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Eino is an open‑source Golang framework for building large‑model applications, offering reusable components, robust orchestration, clean APIs, best‑practice templates, and full‑cycle DevOps tools, with code examples for both Ollama and OpenAI modes, plus streaming and normal output options.
Learn how FastAPI handles various response types—including default JSON, custom status codes and headers, HTMLResponse, FileResponse, StreamingResponse, and user‑defined response classes—while covering best practices, common pitfalls, and code examples for building robust APIs.
This article explains why Kafka is widely adopted by top companies, outlines its high throughput, scalability, and durability, and describes key real‑time data pipeline, stream processing, and big‑data integration scenarios, concluding that mastering Kafka is essential for modern backend and data engineering roles.
Kafka 4.0 replaces ZooKeeper with the native KRaft (Kafka Raft) consensus layer, simplifying deployment, boosting scalability, cutting latency, and introducing a new incremental consumer rebalance protocol and share‑group model that together reshape how distributed streaming applications are built and operated.
This article explains how Huawei's terminal cloud adopts the Apache Iceberg table format to efficiently manage large-scale datasets, detailing its architecture, feature engineering, merge operations, LSM-based storage, schema versioning, AB testing support, catalog enhancements, and future roadmap for full lifecycle data governance.
This article introduces Spring Framework's ResponseBodyEmitter, explains its role in asynchronous HTTP responses, outlines typical use cases such as long polling and real‑time log streaming, provides a complete Spring Boot example with code, and compares it with SSE and raw OutputStream approaches.
The Kuaishou‑hosted Apache Hudi Asia technical salon gathered over 230 attendees and featured seven experts from Kuaishou, Meituan, TikTok, Huawei, JD and others, who shared best practices, architecture designs, and performance optimizations for large‑scale data lake applications across AI, BI, and real‑time workloads.
This article presents Huolala’s end‑to‑end implementation of Flink CDC for real‑time data capture, detailing the business background, reasons for selecting Flink CDC over Canal, component comparisons, production‑level platform enhancements, data‑lake integration, validation methods, and future directions for unified data ingestion.
This article explains the purpose, core methods, and practical scenarios of Spring Framework’s ResponseBodyEmitter, compares it with SSE and raw streaming, and provides a complete Spring Boot controller example that demonstrates how to implement real‑time log streaming and other asynchronous HTTP responses.
The Apache Hudi Asia technical salon held in Beijing on March 29 gathered over 230 on‑site participants and 16,000 online viewers, featuring expert talks from leading Chinese tech companies that showcased real‑world Hudi implementations, performance optimizations, and future roadmap for data‑lake technologies.
This article provides a comprehensive overview of Flink CDC 3.3, detailing its CDC fundamentals, new connectors, Transform module enhancements, asynchronous snapshot splitting, community adoption, and upcoming roadmap for broader ecosystem support and batch‑mode execution.
iQIYI transformed its real‑time data warehouse by replacing a costly Kafka‑based Lambda stack with a unified stream‑batch Iceberg lake, cutting storage expenses by 90%, halving compute costs, extending data retention, and delivering minute‑level freshness for 90% of use cases while preserving second‑level processing where needed.
Chronon is an open‑source ML feature platform that lets engineers declaratively define, compute, and serve both batch and real‑time features with built‑in observability, data‑quality checks, and a low‑latency retrieval API, ensuring online‑offline consistency while simplifying pipeline management and enabling future automation.
This article explains how Spring Framework's ResponseBodyEmitter enables real‑time, chunked HTTP responses for use cases such as log streaming, progress updates, chat, and AI output, detailing its advantages over SSE, usage scenarios, core methods, a complete controller example, and best‑practice considerations.
Apache Kafka 4.0 has been officially released as the first major version that runs entirely without Apache ZooKeeper, introducing KRaft mode, a new consumer group protocol (KIP‑848), early‑access queue support (KIP‑932), updated Java requirements, and other enhancements aimed at improving scalability, operability, and messaging versatility.
This article explains how Spring's ResponseBodyEmitter, introduced in Spring 4.2, enables lightweight asynchronous HTTP streaming for use cases like real-time logs, progress updates, chat, and AI responses, and provides a complete code example with best-practice guidelines.
Alibaba Mama’s advertising platform migrated from a monolithic Flink‑Kafka pipeline to a layered Paimon lakehouse, adding DWS upsert support and multi‑layer storage, which delivers minute‑level data freshness, cuts latency by 2.5 hours, reduces resource use over 40 %, halves development effort and achieves ≥99.9 % availability.
The article introduces DeepSeek4j 1.4, a Java Spring Boot library that overcomes existing framework limitations by preserving DeepSeek's chain‑of‑thought capabilities, adding full reactive streaming, and providing a simple one‑line API along with quick‑start instructions and code examples.
This article introduces Spring Framework's ResponseBodyEmitter, explains its advantages over SSE for asynchronous HTTP responses, demonstrates practical usage with a real‑time log‑streaming example, outlines core methods, working principles, best‑practice considerations, and compares it with traditional Streaming and Server‑Sent Events.
This article examines how Apache Iceberg’s snapshot‑based ACID transactions, logical‑physical partition evolution, and COW/MOR update modes enable efficient real‑time data lake ingestion, and demonstrates AutoMQ’s Kafka‑to‑Iceberg Table Topic solution that simplifies schema management, reduces latency, and cuts operational costs.