Projects sharing features with Heron

Apache Flink is a distributed processing engine designed for both high-throughput, low-latency data streams and finite batch workloads. It functions as a stateful stream processor and a SQL stream processing engine, providing a unified runtime to execute relational queries and event-based transformations. The system is distinguished by its ability to manage persistent operator state to ensure exactly-once processing guarantees and consistency during failures. It features specialized capabilities for complex event processing to detect temporal patterns and handles out-of-order events using eve

Hadoop is a big data infrastructure suite and distributed data processing framework designed to store and process massive datasets across clusters of computers. It consists of a distributed storage system for managing large files across multiple nodes and a parallel computing engine for processing data across a distributed cluster. The framework implements a distributed file system to ensure fault tolerance and high throughput, paired with a programming model that processes large datasets in parallel. It manages the underlying hardware and software environment required for distributed big dat

Hadoop MapReduce in idiomatic Clojure.

Tuple MapReduce for Hadoop: Hadoop API made easy

The metric correlation component of Etsy's Kale system

Introduction to the MR4C repo

Please visit https://github.com/h2oai/h2o-3 for latest H2O

Develop streaming applications for IBM Streams in Python, Java & Scala.

Reactive Microservices for the JVM

A distributed data integration framework that simplifies common aspects of big data integration such as data ingestion, replication, organization and lifecycle management for both streaming and batch data ecosystems.

ZooKeeper client wrapper and rich ZooKeeper framework

Hystrix is a latency and fault tolerance library designed to prevent cascading failures in distributed systems. It functions as a circuit breaker implementation that monitors failure thresholds and opens circuits to isolate remote calls when downstream services degrade. The project distinguishes itself by providing multiple isolation mechanisms, utilizing dedicated thread pools and semaphores to ensure that latency in one dependency does not saturate the entire system. It also features a request collapsing and batching engine that groups concurrent calls into single executions to reduce the t

Map-Reduce for Clojure

Oryx 2: Lambda architecture on Apache Spark, Apache Kafka for real-time large scale machine learning

Ray is a distributed computing framework designed to scale Python and Java applications across clusters by abstracting task scheduling and resource management. It functions as a resource-aware execution engine that manages task dependencies, placement, and fault tolerance across networked compute nodes. At its core, the system provides a stateful actor model, allowing developers to define classes that run in dedicated processes to maintain and mutate internal state across remote method calls. The framework distinguishes itself through a robust cross-language interoperability layer, enabling f

High performance distributed data processing engine

Twitter's collection of LZO and Protocol Buffer-related Hadoop, Pig, Hive, and HBase code.

A Scala API for Cascading

Streaming MapReduce with Scalding and Storm

Big Data Science Swiss Army Knife - http://www.tuktu.io --