Orchestrate everything, everywhere. The open-source control plane for workflows across data, infrastructure, business processes and AI agents. Declarative YAML, event-driven, any language, Git-versioned, scalable to millions of executions.
TranslatedOntiCards is an enterprise-oriented AI Data Hub that offers one-stop capabilities around business databases: data source connection, metadata understanding, natural language query, cross-source fusion, data quality inspection, query auditing, data asset management, and more
The Open Context Layer for Data and AI , OpenMetadata is the open platform for building trusted data context and business semantics for humans, AI assistants, and agents.
Convert documents to structured data effortlessly. Unstructured is open-source ETL solution for transforming complex documents into clean, structured formats for language models. Visit our website to learn more about our enterprise grade Platform product for production grade workflows, partitioning, enrichments, chunking and embedding.
Open-source data movement for ELT pipelines and AI agents — from APIs, databases & files to warehouses, lakes, and AI applications. Both self-hosted and Cloud.
Scalable real-time messaging server in a language-agnostic way. Self-hosted alternative to Pubnub, Pusher, Ably, socket.io, Phoenix.PubSub, SignalR. Set up once and forever.
Open-source ETL/ELT you deploy on your own servers or cloud. Built on DuckDB: no-code/low-code visual pipelines or SQL, 385 components, dbt, CDC, data quality, reverse ETL, lineage, MCP for AI agents. No vendor cloud, no per-row billing.
Evidently is an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.
Apache Camel is an open source integration framework with 350+ connectors. Write routes in Java, YAML, or XML. Run on Spring Boot, Quarkus, or standalone. Apache License 2.0.
An open-source storage framework that enables building a Lakehouse architecture with compute engines including Spark, PrestoDB, Flink, Trino, and Hive and APIs
Asynchronous Python framework for event-driven services. A thin client for Kafka, RabbitMQ, NATS, Redis and MQTT with full access to native broker features, plus AsyncAPI docs, in-memory tests and observability out of the box.
High performance Node.js/PostgreSQL job queue (also suitable for getting jobs generated by PostgreSQL triggers/functions out into a different work queue)
Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
Meltano: the declarative code-first data integration engine that powers your wildest data and ML-powered product ideas. Say goodbye to writing, maintaining, and scaling your own API integrations.
KubeBlocks is a Kubernetes Operator designed to manage a variety of databases and streaming systems, including MySQL, PostgreSQL, MongoDB, Redis, RabbitMQ, RocketMQ, and more, within Kubernetes environments.
Hazelcast is a unified real-time data platform combining stream processing with a fast data store, allowing customers to act instantly on data-in-motion for real-time insights.
Data pipelines for cloud config and security data. Build cloud asset inventory, CSPM, FinOps, and vulnerability management solutions. Extract from AWS, Azure, GCP, and 70+ cloud and SaaS sources.
Apache Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage/tracing and metadata. Runs and scales everywhere python does.
The fastest unified streaming SQL Engine in a single C++ binary. ⚡ Millisecond latency. 100+ GB/s throughput. Continuously compute stateful context from streams, logs, metrics, events, and CDC.