Java Event-Driven Solutions in Azure: Real-Time Data Pipelines โ€” WalkSelf
โฑ 2 oras 30 min ๐Ÿ“š 25 aralin ๐ŸŽง Audio version

Java Event-Driven Solutions in Azure: Real-Time Data Pipelines

Master the fundamentals of ingestion, serverless processing, and cloud storage for real-time telemetry using Java, Azure Event Hubs, Azure Functions, and Cosmos DB.

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Tungkol sa kursong ito

Modern cloud architectures rely on real-time data streaming to power responsive, scalable applications. If you want to build robust event-driven systems using Java, understanding how to harness the power of cloud-native services is essential. This text-based course guides you through the process of building a complete telemetry pipeline. You will learn to ingest high-volume stream data, process it instantly using serverless architecture, and persist it in a globally distributed database. By reading through clear explanations and studying practical code snippets, you will master the principles of event-driven architecture from the ground up. \n\nWhat you'll learn: \n- Understand the core concepts of event-driven architecture and real-time telemetry. \n- Configure Azure Event Hubs to ingest high-throughput data streams. \n- Write Java-based Azure Functions to process incoming events serverless-style. \n- Store structured telemetric data efficiently in Azure Cosmos DB. \n- Apply modern Java features and Azure SDKs for clean, maintainable cloud code. \n- Implement basic monitoring and logging to observe your pipeline's health. \n\nWe begin with the foundational terminology of event hubs, serverless computing, and NoSQL databases. From there, you will explore the step-by-step construction of a data pipeline, learning how to connect each component securely and handle data serialization effectively. This course is designed for beginner backend developers, cloud enthusiasts, and Java programmers new to event-driven architectures. No prior cloud experience is required, as we start with the absolute basics. Start reading today to build your first production-ready real-time data pipeline in the cloud.

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    2 oras 30 min ng practical content

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