AutoStream Real‑Time Computing Platform: Architecture, Resource Management, Scaling, Lakehouse Integration, and PyFlink Practices

This article details Car Home's AutoStream platform evolution from Storm to Flink‑based versions, covering real‑time application scenarios, strict budget‑controlled resource management, automatic scaling, lake‑house architecture with Iceberg, PyFlink integration, and future plans for resource optimisation and batch‑stream unification.

AutoStreamFlinkLakehouse

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AutoStream Real‑Time Computing Platform: Architecture, Resource Management, Scaling, Lakehouse Integration, and PyFlink Practices

Alibaba Cloud Developer

Mar 17, 2022 · Big Data

How AutoStream Scales Real‑Time Data Processing with Flink, Iceberg, and PyFlink

This article details AutoStream's evolution from a Java‑only Storm platform to a Flink‑based, Kubernetes‑native streaming system that integrates budgeting controls, automatic scaling, lakehouse architecture with Iceberg, and PyFlink support, highlighting the technical challenges, solutions, and future roadmap for real‑time analytics.

FlinkIcebergLakehouse

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How AutoStream Scales Real‑Time Data Processing with Flink, Iceberg, and PyFlink

Big Data Technology & Architecture

Aug 25, 2021 · Big Data

Apache Flink Release History and Key Features from 1.7 to 1.12

This article provides a comprehensive overview of Apache Flink's major releases from version 1.7 through 1.12, detailing new functionalities such as Scala 2.12 support, state schema evolution, Blink planner integration, Kubernetes native deployment, Python (PyFlink) enhancements, and numerous performance and stability improvements for stream and batch processing.

Apache FlinkKubernetesPyFlink

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Apache Flink Release History and Key Features from 1.7 to 1.12

Huolala Tech

Apr 8, 2021 · Big Data

Mastering PyFlink on Kubernetes: Practical Deployment Strategies and Lessons

This article explains how to deploy a PyFlink feature platform on Kubernetes, covering basic K8s concepts, Flink execution graphs, various deployment modes, preparation steps, detailed Standalone and Native deployment procedures, and practical tips for efficient big‑data processing.

Cloud NativeDeploymentFlink

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Mastering PyFlink on Kubernetes: Practical Deployment Strategies and Lessons

Alibaba Cloud Developer

Jul 13, 2020 · Big Data

What’s New in Apache Flink 1.11? A Deep Dive into Features and Performance

Apache Flink 1.11.0, released after four months of development, brings major ecosystem, usability, and stability improvements—including CDC support, a new JDBC catalog, real‑time Hive integration, a redesigned source API, PyFlink enhancements, application mode for Kubernetes, and checkpoint optimizations—while highlighting the growing contribution of Chinese developers.

Apache FlinkCheckpointFeature Release

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What’s New in Apache Flink 1.11? A Deep Dive into Features and Performance