Memory-Optimized Tables enhance performance (5-30x) for high-throughput OLTP workloads by storing data in memory and removing traditional locking. They excel in extreme concurrency, heavy tempdb usage, or thousands of transactions per second, requiring specific implementation steps like memory-optimized filegroups and precise index planning. Though not for all workloads, when correctly implemented for suitable cases, they can significantly improve application performance and address complex concurrency problems.
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