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Data sharding techniques for scalability include horizontal partitioning (splitting tables by row ranges), vertical sharding (dividing by columns), and directory-based sharding (using lookup tables for location mapping). Range-based sharding groups data by key values (e.g., dates), while hash-based sharding distributes data evenly using cryptographic hashes. Geographic sharding routes queries to regional servers, reducing latency. Hybrid approaches combine methods for complex datasets, ensuring balanced workloads across distributed systems.

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