A collection of demonstrations showcasing how stream processing can be used to solve real-world problems.

Kafka Streams for Low Code Environments

A scalable, declarative, low-code framework for real-time and batch feature calculation/management (quant finance, anomaly/fraud detection, etc.), predictive ML training/inference and simulation. Built on top of Ray

Postgres Logical Replication plugin for benthos

Repository containing configurations for the benchmarks included in RSP Lab Suite

A data ingestion adaptor to plug data from source to sink with a configuration based pipeline

This is the graduate thesis work for my Computer Science Major

This project utilizes MediaPipe's Face Mesh solution to perform real-time face landmark detection, accurately identifying 468 3D landmarks on the human face. Currently, it focuses on the landmark extraction functionality, enabling applications such as facial feature tracking and augmented reality effects.

This project demonstrates the use of Apache Kafka with Spring Cloud Streams for real-time data processing.

Simple stream processing library for synchronous or parallel and non-distributed execution.

A .NET stream processing

The aim of this big data project is to design and implement a big data system that can provide real-time context-aware recommendations to drivers on the level of possible danger.

The repo simulates query load, optimizes performance, and offers practical guidance for building data warehouses with Azure Synapse Analytics. 🚀

Stream & aggregate tweets containing a set of track terms in memory and write aggregates to rocks db. Aggregates include top hashtags, top mentions and top retweets. Contains a local executable that can run forever, computing aggregates and storing results in a local rocks DB. Also has repl mode for querying results from the db.

A sample real-time streaming analytics application with Spark Structured Streaming and Kafka.

GitHub Analytics Using Apache Pulsar - Streaming Analytics Solution

simple stream processing with event driven services. using nodejs, rabbitmq, apache kafka, mongodb and redis