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Online Transaction Processing

Updated on June 14, 2024
Article byKhalid Ahmed
Edited byAaron Crowe
Reviewed byDheeraj Vaidya, CFA, FRM

What Is Online Transaction Processing (OLTP)?

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.

Online Transaction Processing

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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.

Key Takeaways

  • 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) Explained

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,

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.

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Examples

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.

Advantages And Disadvantages

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

Advantages of OLTPDisadvantages of OLTP
During high peak times, the system’s performance handling transactions may be reduced.Most of the Complex queries or analytics become limited in processing.
This process system can conduct high volumes of transactions with lower response times.Complex and large systems may have limited scope for scalability.
OLTP assures users with accurate, consistent data having integrity.These need huge investments in software and hardware for the proper execution of real-time transactions.
Moreover, it allows rapid retrieval and data entry of transactions.They require backups and maintenance in short intervals for proper functioning.
Businesses can automate routine transactions.Highly specialized professionals with suitable expertise must be deployed for its development and management.
Overall productivity and operational efficiency of businesses improve.Data breaches and security threats may attack with ease.
Critical business activities get a secure and reliable platform for execution.Complex and large systems may get limited scope for scalability.
End users get the benefit of streamlined and efficient data access.However, these may not get integrated with third-party apps and systems plus may not exhibit the desired flexibility.

Difference Between Online Transaction Processing And Batch Processing

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

OLTPBatch processing
It processes real-time data in small amounts.Batch processing processes data in large volumes in batches.
They only handle individual transactions.This processing system operates a batch of multiple transactions.
Data access gets granted on a concurrent basis.Data accuracy and consistency may be sacrificed here.
Moreover, it pushes up the consistency and data accuracy in priority.Data accuracy and consistency may get sacrificed here.
Helpful in daily business operations.Businesses use it for end-of-day and periodic processing.
Transactional data becomes faster and more efficient.Large data sets get the leverage of efficient processing.
Overall, it successfully handles operational data processing due to its design architecture.Due to their design architecture, these create reports and analyze large volumes of data.

Frequently Asked Questions (FAQs)

1. How do online transaction processing systems handle concurrency?

OLTP systems are designed to handle high levels of concurrency by implementing mechanisms like locking and transaction isolation. These techniques ensure that multiple transactions can execute simultaneously without interfering with each other’s operations or causing data inconsistencies.

2. What are the differences between OLTP and OLAP?

OLTP focuses on real-time transaction processing for day-to-day business operations. On the other hand, OLAP (Online Analytical Processing) is geared towards complex querying and analysis of large volumes of historical data for business intelligence and decision support.

3. Can OLTP systems handle large-scale data analytics?

No, OLTP systems are not optimized for large-scale data analytics. They are designed for real-time transaction processing and are generally unsuitable for complex analytical queries on vast historical data.

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 –

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