so that no outsiders can access the data.

E-Commerce Platforms

Online stores and marketplaces rely on managing orders, payments, and stock locations in real-time. Every action that the user performs on the website or in an app must be processed immediately in order to offer the customer a good user experience with few interruptions. In addition, the customer and their orders must also be trackable over a longer period so that subsequent transactions, such as returns or complaints, can be processed accordingly.

Ticket Booking System

Ticket booking systems are used in a wide variety of industries, such as airlines, railroads, cinemas, and other events. The transactions must be processed in real time so that there are no accidental double bookings and at the same time the availability for other users is updated quickly.

CRM and ERP Systems

Many companies use Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP) systems to manage interactions with customers and the flow of goods. OLTP systems are used to update customer data, change stock levels or manage other business processes efficiently.

These examples show that OLTP systems play an important role in many industries and have become an integral part of today’s business world. Thanks to their high-speed, parallel processing, and data integrity, they enable the efficient and secure implementation of business processes.

What are the Disadvantages of OLTP systems?

Online transaction processing is essential for many companies and their business processes. However, these systems also have disadvantages and challenges that have to be dealt with, especially with high transaction volumes.

1. High Infrastructure Costs: For transactions to be processed quickly, companies need high-performance hardware with fast networks to meet the high requirements. However, these usually involve high initial investments for servers and storage systems and also lead to costs for operation and maintenance. This can be remedied by partially outsourcing the systems to the cloud, which means that no initial investment is required and only monthly costs are incurred for use.
2. Complexity of Scaling: Due to the requirements for data consistency, scaling is significantly more complex than with other systems. Vertical scaling, i.e. improving the hardware of the system, is usually cost-intensive and limited by the performance available on the market. Horizontal scaling, on the other hand, in which new servers are added to distribute the load, is very complex for OLTP systems, as it must be ensured that data consistency is maintained during parallel processing.
3. Performance Bottlenecks due to Transaction Locks: As OLTP systems rely on the ACID properties, so-called lock conflicts often occur with high, simultaneous loads, as a change process must first be completed before a new one can be started. In the worst-case scenario, several users may want to access the same data record at the same time and create a deadlock so that they block each other.
4. High Demands on Data Consistency and Availability: As OLTP systems operate in real-time, they must guarantee data integrity and availability. A high level of system availability must therefore be ensured, as even minor interruptions can lead to data loss or incorrect transactions. For this reason, special attention must be paid to preventing network and server failures.
5. Security Risks and Data Protection Requirements: OLTP systems are the heart of a company and often process sensitive information such as customer or financial data. They are therefore a popular target for hacker attacks and other unauthorized access from outside. For this reason, there is a high need for protection and the company must ensure that the standards are kept up to date at all times. In addition, other legal requirements, such as the GDPR or HIPAA, must be complied with.

Overall, OLTP systems offer a powerful infrastructure for real-time data processing. However, these systems also face high costs, scaling issues and other security requirements, so they need to be constantly optimized and kept up to date.

OLTP vs OLAP

The two abbreviations OLAP (for Online Analytical Processing) and OLTP (for Online Transaction Processing) are often mistakenly associated with each other, as the names have a similar structure. In fact, however, the terms do not have much in common, but describe different concepts.

Online Transaction Processing (OLTP) describes systems that specialize in the fast processing of transactions. The main focus here is on ensuring that the relevant people in the front end can work with it relatively easily to process their transactions, for example at the checkout.

Online Analytical Processing (OLAP), on the other hand, is a platform for simpler data analysis in databases. It is therefore primarily used in the backend by business analysts or data scientists to analyze business data and find sometimes complex correlations that can contribute to the further development of the company.

From a technical perspective, the differences are even clearer, as the following points compiled by IBM show:

• OLTP queries are usually based on individual data records for processing a transaction, whereas OLAP queries a large number of data records.
• Response time plays a major role in online transaction processing, whereas the analyses in online analytical processing are not time-relevant and can take longer in many cases.
• With online analytical processing, the data is not changed but merely analyzed. Online transaction processing modifies the database with every incoming transaction.
• The memory requirements of OLTP are low, as although many transactions are stored, they have a clear structure and a limited number of columns. OLAP, on the other hand, processes large amounts of data, as the information often comes from different databases and is merged.

It quickly becomes clear that these two systems differ in fundamental respects and have little in common.

What Future do OLTP Systems have?

Although OLTP systems are facing ever greater challenges and the number of transactions is constantly increasing, they remain a central pillar of the digital economy. In the future, scalability and performance will continue to be of interest, while new aspects such as cloud technologies and AI integration will also become more relevant.

More and more companies are moving their OLTP databases to the cloud to benefit from better scalability, lower operating costs, and high availability. There are specialized databases, such as Aurora or Cosmos DB, which enable distributed transactions across multiple data centers. NoSQL databases are also gaining in importance, as they can be scaled horizontally and therefore meet higher performance requirements. Although they cannot meet the ACID requirements due to their architecture, eventual consistency is increasingly being accepted to further improve performance. In addition, the so-called NewSQL approaches are gaining ground, in which the scalability of NoSQL databases can be combined with the ACID properties of relational databases.

Artificial intelligence is also playing an increasingly important role in OLTP systems. Trained models can be used to automatically optimize queries in order to avoid bottlenecks, or predictive scaling can be used to identify peak loads in real time and adjust resources accordingly.

This is what you should take with you

• Online transaction processing (OLTP for short) describes the simultaneous processing of several so-called transactions.
• Transactions have several requirements for the underlying databases, such as high consistency and fast data processing.
• The architecture of OLTP is usually divided into three levels, namely the presentation, business logic and data storage levels.
• Although OLTP and OLAP share a large part of their name, they are very different. The main difference is that in OLTP the databases are continuously expanded with new transactions, while OLAP only deals with data analysis.

Other Articles on the Topic of OLTP

Microsoft, IBM, and Oracle offer detailed articles on online transaction processing, which were also used as sources for this article:

• Microsoft OLTP
• Oracle OLTP
• IBM OLTP