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  • Decentralized essence: This refers to a system design that does not rely on a central point of control. Instead, operations are distributed across multiple locations or nodes, which can increase resilience and fault tolerance.
  • Distributed storage methodologies: These are techniques used to store data across multiple physical locations, often using a network of computers. This approach can help with data redundancy, ensuring that if one storage site fails, the data is still accessible from another location.
  • Sharding mechanisms: Sharding is a type of database partitioning that separates very large databases into smaller, faster, more easily managed parts called data shards. Each shard is a distinct subset of data, and the shards are distributed across a distributed database system.
  • Seamlessly scalable: This suggests that the system can grow in capacity without significant interruptions or reconfiguration. As demand increases, more resources can be added to the system without affecting its performance or availability.
  • Load-efficient infrastructure: This implies that the system is designed to handle operations efficiently, distributing the workload in a way that optimizes resource use and minimizes bottlenecks or points of failure.