A Comprehensive Guide to OLTP

At first glance, you might assume that oil and data are as opposite as oil and water. The most glaring difference is that one is physical and largely digital. However, once you breach the surface, you find a few reasons to believe that data may be the new oil in today’s data-driven economy.

As more businesses embrace and migrate to digital, data-centric operations, it has also become increasingly vital to gain a solid understanding of the technologies needed to process all of that big data.

In this sense, an online transaction database is crucial in enabling businesses to gain visibility and control over their data, particularly regarding audit trails for data access and change management.

OLTP stands for Online Transaction Processing, a data processing method in which transactions play a significant role in manipulating data in a database. OLTP transactions generate the data and statistics necessary for the business decision-making process.

Understanding what OLTP systems are and the benefits of adopting one can assist you in determining whether OLTP applications and OLTP tools are appropriate for your business.

This article will discuss:

What is OLTP?

To define OLTP in its essence, it is a kind of data processing that entails executing multiple concurrent operations — for example, online banking, shopping, order entry, or text messaging.

 

Traditionally, these operations have been referred to as economic or financial transactions. They get recorded and secured so that a company can access the information at any time for accounting or reporting purposes.

 

However, the term “transaction” has evolved, particularly with the emergence of the internet, to encompass any business digital interaction or engagement. It has expanded to include audit trails for data access and change management, ultimately harnessing web-based transaction processing systems.

 

An online, mobile, or enterprise application typically tracks all customer, supplier, or partner interactions and updates the online transaction processing database. This OLTP database transaction data gets used for reporting and data-driven decision-making.

OLTP vs. OLAP

In the drive to explain OLTP, another term often confused with one another surfaces OLAP.

 

Initially, two types of data processing systems existed in data science: Online Analytical Processing (OLAP) and Online Transaction Processing (OLTP). The primary distinction is that one uses data to derive important insights, whereas the other is strictly operational. However, both systems can be used effectively to handle data challenges and uphold data integrity.

 

An OLAP database is a system for quickly analyzing large numbers of data. They come from a data warehouse, data mart, or other central data stores that hold various data sets, including historical and transaction data. OLAP systems are great for data mining, business intelligence, complicated calculations, financial analysis, budgeting, and sales forecasting.

 

Data warehousing is appropriate for OLAP systems since the purpose is to analyze a huge volume of data from numerous sources effectively and accurately to generate insights that drive subsequent actions and guide future decisions.

 

OLAP is a powerful ally for procurement teams in the pursuit of precise and full expenditure analysis, as well as financial planning and reporting. It analyzes data acquired and stored through OLTP processes to develop large-scale, strategic enhancements that benefit procurement teams or business intelligence and the entire organization.

Creating an OLAP Cube

Furthermore, you can use OLAP databases to create what is known as an OLAP cube.

 

The OLAP cube is the core of most OLAP databases. The OLAP cube extends the standard row-by-column format of a traditional relational database schema and adds layers for other data dimensions.

 

By contrast, OLTP places a greater focus on “processing” than on “analysis.” If OLAP is primarily strategic, OLTP is tactical and transactional, focused on core business functions such as:

 

  • Automated Notification and Reminder Systems
  • Order Entry and Approval
  • Payment Approval and Issuance

OLTP and OLAP Use Cases

Moreover, OLTP systems use a relational database optimized for online transactions to carry out the following OLTP use cases:

 

  • Process large numbers of relatively straightforward transactions, most commonly data insertions, updates, and cancellations.
  • Allow several users to access the data while maintaining data integrity.
  • Support processing in real-time, with millisecond reaction times.
  • Provide indexed data collections that may be searched, retrieved, and queried quickly.
  • Be available 24 hours a day, seven days a week, with continuous incremental backups.

 

An OLTP system is more tactical and immediate in its application in procurement automation than an OLAP system. This system focuses on constant, frequent, and generally straightforward processes. Moreover, OLTP systems generate the data that OLAP systems use to accomplish strategic improvements.

 

Briefly stated, you may find the primary distinction between the two systems in their names: analytical vs. transactional systems. Each system is designed specifically for the type of processing it will be performing.

 

Thus, selecting the most appropriate system for your situation depends on your objectives.

 

It is possible to extract value from large amounts of data using an OLAP database system, for example, if you require a single platform for business insights. If you need to manage daily transactions, on the other hand, an OLTP database system gets built to handle high numbers of transactions per second rapidly and efficiently.

 

However, more and more organizations tend to employ both OLAP and OLTP systems most of the time. Knowing that you can use OLAP systems to evaluate data that leads to the improvement of business processes in OLTP systems, businesses are increasingly integrating the two rather than choosing one over the other.

Summary

Since OLTP systems in many cases supply the transactional databases upon which OLAP applications rely, choosing between the two is a false dichotomy. A more advisable approach is to analyze how you can effectively integrate these database management technologies into your organization’s business processes and workflows. In other words, you will probably need more.