Merge pull request #227588 from xujxu/nocode-pbi-output-update

Nocode pbi output update

Author: ttorble

articles/event-hubs/TOC.yml

@@ -72,7 +72,7 @@
     - name: Capture Event Hubs data in Parquet format
       href: ../stream-analytics/event-hubs-parquet-capture-tutorial.md?toc=/azure/event-hubs/TOC.json
       maintainContext: true
-    - name: Build real time dashboards with Power BI
+    - name: Build real-time dashboards with Power BI
       href: ../stream-analytics/no-code-power-bi-tutorial.md
       maintainContext: true
     - name: Process Apache Kafka for Event Hubs events using Stream analytics
@@ -210,6 +210,8 @@
 
     - name: Process data
       items:
+        - name: Build real-time dashboard with Power BI dataset
+          href: ../stream-analytics/no-code-build-power-bi-dashboard.md?toc=/azure/event-hubs/toc.json
         - name: Capture Event Hubs data in Delta Lake format
           href: ../stream-analytics/capture-event-hub-data-delta-lake.md?toc=/azure/event-hubs/toc.json
         - name: Capture Event Hubs data in Parquet format

articles/stream-analytics/TOC.yml

@@ -37,7 +37,7 @@
     href: event-hubs-parquet-capture-tutorial.md
   - name: Write output data to delta table in ADLS Gen2
     href: write-to-delta-table-adls-gen2.md
-  - name: Build near-real time dashboards with no code editor
+  - name: Build real-time dashboards with no code editor
     href: no-code-power-bi-tutorial.md
   - name: Visualize fraudulent calls in Power BI
     href: stream-analytics-real-time-fraud-detection.md
@@ -235,6 +235,8 @@
       href: move-cluster.md  
   - name: Build with no code editor
     items:
+    - name: Build real-time dashboard with Power BI dataset
+      href: no-code-build-power-bi-dashboard.md
     - name: Capture Event Hubs data in Delta Lake format
       href: capture-event-hub-data-delta-lake.md
     - name: Capture Event Hubs data in Parquet format

articles/stream-analytics/capture-event-hub-data-delta-lake.md

@@ -6,7 +6,7 @@ ms.author: xujiang1
 ms.service: stream-analytics
 ms.topic: how-to
 ms.custom: mvc, event-tier1-build-2022
-ms.date: 12/18/2022
+ms.date: 2/17/2023
 ---
 # Capture data from Event Hubs in Delta Lake format (preview)
 
@@ -40,7 +40,7 @@ Use the following steps to configure a Stream Analytics job to capture data in A
 1. On the **Azure Data Lake Storage Gen2** configuration page, follow these steps:     
     1. Select the subscription, storage account name and container from the drop-down menu. 
     1. Once the subscription is selected, the authentication method and storage account key should be automatically filled in.  
-    1. For **Delta table path**, it's used to specify the location and name of your Delta Lake table stored in Azure Data Lake Storage Gen2. You can choose to use one or more path segments to define the path to the delta table and the delta table name. To learn more, see to [Write to Delta Lake table](./write-to-delta-lake.md).  
+    1. For **Delta table path**, it's used to specify the location and name of your Delta Lake table stored in Azure Data Lake Storage Gen2. You can choose to use one or more path segments to define the path to the delta table and the delta table name. To learn more, see to [Write to Delta Lake table (Public Preview)](./write-to-delta-lake.md).  
     1. Select **Connect**.
 
         :::image type="content" source="./media/capture-event-hub-data-delta-lake/blob-configuration.png" alt-text="First screenshot showing the Blob window where you edit a blob's connection configuration." lightbox="./media/capture-event-hub-data-delta-lake/blob-configuration.png" :::  

articles/stream-analytics/job-diagram-with-metrics.md

@@ -7,7 +7,7 @@ ms.author: xujiang1
 ms.service: stream-analytics
 ms.custom: ignite-2022
 ms.topic: conceptual
-ms.date: 12/8/2022
+ms.date: 2/17/2023
 ---
 
 # Stream Analytics job diagram (preview) in Azure portal
@@ -86,9 +86,9 @@ The processor diagram in physical job diagram visualizes the processor topology
         | --- | --- | 
         | **Input** or **Output** | This processor is used for reading input or writing output data streams. |
         | **ReferenceData** | This processor is used for fetching the reference data. |
-        | **Computing** | This processor is used for processing the stream data according to the query logic, for example, aggregating, filtering, grouping with window, etc.. To learn more about the stream data computation query functions, see [Azure Stream Analytics Query Language Reference](/stream-analytics-query/stream-analytics-query-language-reference). |
+        | **Computing** | This processor is used for processing the stream data according to the query logic, for example, aggregating, filtering, grouping with window, etc. To learn more about the stream data computation query functions, see [Azure Stream Analytics Query Language Reference](/stream-analytics-query/stream-analytics-query-language-reference). |
         | **MarshallerUpstream** and **MarshallerDownstream** | When there's stream data interaction among streaming nodes, there will be two marshaller processors: 1). **MarshallerUpstream** for sending the data in the upstream streaming node and 2). **MarshallerDownstream** for receiving the data in the downstream streaming node. |
-        | **Merger** | This processor is to receive the crossing-partition stream data, which were outputted from several upstream streaming nodes. The best practice to optimize job performance is to update query to remove the merger processor to make the job become parallel since the merger processor is the bottleneck of the job. The job diagram simulator feature within VSCode ASA extension can help you simulating your query locally when you optimizing your job query. To learn more, see [Optimize query using job diagram simulator (preview)](./optimize-query-using-job-diagram-simulator.md). |
+        | **Merger** | This processor is to receive the crossing-partition stream data, which were outputted from several upstream streaming nodes. The best practice to optimize job performance is to update query to remove the merger processor to make the job become parallel since the merger processor is the bottleneck of the job. The job diagram simulator feature within Visual Studio Code ASA extension can help you simulating your query locally when you optimizing your job query. To learn more, see [Optimize query using job diagram simulator (preview)](./optimize-query-using-job-diagram-simulator.md). |
         |
 
 
@@ -97,7 +97,7 @@ The processor diagram in physical job diagram visualizes the processor topology
 
     * **Adapter type**: it shows the type of the input or output adapter. Stream Analytics supports various input sources and output destinations. Each input source or output destination has a dedicated adapter type. It's only available in input processor and output processor. For example, "InputBlob" represents the ADLS Gen2 input where the input processor receives the data from; "OutputDocumentDb" represents the Cosmos DB output where the output processor outputs the data to. 
 
-        To learn more details of the input and output types, see [Azure Stream Analytics inputs overview](./stream-analytics-define-inputs.md), and [Azure Stream Analytics outputs overview](./stream-analytics-define-outputs.md)
+        To learn more details of the input and output types, see [Azure Stream Analytics inputs overview](./stream-analytics-define-inputs.md), and [Azure Stream Analytics outputs overview](./stream-analytics-define-outputs.md).
 
     * **Partition IDs**: it shows which partition IDs' data are being processed by this processor. It's only available in input processor and output processor.
     * **Serializer type**: it shows the type of the serialization. Stream Analytics supports several [serialization types](./stream-analytics-define-inputs.md). It's only available in input processor and output processor.