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Kafka Avro Schema Evolution: Understanding Forward, Backward, and Full Compatibility

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Avro Schema Evolution: Understanding Forward, Backward, and Full Compatibility When working with event-driven architectures and streaming platforms like Kafka, schema evolution is crucial for maintaining system stability while allowing your data models to grow. Let's explore how Avro schema compatibility works using a real-world example from a sales order system. The Challenge: Evolving Return Status Enums Consider this Avro schema field that tracks return order statuses: { "doc": "Status code about this line in the Sales Order.", "name": "return_status", "type": [ "null", { "type": "enum", "name": "LineReturnStatus", "symbols": [ "NEW", "EXPECTED", "REGISTERED", "QUARANTINE", "RECEIVED", "INVOICED", "CANCELLED...
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Agentic AI - KHub-JPS

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  What is the best way to develop agentic ai systems ChatGPT said: Developing an effective  agent AI  (autonomous AI agent) depends on your goals, but the most successful approaches share a common structure. Here’s a concise, practical guide to building robust AI agents in 2025: 🚀  Best Way to Develop an Agent AI (2025 Guide) 1. Define the Agent’s Scope First Agents fail when they try to do everything. Start with: Clear objective  (e.g., “research a topic,” “automate email workflows,” “manage cloud infrastructure”) Constraints  (time, cost, safety boundaries) Environment  (web, files, APIs, local OS, cloud) 2. Choose a Modern Agent Framework These provide planning, memory, tools, and autonomy out-of-the-box: ⭐  Best current frameworks (2025) OpenAI Swarm  — lightweight multi-agent orchestration LangChain Agents  — tool-heavy, customizable AutoGen  (Microsoft) — multi-agent conversation loops LlamaIndex Agents  — strong retriev...
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