The article mainly introduces two applications of real-time big data based on Flink.
This article analyzes the practice of stream and batch unification for big data processing within Alibaba's core business scenarios.
This article shares the results of explorations into real-time data warehouses focusing on the evolution and best practices for data warehouses based on Apache Flink and Hologres.
이 블로그는 빅데이터 플랫폼 도입을 고려 중이고, 어떤 조합으로 시스템을 구축할지 고민이신 분들을 위해 알리바바가 제공하는 모든 서비스들을 나열해 놓고, 각 서비스들의 적용 가능한 시나리오와 서비스 도입 시 고려해야 할 점등을 설명합니다.
This article mainly introduces Flink fault tolerance mechanism principles along with stateful stream computing, global consistency snapshots, and Flink state management.
This article focuses on the underlying Flink Runtime Architecture with four parts, including runtime overview, Jobmaster, TaskExecutor, and ResourceManager.
This article introduces the application of Realtime Compute for Apache Flink with Weibo.
This article focuses on the best practice of Hologres in Taobao's marketing analysis scenarios.
This article focuses on how Hologres is implemented in the real-time data warehouse scenario of Fliggy.
This article focuses on Hologres' best practice of successfully replacing Apache Druid in the Alibaba Network Monitoring Department.
Abstract: This article introduces the storage engine of Hologres and deeply analyzes its implementation principles and core technical advantages.
This article provides an in depth introduction to the architecture, application, and best practices of real-time financial data lakes by Zhongyuan Bank.
This article mainly introduces the current development and future plans of Flink as a unified stream-batch processing engine.
This post summarizes the key takeaways from the 2020 Flink Forward Asia (FFA) Conference hosted in Beijing, China.
This article explains real-time data lakes based on Apache Flink and Apache Iceberg.
This blog shares Alibaba Cloud's suite of real-time big data products and solutions to help enterprises make real-time decisions.
This article analyzes problems that may occur when using Apache Flink for large states and offers several solutions to overcome these issues.
This article explores Flink resource management mechanism from three aspects: basic concepts, current mechanisms and policies, and future development directions.
This article mainly describes the CheckPoint mechanism, backpressure mechanism, and memory model of Flink.
This article introduces the evolution of container management systems and discusses the best practices of using Apache Flink on Kubernetes.
