On‑chain data analysis combines high-volume, time‑series blockchain feeds with relational and graph structures to power fraud detection, DeFi risk models, attribution, and real‑time monitoring. This topic examines the machine learning platforms and integrations needed to ingest, model and serve ML over blockchain data—covering cloud data platforms, pipeline orchestration, catalog/lineage, and database connectors. As of 2025‑11‑29, demand for real‑time and auditable on‑chain ML has increased due to larger on‑chain volumes, tighter compliance expectations, and the need for explainable model pipelines. Practical systems now pair orchestration frameworks with databases that capture both transactional and relationship context, while exposing secure, programmatic access to LLMs and analytic agents via the Model Context Protocol (MCP). Key components and tools: Dagster provides pipeline orchestration to schedule, test and trace feature pipelines; Neo4j (MCP clients/servers) supplies graph persistence and relationship queries for link analysis and entity resolution; Neon offers a serverless Postgres MCP server for scalable, low‑latency transactional storage; Supabase exposes Postgres-backed projects plus edge functions and connectors for rapid ingestion and operational APIs; Grafbase turns GraphQL APIs into high‑performance MCP gateways for federated access; and pydantic’s mcp-run-python enables secure sandboxed Python execution for agent-driven feature computation and safe model inference. Together these pieces address core needs: robust ingestion and connectors to cloud platforms, pipeline orchestration and lineage for reproducibility, graph and relational stores for complex features, and secure MCP integrations so agents and LLMs can query and act on live data. Choosing the right mix depends on scale, latency requirements, explainability and regulatory constraints; this ecosystem emphasizes composability and secure interoperation for production on‑chain ML workflows.