Discover how modern enterprises can achieve comprehensive monitoring by integrating AutoMQ, Prometheus, and VictoriaMetrics. This tutorial provides detailed insights into the functionality and advantages of each component, along with step-by-step deployment and configuration instructions.

Explore the concept of Tiered Storage introduced by Apache Kafka KIP-405 in Kafka 3.9. Learn how it addresses the challenges of Kafka&'s legacy architecture by efficiently managing long-term data, reducing operational burden, and optimizing cloud storage economics.

Discover how AutoMQ supports running entirely on S3 object storage with minimal code changes. Learn about the shared storage architecture, the innovative S3Stream engine, and how AutoMQ leverages WAL to provide low-cost, low-latency Kafka deployments on S3. This article shares the design insights and evolution process of AutoMQ&'s shared stream storage engine, demonstrating why only hundreds of lines of code are needed to implement Kafka on top of S3.

Discover how to integrate AutoMQ, Airbyte, and Databricks to enhance real-time data processing and analysis. This comprehensive guide helps enterprises leverage the efficient stream processing of AutoMQ, flexible data integration of Airbyte, and powerful data analytics of Databricks to make informed decisions quickly.

Explore the advanced observability architecture of AutoMQ and learn how it achieves multi-cloud observability through seamless integration with various monitoring backends. This comprehensive guide covers metric collection, export methods, optimized data handling, and practical implementation in cloud-native environments.

Learn about the critical operation of partition reassignment in Apache Kafka, including key features like load balancing and replication scaling. Discover how AutoMQ improves this process with its shared storage architecture, enabling faster and more efficient reassignments to enhance cluster performance and stability.

Explore the top five emerging trends in data streaming for 2025, including AI-powered stream processing, enhanced privacy measures, edge computing, distributed streaming, and cloud-native architectures. Learn how AutoMQ is poised to lead with cutting-edge features that address these trends, offering AI-driven optimizations, robust security, edge compute integrations, and flexible BYOC solutions for modern data architectures.

Dive into the Go client ecosystem for Apache Kafka, exploring major libraries like Confluent Kafka Go, Franz-go, and SegmentIO kafka-go. Discover their features, performance, and use-case suitability. Learn how AutoMQ ensures 100% protocol compatibility while offering cloud-native enhancements, making it a cost-efficient and scalable alternative for Kafka-based systems in Go.

Explore Kafka KRaft, the consensus protocol introduced in Apache Kafka 2.8.0. Learn how KRaft simplifies Kafka&'s architecture, improves scalability, enhances fault tolerance, and eliminates the need for Apache ZooKeeper. Discover how AutoMQ leverages KRaft for efficient metadata management and scalable stream processing.