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Guided learning journeys that build knowledge step by step.
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This learning path equips energy engineers with the knowledge to design and implement distributed systems for smart grid applications. It covers IoT integration, decentralized energy management, data processing, and reliability, building from foundational concepts to advanced architectural patterns.
This learning path equips SREs and developers with the knowledge and skills to design, implement, and operate observability for distributed systems. It covers foundational concepts, the three pillars (metrics, logs, traces), and the integration of these signals for effective troubleshooting and performance analysis.
This learning path equips DevOps and SRE professionals with the knowledge and skills to deploy and manage distributed systems at scale. Starting with containerization fundamentals, it progresses through orchestration platforms, service mesh, and configuration management, culminating in advanced operational practices for production readiness.
A professional learning path for developers and architects to understand and build applications on serverless distributed platforms like AWS Lambda. It covers core distributed systems concepts, serverless architecture, event-driven design, function orchestration, state management, and operational practices.
This path equips data engineers with the knowledge and skills to design and build distributed stream processing applications. It covers core stream processing concepts, distributed systems fundamentals, and hands-on experience with Apache Flink and Kafka Streams for stateful, event-time-based processing.
This learning path equips systems engineers with the knowledge to design and implement distributed storage systems. It covers core distributed systems concepts, storage architectures, replication, consistency, erasure coding, and durability, culminating in a capstone design project.
A focused learning path for developers who want to build distributed systems in Go. It covers Go concurrency primitives, communication patterns with gRPC and protobufs, and the practical aspects of building and operating microservices.
A graduate-level learning path for researchers and engineers to master simulation of distributed systems. It covers foundational distributed systems concepts, discrete-event simulation theory, and hands-on experience with major network simulators (OMNeT++, ns-3) and emulation tools (Mininet). The path emphasizes building a mental model of simulation, applying it to real distributed systems, and validating results.
This learning path equips students and engineers with the skills to evaluate distributed systems performance. It covers fundamental concepts, metrics, benchmarking, workload generation, performance modeling, and statistical analysis, providing a structured approach to performance evaluation.
This path equips researchers and advanced students with the game-theoretic concepts necessary to analyze and design distributed systems, covering mechanism design, incentives, auctions, and reputation systems.