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Revert "Update analytical-store-introduction.md"
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articles/cosmos-db/analytical-store-introduction.md

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By using horizontal partitioning, Azure Cosmos DB transactional store can elastically scale the storage and throughput without any downtime. Horizontal partitioning in the transactional store provides scalability & elasticity in auto-sync to ensure data is synced to the analytical store in near real time. The data sync happens regardless of the transactional traffic throughput, whether it's 1000 operations/sec or 1 million operations/sec, and it doesn't impact the provisioned throughput in the transactional store.
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## Automatically handle schema updates
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## <a id="analytical-schema"></a>Automatically handle schema updates
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Azure Cosmos DB transactional store is schema-agnostic, and it allows you to iterate on your transactional applications without having to deal with schema or index management. In contrast to this, Azure Cosmos DB analytical store is schematized to optimize for analytical query performance. With the auto-sync capability, Azure Cosmos DB manages the schema inference over the latest updates from the transactional store. It also manages the schema representation in the analytical store out-of-the-box which, includes handling nested data types.
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> [!NOTE]
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> You can only read from analytical store using Azure Synapse Analytics runtimes. And the opposite is also true, Azure Synapse Analytics runtimes can only read from analytical store. Only the auto-sync process can change data in analytical store. You can write data back to Cosmos DB transactional store using Azure Synapse Analytics Spark pool, using the built-in Azure Cosmos DB OLTP SDK.
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## Pricing
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## <a id="analytical-store-pricing"></a> Pricing
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Analytical store follows a consumption-based pricing model where you're charged for:
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