A curated list of awesome big data frameworks, ressources and other awesomeness.

Apache Kafka running on Kubernetes

Probabilistic data structures for processing continuous, unbounded streams.

An open-source framework for real-time anomaly detection using Python, ElasticSearch and Kibana

NIST Certified SCAP 1.2 toolkit

A stream processing API for Go (alpha)

Go stream processing library

Series and Panels for Real-time and Exploratory Analysis of Data Streams

Simple yet powerful live data computation framework

The Open Source Time-Series Data Historian

Tideland GoCells (Event Based Applications)

The Tornado 🌪️ framework, designed and implemented for adaptive online learning and data stream mining in Python.

Appbase.io streaming client lib for Javascript

A framework for data stream modeling and associated data mining tasks such as clustering and classification. - R Package

Learn how to use Kinesis Firehose, AWS Glue, S3, and Amazon Athena by streaming and analyzing reddit comments in realtime. 100-200 level tutorial.

MIST: High-performance IoT Stream Processing

HyperLogLog and other probabilistic data structures for mining in data streams

A Node.js and JavaScript synchronous data pipeline processing, data sharing and stream processing library. Actionable & Transformable Pipeline data processing.

unsupervised concept drift detection

A library for performing Content-Defined Chunking (CDC) on data streams.

Simple cloud based logger for microcontrollers.

RPJiOS: RPJ's RPi OS, a sensor data platform for the Raspberry Pi built with python2.7 and redis.

A simple C# network library.

Verifies data streams from synchrophasor measurement devices

Kafka-ML: connecting the data stream with ML/AI frameworks (now TensorFlow)

非结构化课程作业，包括社交网络、链路预测、数据流、文本分析

Real-time data stream classification and knowledge generation engine with no dependencies

Library to connect to the Azure Event Hub via AMQP 1.0 for the Go programming language (Golang) based on Apache Qpid Proton (an AMQP 1.0 C library)

Twitter based sentiment analysis using JAVA and Hadoop. In this project we are doing the sentiment analysis on twitter data to analyse whether the tweets posted by people are positive or negative or neutral by checking the tweets with the AFFIN dictionary which has a set of 2500 words along with the value of each word ranging from -5 to +5 denoting whether tweets are positive or negative.