Merge pull request #270234 from jonburchel/patch-39

Remove references to memory optimized compute

Author: JamesJBarnett

articles/data-factory/.openpublishing.redirection.data-factory.json

@@ -1090,7 +1090,12 @@
             "source_path_from_root": "/articles/data-factory/connector-troubleshoot-google-adwords.md",
             "redirect_url": "/azure/data-factory/connector-troubleshoot-google-ads",
             "redirect_document_id": false
-          }          
+          },
+          {
+            "source_path_from_root": "/articles/data-factory/memory-optimized-compute.md",
+            "redirect_url": "azure/data-factory/control-flow-execute-data-flow-activity#type-properties",
+            "redirect_document_id": false
+          }           
     ]
 }                    
 

articles/data-factory/TOC.yml

@@ -240,8 +240,6 @@ items:
             href: concepts-data-flow-performance-transformations.md
           - name: Using data flows in pipelines
             href: concepts-data-flow-performance-pipelines.md
-          - name: Memory optimized compute
-            href: memory-optimized-compute.md
       - name: Integration Runtime performance
         href: concepts-integration-runtime-performance.md
       - name: Manage data flow canvas

articles/data-factory/concepts-integration-runtime-performance.md

@@ -19,14 +19,6 @@ For more information how to create an Integration Runtime, see [Integration Runt
 
 The easiest way to get started with data flow integration runtimes is to choose small, medium, or large from the compute size picker. See the mappings to cluster configurations for those sizes below.
 
-## Cluster type
-
-There are two available options for the type of Spark cluster to utilize: general purpose & memory optimized.
-
-**General purpose** clusters are the default selection and will be ideal for most data flow workloads. These tend to be the best balance of performance and cost.
-
-If your data flow has many joins and lookups, you may want to use a **memory optimized** cluster. Memory optimized clusters can store more data in memory and will minimize any out-of-memory errors you may get. Memory optimized have the highest price-point per core, but also tend to result in more successful pipelines. If you experience any out of memory errors when executing data flows, switch to a memory optimized Azure IR configuration. 
-
 ## Cluster size
 
 Data flows distribute the data processing over different cores in a Spark cluster to perform operations in parallel. A Spark cluster with more cores increases the number of cores in the compute environment. More cores increase the processing power of the data flow. Increasing the size of the cluster is often an easy way to reduce the processing time.

articles/data-factory/control-flow-execute-data-flow-activity.md

@@ -70,7 +70,7 @@ Property | Description | Allowed values | Required
 dataflow | The reference to the Data Flow being executed | DataFlowReference | Yes
 integrationRuntime | The compute environment the data flow runs on. If not specified, the autoresolve Azure integration runtime is used. | IntegrationRuntimeReference | No
 compute.coreCount | The number of cores used in the spark cluster. Can only be specified if the autoresolve Azure Integration runtime is used | 8, 16, 32, 48, 80, 144, 272 | No
-compute.computeType | The type of compute used in the spark cluster. Can only be specified if the autoresolve Azure Integration runtime is used | "General", "MemoryOptimized" | No
+compute.computeType | The type of compute used in the spark cluster. Can only be specified if the autoresolve Azure Integration runtime is used | "General" | No
 staging.linkedService | If you're using an Azure Synapse Analytics source or sink, specify the storage account used for PolyBase staging.<br/><br/>If your Azure Storage is configured with VNet service endpoint, you must use managed identity authentication with "allow trusted Microsoft service" enabled on storage account, refer to [Impact of using VNet Service Endpoints with Azure storage](/azure/azure-sql/database/vnet-service-endpoint-rule-overview#impact-of-using-virtual-network-service-endpoints-with-azure-storage). Also learn the needed configurations for [Azure Blob](connector-azure-blob-storage.md#managed-identity) and [Azure Data Lake Storage Gen2](connector-azure-data-lake-storage.md#managed-identity) respectively.<br/> | LinkedServiceReference | Only if the data flow reads or writes to an Azure Synapse Analytics
 staging.folderPath | If you're using an Azure Synapse Analytics source or sink, the folder path in blob storage account used for PolyBase staging | String | Only if the data flow reads or writes to Azure Synapse Analytics
 traceLevel | Set logging level of your data flow activity execution | Fine, Coarse, None | No