Distributed systems are a cornerstone of modern computing, enabling scalable and resilient applications across multiple nodes. Here are key principles to master:

  1. CAP Theorem
    The trade-off between Consistency, Availability, and Partition tolerance is fundamental.

    CAP_Theorem
    *Read more about the CAP theorem at [CAP_Theorem](/en/distributed_systems_introduction).*

  2. Eventual Consistency
    A model where data becomes consistent over time despite network partitions.

    Eventual_Consistency
    *Explore practical implementations in [Distributed_Systems_Practices](/en/distributed_systems_practices).*

  3. Distributed Consensus Algorithms
    Protocols like Paxos and Raft ensure agreement across nodes.

    Distributed_Consensus_Algorithms
    *Learn about consensus mechanisms in [Consensus_Algorithms](/en/distributed_systems_consensus).*

  4. Fault Tolerance & Replication
    Redundancy and failover strategies are critical for system reliability.

    Fault_Tolerance_Redundancy
    *Check our guide on [Fault_Tolerance](/en/distributed_systems_fault_tolerance).*

For hands-on projects, consider building a distributed key-value store or exploring microservices architecture. 🚀