Update 7-design-azure-stream-analytics-solution-for-data-analysis.md

Author: staleycyn

learn-pr/wwl-azure/design-data-integration/includes/7-design-azure-stream-analytics-solution-for-data-analysis.md

@@ -5,6 +5,7 @@ The process of consuming data streams, analyzing them, and deriving actionable i
 Azure Stream Analytics works on the following concepts:
 
 - **Data streams**: Data streams are continuous data generated by applications, IoT devices, or sensors. The data streams are analyzed and actionable insights are extracted. Some examples are monitoring data streams from industrial and manufacturing equipment and monitoring water pipeline data by utility providers. Data streams help us understand change over time.
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 - **Event processing**: Event processing refers to consumption and analysis of a continuous data stream to extract actionable insights from the events happening within that stream. An example is how a car passing through a tollbooth should include temporal information like a timestamp that indicates when the event occurred.
 
 > [!Important] 
@@ -16,7 +17,7 @@ The following illustration shows the Stream Analytics pipeline, and how data is
 
 #### Key features
 
-Stream Analytics ingests data from Azure Event Hubs (including Azure Event Hubs from Apache Kafka), Azure IoT Hub, or Azure Blob Storage. The query, which is based on SQL query language, can be used to easily filter, sort, aggregate, and join streaming data over a period. You can also extend this SQL language with JavaScript and C# user-defined functions (UDFs).
+Stream Analytics ingests data from Azure Event Hubs (including Azure Event Hubs from Apache Kafka), Azure IoT Hub, or Azure Blob Storage. The query, which is based on SQL query language, can be used to easily filter, sort, aggregate, and join streaming data over a period. 
 
 An Azure Stream Analytics job consists of an input, query, and an output. You can do the following tasks with the job output:
 
@@ -46,7 +47,13 @@ In the Tailwind Traders scenario, we can apply Azure Stream Analytics to visuali
 Azure Stream Analytics can be a valuable component in your data integration plan for Tailwind Traders. Review the following benefits of the service.
 
 - **Consider provisioning requirements**. Azure Stream Analytics is a fully managed service. It's offered as a PaaS (Platform as a Service) offering, so there's no overhead of provisioning any hardware or infrastructure. Azure Stream Analytics fully manages your job, so you can focus on your business logic and not on the infrastructure.
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 - **Consider costs**. Stream Analytics is low cost. Billing is done by Streaming Units (SUs) consumed that represents the amount of CPU and memory resources allocated. Scaling up and down are based on business needs, which can also lower costs. No maintenance charges are involved.
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 - **Consider implementation**. You can run Azure Stream Analytics in the cloud for large-scale analytics. For ultra-low latency analytics, run Stream Analytics on IoT Edge or Azure Stack.
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 - **Consider performance**. Stream Analytics offers reliable performance guarantees. It supports higher performance by partitioning, which allows complex queries to be parallelized and executed on multiple streaming nodes. Stream Analytics can process millions of events every second. It can deliver results with ultra-low latencies.
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 - **Consider security**. Stream Analytics encrypts all incoming and outgoing communications and supports TLS 1.2. Built-in checkpoints are also encrypted. Stream Analytics doesn't store the incoming data because all processing is done in-memory.
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+- **Consider the no-code editor**. For teams who prefer a visual, drag-and-drop approach that complements the SQL query language for mixed skill levels.