Transactional databases enable data to be efficiently and securely read and written. They power many business processes, including online banking and ecommerce transactions. This article explores how transactional databases work, what makes them so reliable, and how they’re used in banking, retail, and healthcare.
What Is a Transactional Database?
Transactional databases are a type of database management system optimized to read and write individual rows of data very quickly while ensuring data integrity. Transactional databases automatically roll back or undo a transaction or database operation that can’t be fully completed. Each transaction is handled separately from all other transactions occurring within the database, ensuring that the stored data remains secure, easily accessible, and coherent even during a system failure.
Key Properties of a Transactional Database
Transactional databases use a type of data processing referred to as online transaction processing (OLTP). OLTP enables transactional databases to execute numerous transactions concurrently. These systems contain important properties that help them simultaneously execute database transactions at scale.
OLTP systems often have large user populations, with many people accessing the same data simultaneously. Concurrency manages the write process when two different users attempt to take action at the same time—one user must wait until the other has finished processing before they can complete their action. This type of control is what prevents two online shoppers from both buying the last item in stock from an online retailer or two travelers from booking the same seat on a flight.
Transactional databases must be capable of rapidly scaling up or down to meet user demand. Scalability ensures that systems can handle surges in traffic, elastically expanding when required. Online banking is a prime example, with higher volumes of customers seeking to access their online accounts at popular times.
Most digital transactions must be executed incredibly quickly. Transactional databases are able to handle multiple, concurrent read and write requests at lightning speed. Online retail is a prime example of why speed is crucial—with thousands of shoppers on a retailer’s site expecting a quick and easy shopping, checkout, and payment verification experience, processing speed must keep up.
Transactional databases contain vast stores of sensitive customer data. A successful breach of one of these systems could compromise vast amounts of user data and account information. Maintaining robust data security safeguards customer account data and the reputation of the business.
Hardware and software systems sometimes fail. In the event of a system failure, transactional databases are designed to be recoverable, protecting data integrity and ensuring continuity for the user.
Processing Transactions Reliably with ACID
Transactional databases must protect the integrity of the data written to the database. The properties that enable these safeguards are collectively referred to as ACID. This acronym describes the four defining features that should be built into transactional processing.
Atomicity: Atomicity requires that every step in a transaction be completed in its entirety before being committed to the database. If there’s a failure at any stage of the transaction, the other steps will also fail or be reverted.
Consistency: Consistency ensures that one of two things happens: The transaction is either successfully written to the database, moving it from one valid state to another, or it is automatically reverted. This enables the database to remain in a constant state of internal consistency.
Isolation: ACID-compliant transactional databases require that individual transactions run independently. Transactions that have not been fully processed are not able to be acted on or modified by other transactions.
Durability: When transactions have been fully processed and committed to the database, they cannot be lost, even if the database fails.
Transactional Database Use Cases
Transactional databases underpin many of the applications consumers and businesses have come to rely on. Here are three examples of how these systems and the data they generate create real-world value.
OLTP systems enable a range of banking services, including ATM transactions and online banking transactions such as funds transfers and mobile deposits. Security safeguards protect user account information and ensure accurate account balances are maintained even during a system failure.
Transactional databases process massive volumes of concurrent credit card transactions both online and in brick-and-mortar retail stores. These systems also maintain accurate order entry to ensure online inventory is not sold multiple times to concurrent users.
Accurate record-keeping is vital inhealthcare. Transactional databases ensure two users aren’t writing to a patient’s EHR record simultaneously, resulting in only one version of the record being saved. This technology also powers insurance claims processing.
Analyzing Transactional Data with Snowflake’s New Unistore Workload
Transactional databases are a valuable source of data for decision-makers with vital intelligence. Customer purchase history, online bookings, inventory control, production scheduling, and more can all be mined for insights. But before transactional data can be analyzed, it traditionally had to be copied and moved out of the transactional database and into an OLAP (online analytical processing) database for analysis.
Since transactional and analytical data have traditionally remained separate, organizations have been limited in how quickly they could use data from transactional databases to evolve their businesses. Snowflake has changed that.
Snowflake's Unistore workload provides a modern approach to unified database management, enabling the handling of transactional and analytical data together. It enables organizations to conduct real-time analytical queries directly on transactional data within the platform, breaking down data silos between transactional and historic data. Unistore uses a single data set to power modern development, making it possible to act on transactional data immediately, build better customer experiences, and gain new insights.
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