FeatHub - A stream-batch unified feature store for real-time machine learning
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Updated
May 27, 2024 - Python
FeatHub - A stream-batch unified feature store for real-time machine learning
Simple stream processing pipeline
Adapter for dbt that executes dbt pipelines on Apache Flink
📡 Real-time data pipeline with Kafka, Flink, Iceberg, Trino, MinIO, and Superset. Ideal for learning data systems.
Streaming Synthetic Sales Data Generator: Streaming sales data generator for Apache Kafka, written in Python
Jupyter Integration for Flink SQL via Ververica Platform
A complete data engineering project demonstrating modern data stack practices with Apache Flink, Iceberg, Trino and Superset
Prototype which extracts stateful dataflows by analysing Python code.
This repo demonstrates how to use AWS application auto-scaling to implement custom-scaling in your Kinesis Data Analytics for Apache Flink applications
A Smart Traffic Management System for Ho Chi Minh City, Vietnam leveraging batch and real-time data processing, intuitive dashboards, and monitoring tools to optimize traffic flow, enhance safety, and support sustainable urban mobility through advanced analytics and user-friendly applications.
Python Examples for running Apache Flink® Table API on Confluent Cloud
Apache Flink MCP Server is a Model Context Protocol (MCP) implementation that empowers AI assistants and large language models to interact directly with Apache Flink clusters through natural language. It enables intelligent monitoring, management, and analysis of real-time streaming applications—making stream processing more intuitive, accessible.
A streaming data platform processing live crypto ticks through six tiers with no traditional database.
A flinksql-mlflow-pytorch implementation
AUTH: Analytics of Utility Things is a platform for ingesting, processing and extracting insights from next billion connected Internet of Things (IoT).
Helps explain how Flink handles late arriving data and the effects on message order
Real-time monitoring pipeline using Kafka, Flink, PostgreSQL, and Grafana to stream metrics, detect anomalies (EWMA + 3σ), and visualize results.
Airflow to maintain operation of Flink pipeline, Doris database and Kafka.
Declarative Apache Flink Statefun over FastAPI
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