OLTP (Online Transaction Processing)
A database processing paradigm optimized for handling large volumes of short, atomic transactions with fast reads and writes, powering the operational systems that run day-to-day business operations.
OLTP systems power your application's core operations: user registration, order placement, payment processing, and inventory management. They are optimized for high-throughput transactional workloads with ACID guarantees (Atomicity, Consistency, Isolation, Durability), ensuring data integrity even under concurrent access.
OLTP databases like PostgreSQL, MySQL, and Amazon Aurora use row-based storage and B-tree indexes optimized for point lookups and small range scans. They handle thousands of concurrent transactions per second, each touching a small number of rows. Response times are measured in milliseconds.
For AI applications, OLTP databases are the source of truth for operational data that feeds into ML pipelines. User interactions, transactions, and state changes captured in OLTP systems are extracted (via ETL, ELT, or CDC) and loaded into analytical systems for feature engineering and model training. Understanding the distinction between OLTP and OLAP helps teams avoid anti-patterns like running heavy analytical queries on production OLTP databases, which degrades application performance.
Related Terms
Cosine Similarity
A measure of similarity between two vectors based on the cosine of the angle between them, ranging from -1 (opposite) to 1 (identical), commonly used to compare embeddings.
Dimensionality Reduction
Techniques that reduce the number of dimensions in high-dimensional data while preserving meaningful structure, used for visualization, compression, and noise removal.
Batch Inference
Processing multiple ML predictions as a group at scheduled intervals rather than one-at-a-time on demand, optimizing for throughput and cost over latency.
Real-Time Inference
Generating ML predictions on-demand as requests arrive, typically with latency requirements under 200ms for user-facing features.
Data Pipeline
An automated sequence of data processing steps that moves data from source systems through transformations to destination systems, enabling reliable and repeatable data flows across an organization.
ETL (Extract, Transform, Load)
A data integration pattern that extracts data from source systems, transforms it into a structured format suitable for analysis, and loads it into a target data warehouse or database.