"Apache Parquet" matching MCP servers:

Enables inspection and management of Apache Airflow DAGs, runs, and logs across multiple instances. It provides tools for monitoring workflows and performing gated write operations like triggering DAGs or clearing task instances.

Enables lookups across Apache Iceberg Javadocs, local source evidence, and Iceberg REST Catalog operations through MCP tools and prompts.

MCP Server for Apache Pinot

A Model Context Protocol server that connects AI agents to Apache Jena, enabling them to execute SPARQL queries and updates against RDF data stored in Jena Fuseki.

Enables AI agents to manage and interact with Apache AGE graph databases through natural language. Supports creating, updating, querying, and visualizing multiple graphs with vertices and edges.

Enables AI-controlled management of Apache Beam data pipelines across different runners (Flink, Spark, Dataflow, Direct) via the Model Context Protocol.

Provides integration with Apache Airflow's REST API, allowing AI assistants to programmatically interact with Airflow workflows, monitor DAG runs, and manage tasks.

A Model Context Protocol server that provides tools for interacting with databases, including PostgreSQL, DuckDB, and Google Cloud Storage Parquet files.

Offline MCP server that checks and scans your own AI prompts and outputs against a local compliance rule corpus. Returns rule ID, severity, citation, and remediation per finding — no API key, Apache 2.0.

MCP server that diagnoses Apache Spark job failures and optimizes performance using stack-trace analysis and LLM providers, supporting EMR and local sources.

Enables read-only SQL querying and exploration of data files (CSV, Parquet, JSON, Excel, etc.) via DuckDB, supporting local paths, globs, URLs, and S3 buckets.

Code-mode MCP server (docs_search + execute_code two-tool surface) backed by a unified capability manifest across three sandbox kernels (in-process node:vm, WASM via QuickJS / Pyodide / Wasmtime, and remote microVM via E2B / Cloudflare Sandbox). At N=30 tools the bootstrap-context cost drops to 13.6% of direct tool-use. Apache-2.0.

Provides full-text search and retrieval tools for Apache Spark documentation using SQLite FTS5 with BM25 ranking. It enables AI assistants to efficiently search, filter by section, and read specific Spark documentation pages.

A Model Context Protocol server that renders Apache ECharts options to SVG, enabling AI agents to generate deterministic charts server-side without a browser.

Read-only SQL across PostgreSQL, MySQL, S3-compatible buckets and folders of Parquet, CSV and JSON files — and one query can join across all of them, federated in-process by DuckDB. Sources are given as connection strings at startup, and no tool writes: they are absent rather than disabled.

An MCP server that enables AI assistants to interact with Apache Airflow's REST API for DAG management, task monitoring, and system diagnostics. It provides comprehensive tools for triggering workflows, retrieving logs, and inspecting system health across Airflow 2.x and 3.x versions.

MCP server for Hydra ETL: lets AI assistants write declarative YAML ETL pipelines (CSV, Parquet, PostgreSQL, MySQL, MongoDB, APIs) and validate them before any data is read or written.

Enables accessing and querying Dutch government open datasets from CBS and data.overheid.nl, with tools for searching, filtering, downloading, and analyzing data using CSV, Parquet, DuckDB, or Pandas.

Enables AI assistants to answer questions about Apache Iggy using the project's current guides and Rust API documentation, with search, retrieval, and documentation analysis tools.

MCP server for chatting with physical-world data from robotics, drones, automotive, and IoT sources using natural language. It generates auditable SQL queries over Apache Arrow/DuckDB to let you analyze, summarize, and build data pipelines.