How to Connect LLMs to Streaming Data Sources (2026)

Large language models need real-time data to give accurate, current answers — but most LLMs are trained on static snapshots. Connecting LLMs to streaming data requires a real-time context layer that continuously prepares queryable, up-to-date information. A streaming database like RisingWave serves as this layer: it ingests CDC and Kafka streams, maintains SQL materialized views, and serves context via PostgreSQL protocol or MCP server.

Three Ways to Connect LLMs to Streaming Data

1. Model Context Protocol (MCP)

MCP (97M+ monthly SDK downloads) standardizes how LLMs access external data:

LLM → MCP Client → MCP Server (RisingWave) → Streaming Materialized Views

RisingWave has an MCP server that lets Claude, ChatGPT, and Copilot query real-time data.

2. Function Calling / Tool Use

LLMs call a function that queries the streaming database:

def get_customer_context(customer_id: str) -> dict:
    conn = psycopg2.connect(host="risingwave", port=4566, dbname="dev")
    cursor = conn.cursor()
    cursor.execute("SELECT * FROM customer_context WHERE id = %s", (customer_id,))
    return dict(zip([d[0] for d in cursor.description], cursor.fetchone()))

3. Real-Time RAG

Streaming database maintains fresh structured context alongside vector embeddings:

CREATE MATERIALIZED VIEW knowledge_base AS
SELECT doc_id, title, content, updated_at
FROM documents;  -- CDC table, always current

Why Not Just Use APIs?

Frequently Asked Questions

What is MCP and how does it help LLMs access streaming data?

Model Context Protocol is an open standard for connecting AI models to external data sources. RisingWave's MCP server exposes streaming materialized views to any MCP-compatible LLM, providing always-current context without custom integration code.

Do I need Kafka to connect LLMs to streaming data?

No. RisingWave ingests directly from PostgreSQL/MySQL CDC without Kafka. For Kafka-based architectures, RisingWave also supports Kafka as a source.