> Markdown version of [/videos/100212-olap-for-ai-applications-and-why-you-should-care?t=718](https://www.wearedevelopers.com/videos/100212-olap-for-ai-applications-and-why-you-should-care?t=718). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # OLAP for AI Applications and why you should care Modern AI agents require more than simple document retrieval. Search-OLAP natively unifies semantic search with rigid SQL aggregations. Empower your LLMs to reason across billions of rows instantly. - **Speakers:** [Andrey Abramov](https://www.wearedevelopers.com/@andrey-abramov) - **Event:** World Congress 2026 Europe - **Published:** July 10, 2026 - **Duration:** 30:06 - **URL:** https://www.wearedevelopers.com/videos/100212-olap-for-ai-applications-and-why-you-should-care ## Summary Modern AI agents and RAG applications demand more than simple document retrieval; they require multi-step reasoning that blends unstructured semantic search with rigid analytical processing. When an AI assistant handles complex prompts—such as finding charming real estate, filtering by budget, and aggregating neighborhood market trends—traditional boundaries between search engines and analytical databases break down. Because LLMs suffer from limited context windows and cannot ingest millions of rows, the underlying data architecture must natively handle both vector similarity and statistical aggregations to return precise insights rapidly. To address this duality, Search-OLAP emerges as a hybrid architecture where information retrieval becomes a native analytical primitive. By coupling vectorized execution with columnar storage, this approach allows BM25, semantic search, and SQL aggregations to coexist as peers. Rather than relying on costly ETL pipelines to duplicate logs into search engines like Elasticsearch, Search-OLAP accesses data where it lives—whether in remote Parquet files, PostgreSQL instances, or Iceberg lakehouses. A distributed, Postgres-compatible query federation lets the AI agent interact with a unified interface while utilizing fine-grained role-based access control (RBAC). Benchmarking search-analytics workloads requires new standards; initiatives like SearchBench demonstrate that columnar Search-OLAP can compress payloads significantly and ingest a billion rows in under eight minutes. Ultimately, giving an LLM access to robust real-time indexing alongside KNN similarity—demonstrated efficiently through large-scale interactive scenarios like a three-billion-row chess modeling agent—closes the gap between simply retrieving information and deeply reasoning about it. **Keywords:** search-OLAP, RAG systems, semantic search, vector similarity, SQL aggregations, vectorized execution, columnar storage, data federation, postgres RBAC, LLM reasoning chains, remote parquet files, iceberg lakehouses, searchbench, KNN search, BM25 retrieval ## Chapters 1. **How AI agents execute reasoning chains for complex queries** (01:26) — The transition from human analysts to AI agents generating dozens of interrelated queries to find relevant data. 1. **Architectural challenges of AI-driven data queries** (07:07) — Why limited LLM context windows and scattered, siloed data prevent effective precomputation or caching for agentic workflows. 1. **Introducing SearchOLAP for native analytical processing** (10:06) — Processing data where it lives by integrating search primitives into an analytical database architecture instead of duplicating it. 1. **Core architecture and federated execution of SearchOLAP** (11:58) — How columnar storage, zone map indices, and remote row navigation combine to support distributed, scalable query processing securely. 1. **Handling latency and synchronization for remote data sources** (15:54) — Strategies for managing network latency and keeping external databases or lakehouses in sync using CDC tools and snapshots. 1. **Benchmarking SearchOLAP against existing database engines** (18:16) — Analyzing insertion speed, disk size, and query latency using the open-source SearchBench framework on large-scale telemetry data. 1. **Live demo predicting chess moves across billions of rows** (21:17) — A real-time demonstration scanning three billion chess moves and evaluating them simultaneously with k-nearest neighbors and an evaluation engine. 1. **Q&A on analytical databases and market convergence** (25:05) — A discussion comparing SearchOLAP to other compute engines and how major database providers are converging transactional and analytical workloads. ## Related Moments - [Designing highly scalable hybrid AI search engines](https://www.wearedevelopers.com/videos/436-hybrid-ai-next-generation-natural-language-processing) (from "Hybrid AI: Next Generation Natural Language Processing") - [Revolutionizing database search queries with language models](https://www.wearedevelopers.com/videos/2088-plan-to-link-your-llm-to-your-production-database-what-could-possibly-go-wrong) (from "Plan to link your LLM to your production database? What could possibly go wrong?") - [Scaling semantic search with Astra DB and Apache Cassandra](https://www.wearedevelopers.com/videos/966-accelerating-genai-development-harnessing-astra-db-vector-store-and-langflow-for-llm-powered-apps) (from "Accelerating GenAI Development: Harnessing Astra DB Vector Store and Langflow for LLM-Powered Apps") - [Core principles for robust AI search systems](https://www.wearedevelopers.com/videos/1989-tomb-raider-ai-search-with-kotlin) (from "Tomb rAIder: AI Search with Kotlin") - [Substituting traditional classification models with search-based AI architecture](https://www.wearedevelopers.com/videos/830-what-comes-after-chatgpt-vector-databases-the-simple-and-powerful-future-of-ml) (from "What comes after ChatGPT? Vector Databases - the Simple and powerful future of ML?") - [Reviewing system architecture for custom semantic search](https://www.wearedevelopers.com/videos/860-harry-potter-and-the-elastic-semantic-search) (from "Harry Potter and the Elastic Semantic Search") ## Related Articles - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [SEO in an AI world - Google vs. ChatGPT and survival tips for content creators](https://www.wearedevelopers.com/magazine/534-seo-in-an-ai-world-google-vs-chatgpt-and-survival-tips-for-content-creators) - [Stephan Gillich - Bringing AI Everywhere](https://www.wearedevelopers.com/magazine/489-stephan-gillich-bringing-ai-everywhere) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) ## Related Jobs - [Principal Engineer - AI Search & Vector Infrastructure](https://www.wearedevelopers.com/jobs/ext/319507-principal-engineer-ai-search-vector-infrastructure) at **Redis** - [Principal Engineer - AI Search & Vector Infrastructure](https://www.wearedevelopers.com/jobs/ext/353953-principal-engineer-ai-search-vector-infrastructure) at **Redis** - [Principal Engineer - AI Search & Vector Infrastructure](https://www.wearedevelopers.com/jobs/ext/381484-principal-engineer-ai-search-vector-infrastructure) at **Redis** - [AI Software Engineer (Germany)](https://www.wearedevelopers.com/jobs/48317-ai-software-engineer-germany) at **Sunhat** - [Data Scientist](https://www.wearedevelopers.com/jobs/ext/1351648-data-scientist) at **Almedia** - [Senior AI Agent Software Engineer (Go, Python) (m/f/x)](https://www.wearedevelopers.com/jobs/48277-senior-ai-agent-software-engineer-go-python-m-f-x) at **Dynatrace**