aiqbench takeMachine Learning

This architecture bridges the gap between massive-scale analytics and low-latency AI inference by enabling direct point queries on Apache Parquet without costly dataset replication. By mapping lookup keys directly to file locations, Spotify allows ML pipelines and online services to access the same data lake efficiently while preserving immutable storage formats. This advancement is significant for reducing infrastructure costs in large-scale model training and serving where traditional operational databases are prohibitively expensive or difficult to maintain at exabyte scales.

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Spotify Builds External Index to Enable Low Latency Point Queries on its Data Lake

InfoQAugust 12, 20263 min read

Spotify introduced external indexing architecture for Apache Parquet data lakes that enables low-latency point queries without replicating datasets into operational databases. The approach maps lookup keys to Parquet files and row locations, allowing targeted reads from cloud object storage while supporting analytics, machine learning, AI applications, and online services from the same datasets. By Leela Kumili

Spotify has introduced Random Access Parquet (RAP), a storage architecture that enables low-latency point queries directly against data stored in its data lake, allowing online services and AI applications to retrieve individual records without replicating datasets into operational databases. RAP adds an external indexing layer over Apache Parquet files, enabling interactive lookups while continuing to use…

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