Indexing the Data Lake for Online Point Queries

Spotify is addressing the challenge of performing low-latency point queries on massive data lakes by implementing Random Access Parquet (RAP). This solution allows for efficient data retrieval without the overhead of traditional distributed SQL engines, supporting both online services and AI agents.
Companies like Spotify need vast quantities of data accessible at low latency for online services and, increasingly, AI Agents acting on behalf of users. Online services — such as portals and personalization features that use or display a user's listening history — need to look up and paginate through per-user data at interactive speeds. AI Agents answering questions like, "what was I listening to last summer?" need to retrieve a user's data quickly so they can reason over it — filtering, aggregating, or even running SQL locally — to build context for LLM prompts. Both patterns share the same underlying primitive: fast point queries by key over datasets that are often far too large to economically keep resident in a key-value (KV) store like Bigtable or DynamoDB.
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