This article provides deep insights into the data lake concept and compares some common solutions available in the market.
This is an extra article from the 10-part series, discussing the engineering implementation of Paxos.
Part 6 of this 10-part series focuses on the source codes of the distributed deadlock detection function in PolarDB-X.
This short article explains the benefits of Alibaba Cloud Tablestore.
Distributed transactions can be used to solve consistency problems during data replication, but that's not the whole purpose of them, at least not just that.
The combination of the two characteristics of replication - master-slave and timeliness, is causing data consistency risks.
What makes a system reliable is its high availability and high SLA. Today we look at how distributed service runs stably so that users can continue to benefit from its capabilities.
This article describes the resource definition, visualized control capability, and distributed batch processing capability of the task scheduling platform.
This article explains the zero-trust concept and how to use it to enhance application security in ASM.
This article reviews Alibaba Cloud's enterprise-level cloud-native data lake solution launched during the double 11 festival and discusses its key benefits.
This article introduces the EPaxos algorithm in a simple and easy-to-understand way, suitable even for those with basic knowledge of Paxos or Raft algorithms.
This article introduces the core protocol process of EPaxos from the perspective of the comparison between Paxos and EPaxos.
This article discusses the practices and challenges of EMR Spark on Alibaba Cloud Kubernetes.
This article mainly introduces Flink fault tolerance mechanism principles along with stateful stream computing, global consistency snapshots, and Flink state management.
This article describes the basic concepts, importance, development, and current applications of Apache Flink.
This post describes the core capabilities of MaxCompute and discusses its advantages through several use cases.
Edge computing is a distributed computing concept that integrates intelligence to edge devices, also called edge nodes, allowing data to be processed .
This article discusses the challenges and limitations of various solutions in CDC data analysis and describes how to use Flink and Iceberg to overcome them.
In this article, the author explains building a real-time data warehouse using Apache Flink and Apache Iceberg.
In this article, the author discusses how Apache Flink and Apache Iceberg have opened a new chapter in building a data lake architecture featuring stream-batch unification.