Merge pull request #234985 from Rodrigossz/main

TTL info

Author: Jill Grant

articles/cosmos-db/analytical-store-change-data-capture.md

@@ -14,10 +14,7 @@ ms.date: 04/03/2023
 
 [!INCLUDE[NoSQL, MongoDB](includes/appliesto-nosql-mongodb.md)]
 
-Change data capture (CDC) in [Azure Cosmos DB analytical store](analytical-store-introduction.md) allows you to efficiently consume a continuous and incremental feed of changed (inserted, updated, and deleted) data from analytical store. The change data capture feature of the analytical store is seamlessly integrated with Azure Synapse and Azure Data Factory, providing you with a scalable no-code experience for high data volume. As the change data capture feature is based on analytical store, it [doesn't consume provisioned RUs, doesn't affect your transactional workloads](analytical-store-introduction.md#decoupled-performance-for-analytical-workloads), provides lower latency, and has lower TCO.
-
-> [!IMPORTANT]
-> This feature is currently in preview.
+Change data capture (CDC) in [Azure Cosmos DB analytical store](analytical-store-introduction.md) allows you to efficiently consume a continuous and incremental feed of changed (inserted, updated, and deleted) data from analytical store. Seamlessly integrated with Azure Synapse and Azure Data Factory, it provides you with a scalable no-code experience for high data volume. As the change data capture feature is based on analytical store, it [doesn't consume provisioned RUs, doesn't affect your transactional workloads](analytical-store-introduction.md#decoupled-performance-for-analytical-workloads), provides lower latency, and has lower TCO.
 
 The change data capture feature in Azure Cosmos DB analytical store can write to various sinks using an Azure Synapse or Azure Data Factory data flow.
 
@@ -27,13 +24,16 @@ For more information on supported sink types in a mapping data flow, see [data f
 
 In addition to providing incremental data feed from analytical store to diverse targets, change data capture supports the following capabilities:
 
-- Supports applying filters, projections and transformations on the Change feed via source query
 - Supports capturing deletes and intermediate updates
 - Ability to filter the change feed for a specific type of operation (**Insert** | **Update** | **Delete** | **TTL**)
-- Each change in Container appears exactly once in the change data capture feed, and the checkpoints are managed internally for you
-- Changes can be synchronized from “the Beginning” or “from a given timestamp” or “from now”
-- There's no limitation around the fixed data retention period for which changes are available
+- Supports applying filters, projections and transformations on the Change feed via source query
 - Multiple change feeds on the same container can be consumed simultaneously
+- Each change in container appears exactly once in the change data capture feed, and the checkpoints are managed internally for you
+- Changes can be synchronized "from the Beginning” or “from a given timestamp” or “from now”
+- There's no limitation around the fixed data retention period for which changes are available
+
+> [!IMPORTANT]
+> Please note that "from the beginning" means that all data and all transactions since the container creation are availble for CDC, including deletes and updates. To ingest and process deletes and updates, you have to use specific settings in your CDC processes in Azure Synapse or Azure Data Factory. These settings are turned off by default. For more information, click [here](get-started-change-data-capture.md)
 
 ## Features
 
@@ -60,6 +60,11 @@ WHERE Category = 'Urban'
 > [!NOTE]
 > If you would like to enable source-query based change data capture on Azure Data Factory data flows during preview, please email [[email protected]](mailto:[email protected]) and share your **subscription Id** and **region**. This is not necessary to enable source-query based change data capture on an Azure Synapse data flow.
 
+### Multiple CDC processes
+
+You can create multiple processes to consume CDC in analytical store. This approach brings flexibility to support different scenarios and requirements. While one process may have no data transformations and multiple sinks, another one can have data flattening and one sink. And they can run in parallel.
+
+
 ### Throughput isolation, lower latency and lower TCO
 
 Operations on Cosmos DB analytical store don't consume the provisioned RUs and so don't affect your transactional workloads. change data capture with analytical store also has lower latency and lower TCO. The lower latency is attributed to analytical store enabling better parallelism for data processing and reduces the overall TCO enabling you to drive cost efficiencies in these rapidly shifting economic conditions.