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.
- camel
- integration
- java
- microservices
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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.
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.
Open-source low code data preparation library in python. Collect, clean and visualization your data in python with a few lines of code.
Apache Spark to Apache Cassandra connector
MongoDB data stream pipeline tools by YouGov (adopted from MongoDB)
EventMesh is a new generation serverless event middleware for building distributed event-driven applications.
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.
Enterprise Network Flow Collector (IPFIX, sFlow, Netflow)
Data structure and algorithm library for go, designed to provide functions similar to C++ STL
📈 Collect customer event data from your apps. (Note that this project only includes the API collector, not the visualization platform)
The MongoDB Spark Connector
Job Queue for Elixir. Clustered or Local. Straight BEAM. Optional Ecto. 💪🍈
Multi-Modal Database replacing MongoDB, Neo4J, and Elastic with 1 faster ACID solution, with NetworkX and Pandas interfaces, and bindings for C 99, C++ 17, Python 3, Java, GoLang 🗄️
The Apache Spark - Apache HBase Connector is a library to support Spark accessing HBase table as external data source or sink.
Neo4j Connector for Apache Spark, which provides bi-directional read/write access to Neo4j from Spark, using the Spark DataSource APIs
A highly optimized double-ended queue
Read and write Neuroglancer datasets programmatically.
Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.
Open-source streaming SQL engine written in Rust using Apache Arrow and DataFusion. Supports continuous queries, temporal stream joins, tumbling/session windows, and CDC/Kafka connectors. Lightweight, embeddable, and sub-microsecond latency