This article introduces MQ for Kafka, Kafka Connect, ecosystem planning, and connectivity issues.
This blog provides an in-depth overview of the Kafka messaging system along with a walkthrough of its various models and techniques.
This article focuses on the various log collection solutions provided by Serverless Application Engine (SAE) as well as related architectures and scenarios.
This article discusses the basics of Apache Hudi, Flink Hudi integration, and use cases.
This article introduces how to deploy General DBAudit (a lightweight, low-cost database security solution) in the Kubernetes environment.
This sharing introduces the architecture model design for device messaging application scenarios and how to implement an integrated messaging platform based on RocketMQ.
This article explains how Apache Kafka works and how to deploy it.
This article describes the usage scenarios and problems of MQ in several typical businesses.
The article mainly introduces two applications of real-time big data based on Flink.
본 블로그를 참조하여 알리바바의 대표적인 MQ 서비스인 Message Queue for RocketMQ, RabbitMQ, Kafka, MQTT에 대한 설명과 비교, 적절한 사용 시나리오에 대해 알아볼 수 있습니다.
In this article, Alibaba technical expert Aohai introduces the basic concepts and architecture of an enterprise-level recommender system.
This article aims to give readers a deeper understanding of Alibaba Cloud Data Lake Formation (DLF) and Databricks DataInsight (DDI).
This article introduces the optimization and evolution of Flink Hudi's original mini-batch-based incremental computing model through stream computing.
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 tutorial shows you how to synchronize Redis data with ApsaraDB RDS for MySQL using Canal and Kafka.
This article describes how to use Knative to implement practices with event-driven framework for users in different scenarios.
This article explains the background of Delta Lake along with practices, problems, and solutions.
In this article, the author explains building a real-time data warehouse using Apache Flink and Apache Iceberg.
This article introduces the major changes and new features of Flink 1.11
This article introduces the enhanced capabilities of Flink 1.11 to support SQL to process batch and streaming data
