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What are the data sharding techniques for scalability? Data sharding techniques for scalability include horizontal partitioning, where databases are split into smaller, distributed shards based on criteria like user ID or region. Each shard operates independently, reducing query latency and enabling parallel processing. Range-based sharding groups data by value ranges, while hash-based sharding uses algorithms to distribute data evenly. Consistent hashing minimizes rebalancing during scaling. Sharding is critical for high-traffic systems (e.g., social media, IoT), improving throughput and fault isolation while maintaining performance under load.

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