OLAP (Online Analytical Processing)
A computing approach optimized for complex analytical queries over large datasets, supporting multi-dimensional analysis with operations like aggregation, filtering, and drill-down across multiple dimensions.
OLAP systems are designed for analytical workloads: complex queries that aggregate millions of rows, join large tables, compute running totals, and group by multiple dimensions. These queries are read-heavy, scan large portions of the data, and return aggregated results. Data warehouses and columnar databases are OLAP systems.
OLAP contrasts with OLTP (Online Transaction Processing), which handles high-volume transactional operations like inserting orders, updating inventory, and processing payments. OLTP systems optimize for many small, fast read-write operations; OLAP systems optimize for fewer, complex read operations over large datasets.
For AI and growth teams, OLAP systems are where analytical queries for dashboards, reports, and feature engineering run. Computing features like "total revenue per customer over the last 90 days" or "average session duration by user segment" are OLAP workloads. Understanding the OLAP nature of these queries helps teams choose appropriate tools and optimize query performance for their ML data pipelines.
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.