OLTP (online transactional processing) enables the rapid, accurate data processing behind ATMs and online banking, cash registers and ecommerce, and scores of other services we interact with each day.
OLTP, or online transactional processing, enables the real-time execution of large numbers of database transactions by large numbers of people, typically over the internet.
A database transaction is a change, insertion, deletion, or query of data in a database. OLTP systems (and the database transactions they enable) drive many of the financial transactions we make every day, including online banking and ATM transactions, e-commerce and in-store purchases, and hotel and airline bookings, to name a very few. In each of these cases, the database transaction also remains as a record of the corresponding financial transaction. OLTP can also drive non-financial database exchanges, including password changes and text messages.
In OLTP, the common, defining characteristic of any database transaction is its atomicity (or indivisibility)—a transaction either succeeds as a whole or fails (or is canceled). It cannot remain in a pending or intermediate state.
In general, OLTP systems do the following:
OLTP is often confused with online analytical processing, or OLAP. Both have similar acronyms and are online data processing systems, but that's where the similarity ends.
OLTP is optimized for executing online database transactions. OLTP systems are designed for use by frontline workers (e.g., cashiers, bank tellers, part desk clerks) or for customer self-service applications (e.g., online banking, e-commerce, travel reservations).
OLAP, on the other hand, is optimized for conducting complex data analysis. OLAP systems are designed for use by data scientists, business analysts, and knowledge workers, and they support business intelligence (BI), data mining, and other decision support applications.
Not surprisingly, there are several distinct technical differences OLTP and OLAP systems:
It's worth noting OLTP systems often serve as a source of information for OLAP systems. And often, the goal of the analytics performed using OLAP is to improve business strategy and optimize business processes, which can provide a basis for making improvements to the OLTP system.
For a deep dive into the differences between these approaches, check out "OLAP vs. OLTP: What's the Difference?"
Since the inception of the internet and the e-commerce era, OLTP systems have grown ubiquitous. They’re found in nearly every industry or vertical market and in many consumer-facing systems. Everyday examples of OLTP systems include the following:
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IBM Db2 is a relational, multi-modal database that delivers advanced data management and analytics capabilities for both structured and unstructured data and a broad array of workloads, including OLTP. In essence, Db2 enables enterprises to perform OLAP queries directly on a transactional database that’s optimized for use in production systems, combining the benefits of OLTP and OLAP databases into one high-performing and efficient data store.