Python Examples for running Apache Flink® Table API on Confluent Cloud
-
Updated
Oct 3, 2026 - Python
Python Examples for running Apache Flink® Table API on Confluent Cloud
Apache Flink examples designed to be run by AWS Kinesis Data Analytics (KDA).
Helps explain how Flink handles late arriving data and the effects on message order
Real-time GPU cost governance on Confluent Cloud for Apache Flink — energy-per-useful-token (J/1k) detection, forecasting & a closed remediation loop. Measured on IBM Granite-3.3-8B / NVIDIA L4.
学习项目:MySQL → CDC → Kafka → Flink SQL → ClickHouse 电商实时数仓,单机 docker compose 可完整跑通
Real-time gaming analytics pipeline — Kafka 4.0 + Flink 2.0 SQL + ClickHouse. Fully containerised with Docker Compose.
A Kubernetes operator that manages FlinkSQL Studio deployments as first-class CRDs — deploy, configure, and lifecycle-manage FlinkSQL Studio instances
Engaging, interactive visualizations crafted with Streamlit, seamlessly powered by Apache Flink in batch mode to reveal deep insights from data.
Modern Flink 2.x + Kafka 4.x + Iceberg lakehouse demos with SQL, PyFlink, and Java.
Matching card authorizations to settlements in Flink SQL, including the ones that arrive four days late and the ones that never arrive
Flink K8s Cluster Template
Real-time pipeline over Wikimedia's live edit firehose: Kafka + Flink SQL, event-time windows, MATCH_RECOGNIZE edit-war detection, exactly-once.
End-to-end data pipeline: PostgreSQL CDC (Debezium) → Kafka → Flink SQL → OpenSearch (search) + Redis (features). Local Docker stack, sample data, and verification scripts.
Confluent Cloud demo: live OpenSky flight data -> Kafka -> Flink SQL, with Schema Registry.
Project to stream data in Avro format from Netflix Kaggle dataset to a Kafka topic in Confluent Cloud, then analyze it with Flink SQL.
Real-time energy telemetry pipeline on Confluent Cloud: Python producer, Kafka + Schema Registry (Avro), Flink SQL windowed power usage and overload alerts
🍔 Build a production-grade data pipeline for food delivery, enabling real-time insights and features through PostgreSQL, Kafka, and Flink.
⚡ Real-time e-commerce lakehouse you can run on a laptop with one command: Kafka → Flink SQL → Apache Iceberg → Trino → Streamlit. Features exactly-once streaming, event-time watermarks, windowed aggregations, and a live dashboard that shows Iceberg snapshots, compaction and time travel.
To associate your repository with the flink-sql topic, visit your repo's landing page and select "manage topics."