AWS emr documentation change
Summary
Minor grammatical and phrasing improvements in streaming job documentation (e.g., 'you can use' → 'use', 'see' → 'refer to'). No technical changes to functionality.
Security assessment
Changes are purely editorial (grammar, phrasing consistency, and link formatting). No mention of vulnerabilities, security controls, or new security features. References to existing reliability features like thrash prevention thresholds and checkpointing remain unchanged in substance.
Diff
diff --git a/emr/latest/EMR-Serverless-UserGuide/jobs-streaming.md b/emr/latest/EMR-Serverless-UserGuide/jobs-streaming.md index f2e32a800..a1ab5b05b 100644 --- a//emr/latest/EMR-Serverless-UserGuide/jobs-streaming.md +++ b//emr/latest/EMR-Serverless-UserGuide/jobs-streaming.md @@ -15 +15 @@ The following are some use cases with streaming jobs: - * **Fraud detection** – you can use streaming jobs to run near real-time fraud detection in financial transactions, credit card operations, or online activities when you analyze data streams and identify suspicious patterns or anomalies as they occur. + * **Fraud detection** – use streaming jobs to run near real-time fraud detection in financial transactions, credit card operations, or online activities when you analyze data streams and identify suspicious patterns or anomalies as they occur. @@ -21 +21 @@ The following are some use cases with streaming jobs: - * **Internet of Things (IoT) analytics** – streaming jobs can handle and analyze high-velocity streams of data from IoT devices, sensors, and connected machinery, so you can run anomaly detection, predictive maintenance, and other IoT analytics use cases. + * **Internet of Things (IoT) analytics** – streaming jobs can handle and analyze high-velocity streams of data from IoT devices, sensors, and connected machinery, so run anomaly detection, predictive maintenance, and other IoT analytics use cases. @@ -66 +66 @@ When you use streaming jobs, keep in mind the following considerations and limit - * Streaming jobs are only compatible with the Spark engine, which is built on-top of the [structured streaming framework](https://spark.apache.org/streaming/). + * Streaming jobs are only compatible with the Spark engine, which is built on the [structured streaming framework](https://spark.apache.org/streaming/). @@ -68 +68 @@ When you use streaming jobs, keep in mind the following considerations and limit - * EMR Serverless indefinitely retries streaming jobs, and you can't customize the number of maximum attempts. Thrash prevention is automatically included to stop the job retry if the amount of failed attempts has surpassed a threshold set over an hourly window. The default threshold is five failed attempts over one hour. You can configure this threshold to be between 1 and 10 attempts. For more information, see [Job resiliency](https://docs.aws.amazon.com/emr/latest/EMR-Serverless-UserGuide/SECTION-jobs-resiliency.xml.html). + * EMR Serverless indefinitely retries streaming jobs, and you can't customize the number of maximum attempts. Thrash prevention is automatically included to stop the job retry if the amount of failed attempts has surpassed a threshold set over an hourly window. The default threshold is five failed attempts over one hour. You can configure this threshold to be between 1 and 10 attempts. For more information, refer to [Job resiliency](https://docs.aws.amazon.com/emr/latest/EMR-Serverless-UserGuide/SECTION-jobs-resiliency.xml.html). @@ -70 +70 @@ When you use streaming jobs, keep in mind the following considerations and limit - * Streaming jobs have checkpoints to save runtime state and progress, so EMR Serverless can resume the streaming job from the latest checkpoint. For more information, see [ Recovering from failures with Checkpointing](https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#recovering-from-failures-with-checkpointing) in the Apache Spark documentation. + * Streaming jobs have checkpoints to save runtime state and progress, so EMR Serverless can resume the streaming job from the latest checkpoint. For more information, refer to [ Recovering from failures with Checkpointing](https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#recovering-from-failures-with-checkpointing) in the Apache Spark documentation.