Data Partitioning
The practice of dividing large datasets into smaller, manageable segments based on key attributes like date or region, improving query performance by allowing the system to scan only relevant partitions.
Data partitioning organizes data into physical or logical segments based on a partition key. A table partitioned by date stores each day's data separately. When a query filters on date range, the query engine skips irrelevant partitions entirely, dramatically reducing the amount of data scanned and the query execution time.
Common partitioning strategies include range partitioning (by date, numeric ranges), hash partitioning (distributing evenly by hash of a key), and list partitioning (by specific values like country or category). The choice of partition key should align with common query patterns; partitioning by date is effective only if most queries filter on date.
For AI teams working with large datasets, proper partitioning can reduce feature engineering query times from hours to minutes. Training data extraction queries that filter by date range benefit enormously from date-based partitioning. Feature computation queries that aggregate by user benefit from user-based partitioning. The partition strategy should be designed around the most common and expensive query patterns in your ML workflows.
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.