Implementing and using an OLTP system can create a few challenges: This supports efficiency because it enables the OLTP system to process large numbers of transactions independently, and avoids extra processing needed to maintain data integrity in the presence of redundant data. The goal of efficiently processing and storing individual transactions by an OLTP system is partly accomplished by data normalization - that is, breaking the data up into smaller chunks that are less redundant. OLTP systems are designed to efficiently process and store transactions, as well as query transactional data. Use this architecture when any tangible delay in processing would have a negative impact on the day-to-day operations of the business. Transactional data tends to have the following traits: RequirementĬhoose OLTP when you need to efficiently process and store business transactions and immediately make them available to client applications in a consistent way. A related deployment architecture is the N-tier architecture, which may have multiple middle-tiers handling business logic.
A 3-tier architecture typically consists of a presentation tier, business logic tier, and data store tier. The most common deployment architecture that uses transactional data is the data store tier in a 3-tier architecture. Transactional databases can support strong consistency for transactions using various locking strategies, such as pessimistic locking, to ensure that all data is strongly consistent within the context of the enterprise, for all users and processes. There are more formal definitions of these properties, such as ACID.) (These are very informal descriptions of atomicity and consistency. Consistency means that transactions always leave the data in a valid state. In a traditional RDBMS, this rollback happens automatically if a transaction cannot be completed. If a transaction cannot be completed, the database system must roll back any steps that were already done as part of that transaction. Atomicity means that an entire transaction always succeeds or fails as one unit of work, and is never left in a half-completed state. Transactions typically need to be atomic and consistent. Transactional events, which represent the transactions themselves, typically contain a time dimension, some numerical values, and references to other data. These interactions are typically business transactions, such as payments received from customers, payments made to suppliers, products moving through inventory, orders taken, or services delivered. Transactional data is information that tracks the interactions related to an organization's activities.
OLTP systems record business interactions as they occur in the day-to-day operation of the organization, and support querying of this data to make inferences. The management of transactional data using computer systems is referred to as online transaction processing (OLTP).
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