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Stream Processing and Complex Event Processing Engine
The main features of siddhi-io/siddhi are: Machine Learning, Streaming SQL.
Projects with overlapping indexed features include: donnemartin/data-science-ipython-notebooks — This project is a collection of interactive Python notebooks and educational resources designed for mastering data… pkmital/tensorflow_tutorials — This project is a collection of educational Jupyter Notebooks providing tutorials on neural network construction and… apache/flink — Apache Flink is a distributed processing engine designed for both high-throughput, low-latency data streams and finite… arroyosystems/arroyo — Arroyo is a high-performance stream processing platform built in Rust. It executes continuous SQL queries on streaming… albertsuarez/searchly — 🎶 Song similarity search API based on lyrics. aimhubio/aim — Aim is an open-source platform for logging, visualizing, and comparing machine learning training runs and LLM traces.…
Arroyo is a high-performance stream processing platform built in Rust. It executes continuous SQL queries on streaming data with event-time semantics, enabling accurate windowed aggregations, joins, and stateful computations on unbounded event streams. The platform uses native Rust execution for high throughput and low latency, with periodic checkpointing for exactly-once fault tolerance and horizontal scaling across distributed workers. The system integrates deeply with Kafka for reading and writing topics with exactly-once delivery and supports change data capture (CDC) from MySQL and Postg
This project is a collection of interactive Python notebooks and educational resources designed for mastering data science, machine learning, and numerical computing. It provides a series of practical guides and tutorials covering deep learning, big data processing, and statistical analysis. The repository features specialized instructional suites for implementing classical machine learning algorithms, building deep learning model architectures, and managing AWS cloud infrastructure. It includes dedicated notebooks for data visualization and numerical computing exercises. The project covers
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
This project is a collection of educational Jupyter Notebooks providing tutorials on neural network construction and tensor operations using the TensorFlow framework. It serves as a machine learning educational repository and implementation guide for deep learning students. The suite focuses on specific advanced architectures, including convolutional networks for image classification, residual networks with skip connections for training stability, and variational autoencoders for generative modeling and data synthesis. It also includes guides for building denoising and deep autoencoders to pe