Online Transaction Processing

Online Transaction Processing (OLTP) is a type of data processing that manages and records transactions occurring in real-time or near real-time. Its primary purpose is to guarantee transactional data consistency, accuracy, and timelines and provide end users faster response times.

Businesses commonly use the OLTP system in various aspects of their operations, including finance, retail, transportation, healthcare, credential verification, system login, and bolstering businesses with an additional layer of protection. It has increased the productivity and efficiency of systems and reduced errors in all transactions. Nevertheless, it also has limitations and challenges for businesses and implementing agencies.

• Online transaction processing is an information system architecture that enables businesses to process many fast transactions in real-time.
• The primary aim of OLTP is to maintain data consistency, accuracy, and timeliness, while also providing a quick response time to end-users.
• OLTP systems can improve businesses' overall productivity and operational efficiency, but they can also expose them to data breaches and security threats.
• It only handles individual transactions, whereas Batch processing handles a batch of multiple transactions.

Online Transaction Processing (OLTP) refers to a category of information system architecture facilitating fast business transactions in real-time. Various industries and applications commonly use OLTP systems, such as e-commerce, banking, retail, airline reservations, etc. These systems have been designed to handle small yet lightning-fast transactions related to normal business operations, like,

• Cash withdrawals at ATMs
• Point-of-sale transactions
• Security trading

Moreover, these systems gain recognition from the characteristics of online transaction processing, particularly their client-server architecture. It means that a client sends a request for a transaction to the server, which processes the transaction and responds appropriately to the client. Servers are based on an application server and database management system (DBMS) for managing transactions and ensuring the correct and consistent online transaction processing database.

Hence, it offers numerous benefits, like faster transaction time on a real-time basis but remains vulnerable to risks like security breaches, hacking, data loss, and system crashes.

 However, OLTP is also affected by certain factors:

• Configuration and quality of software and hardware infrastructure impact its efficiency
• The high volume and nature of transactional workloads impact their performance
• Improper data distribution plus access could lead to slow and unreliable transactions
• Ensure the design and optimization of data for faster transaction processing, efficient storage, and retrieval.
• Enforcement of security protocols like encryption and access controls impacts its operations.

Hence, to achieve the highest performance and efficiency of OLTPs, businesses must consider these factors. Thus, online transaction processing systems focus on providing fast, reliable, and concurrent transaction processing to support critical business functions and user interactions.

Let us go through a few examples to understand the topic.

Example #1

Suppose a retail associate named Alex processes sales transactions with the help of the OLTP system at the sales counter. When a customer makes a purchase, Alex punches all the product details, price, and payment data into the OLTP system. As a result, the OLTP system:

• Deducts product cost from the customer's account
• Ensures accuracy, security, and consistency of the transaction
• Updates the retail store's stock
• The inventory level's real-time update gets reflected

Therefore, this process helps Alex manage sales transactions, product inventory, and better smoother customer shopping.

Example #2

Consumers worldwide continue to experience credit card fraud, which cost the sector $32 billion in 2021. To solve the issue, IBM is utilizing OLTP to assist banks in "massive scale" fraud analysis during transactions.

Due to latency concerns, banks' attempts to address the issue have been limited by the inability to run deep-learning models at scale in real-time, which results in detection models only being used on fewer than 10% of high-volume transactions.

Big Blue has solved this issue with the IBM Z16. Hence, it combines substantial data processing with highly secure and dependable AI inferencing via its IBM Telum Processor.

Therefore, this might mean less time and effort in handling illegal credit card charges for customers. On the other hand, for retailers and card issuers, it could mean less revenue lost as consumers avoid inaccurate denials.

Below is the table listing some essential advantages and disadvantages of OLTP:

Let us use the table below to ascertain the significant differences between the two:

This article has been a guide to what is Online Transaction Processing. We explain its examples, differences with batch processing, advantages, and disadvantages. You may also find some useful articles here -

• Online Analytical Processing
• Transaction Processing System
• Straight Through Processing