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Tags: Data pipelines and messaging
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1–8 of 8 · Most stars

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.

Desbordante is a high-performance data profiler that is capable of discovering many different patterns in data using various algorithms. It also allows to run data cleaning scenarios using these algorithms. Desbordante has a console version and an easy-to-use web application.

A fast to develop, fast to run, Go based toolkit for ETL and feature extraction on Hadoop.

Real-time decision features without streaming infra. Turn live events into product reflexes — no Kafka, no Flink, no feature store.

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.

A production-grade messaging abstraction for Go. It provides a unified API to decouple business logic from various brokers including Kafka, RabbitMQ, RocketMQ, NATS, Redis, and AWS SQS. Featuring built-in OpenTelemetry integration and engineered for high reliability.