Topic
DataBase Engine
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Inside the VAST Vector Store: Trillion-Scale Vector Search in the VAST DataBase (Part 2 of 2)
How VAST stores vectors as a first-class data type, searches trillions of vectors in under 200ms with hierarchical clustering, unifies governance, and beats PGVector,…
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Inside the VAST Event Broker: Real-Time Analytics and AI at Scale (Part 2 of 2)
The VAST Event Broker unifies streaming, analytics, and AI on one Kafka-compatible platform, storing topics as queryable tables for real-time insight at scale.
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Why Event Streaming Is the Backbone of Real-Time AI, and Where Legacy Kafka Falls Short (Part 1 of 2)
AI reacts to data the moment it arrives, which makes real-time event streaming the backbone of modern AI. Here is where legacy Kafka platforms…
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Proof, Not Promises: How the VAST DataBase Outperforms Iceberg and Parquet
Head-to-head benchmarks against Iceberg and Parquet on matched hardware: about 3x faster ingest, 5x faster updates, 21x faster schema backfills, and flat sub-100ms queries…
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From Lakehouse to AI: Analytics, Catalogs, and RAG on the VAST DataBase (Part 4 of 4)
Open table formats, catalogs, governance, and retrieval-augmented generation. The path from lakehouse analytics to trustworthy AI on one governed copy of data.
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Under the Hood of the VAST DataBase: Row and Column, ACID, and Exabyte Scale (part 3 of 4)
Row layout is fast to write; columnar is fast to scan. See how the VAST DataBase delivers both, with ACID transactions on a single…
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Inside the VAST DataBase Engine: One System for Tables, Files, and Streams (part 2 of 4)
Data sprawl is the result of a hardware limit, not bad planning. Here is how the VAST DataBase engine unifies tables, files, and streams…
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Why Modern Data Architectures Break, and What the VAST DataBase Fixes (part 1 of 4)
Over fifty years we stacked databases, warehouses, and data lakes on top of each other. Here is why modern data architectures break, why data…

