Query multiple databases with a single SQL statement without moving data. Learn how federated queries work, predicate pushdown, and common use cases.

Access and combine data from multiple sources through a unified interface without replication. Learn how it compares to ETL and when to use it.

Cache frequently accessed data locally for sub-second queries while keeping it fresh with CDC. Learn acceleration strategies and when to use them.

Track row-level database changes and stream them in real time. Learn log-based, trigger-based, and polling patterns for real-time data pipelines.

Combine vector similarity with keyword matching for more accurate results. Learn about RRF, score fusion, and why hybrid search matters for RAG.

The standard ranking function for full-text search. Learn how BM25 scores documents, how inverted indexes work, and when keyword search needs vector search.

Find semantically similar content by comparing vector embeddings. Learn about ANN algorithms, distance metrics, and vector indexes.

Retrieval augmented generation grounds LLM responses in real data at inference time. Learn the three-stage pipeline, production challenges, and hybrid search integration.

Understand how large language models generate responses. Learn about tokenization, KV caching, quantization, and latency optimization.

LLMs output structured function calls instead of text to interact with external tools. Learn the tool calling loop, security considerations, and MCP.

MCP standardizes how AI models discover and invoke external tools and data. Learn the client-server architecture and how gateways enable enterprise AI.

Translate natural language questions into SQL queries using LLMs. Learn about NSQL, schema-aware generation, and production safeguards.

Dense vector representations that capture semantic meaning. Learn how embedding models work, how they enable semantic search and RAG, and how to choose the right model.

An extensible SQL query engine written in Rust. Learn the architecture, how it compares to DuckDB and Trino, and how Spice extends it.

A distributed SQL query engine that scales DataFusion across multiple nodes. Learn the scheduler-executor architecture and how it compares to Spark.

A compressed columnar file format with adaptive encoding for fast analytical queries. Learn how it compares to Parquet and powers Spice Cayenne.

Query data in place or move it to a warehouse? Compare federation and ETL across latency, freshness, cost, and operational complexity.

Keyword matching or semantic similarity? Compare BM25 and vector search across accuracy, performance, and use cases -- and learn when hybrid search wins.

Ground LLM responses with retrieved data or train the model directly? Compare RAG and fine-tuning across cost, freshness, accuracy, and implementation effort.

Access data virtually or replicate it physically? Compare virtualization and replication across latency, consistency, cost, and when to combine both.

Deploy a data runtime alongside your app or as a shared service? Compare sidecar and microservice architectures across latency, scaling, resource usage, and operational complexity.

Combine sidecar caching with a centralized cluster for sub-millisecond reads and centralized data management. Learn the CDN-for-data pattern.