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Endigest AI Core Summary
This post explores how Google Cloud's Spanner database is designed to power agentic AI workflows through its multi-model architecture.
•Fragmented multi-database strategies introduce data inconsistency, operational silos, and ETL latency that block real-time AI intelligence
•Spanner unifies relational, key-value, graph, vector, and full-text search capabilities within a single database, eliminating cross-system synchronization
•Vector search is powered by Google's ScaNN technology, supporting indexes with over 10 billion vectors using both KNN and ANN
•Graph support is built on the ISO standard GQL, and Cassandra workloads can migrate via a native endpoint without code changes
•MakeMyTrip consolidated MongoDB, Neo4j, Elasticsearch, and Qdrant into Spanner, achieving 75% reduction in operational complexity and 9% improvement in answer-quality score
This summary was automatically generated by AI based on the original article and may not be fully accurate.
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