Merge pull request #289009 from Daidihuang/2024_10-Monthly-broken-links-fix-sreekzz

2024_10 - Fix monthly broken links - sreekzz

Author: prmerger-automator[bot]

articles/hdinsight/hadoop/apache-hadoop-deep-dive-advanced-analytics.md

@@ -62,7 +62,7 @@ There are three scalable machine learning libraries that bring algorithmic model
 
 ### Azure Machine Learning and Apache Hive
 
-[Azure Machine Learning Studio (classic)](https://studio.azureml.net/) provides tools to model predictive analytics, and a fully managed service you can use to deploy your predictive models as ready-to-consume web services. Azure Machine Learning provides tools for creating complete predictive analytics solutions in the cloud to quickly create, test, operationalize, and manage predictive models. Select from a large algorithm library, use a web-based studio for building models, and easily deploy your model as a web service.
+[Azure Machine Learning Studio (classic)](https://ml.azure.com/) provides tools to model predictive analytics, and a fully managed service you can use to deploy your predictive models as ready-to-consume web services. Azure Machine Learning provides tools for creating complete predictive analytics solutions in the cloud to quickly create, test, operationalize, and manage predictive models. Select from a large algorithm library, use a web-based studio for building models, and easily deploy your model as a web service.
 
 ### Apache Spark and Deep learning
 

articles/hdinsight/spark/apache-spark-streaming-overview.md

@@ -90,7 +90,7 @@ ssc.start()
 ssc.awaitTermination()
 ```
 
-For details on the Spark Stream API, see [Apache Spark Streaming Programming Guide](https://people.apache.org/~pwendell/spark-releases/latest/streaming-programming-guide.html).
+For details on the Spark Stream API, see [Apache Spark Streaming Programming Guide](https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html).
 
 The following sample application is self-contained, so you can run it inside a [Jupyter Notebook](apache-spark-jupyter-notebook-kernels.md). This example creates a mock data source in the class DummySource that outputs the value of a counter and the current time in milliseconds every five seconds. A  new StreamingContext object  has a batch interval of 30 seconds. Every time a batch is created, the streaming application examines the RDD produced. Then converts the RDD to a Spark DataFrame, and creates a temporary table over the DataFrame.
 
@@ -229,7 +229,7 @@ After the first minute, there are 12 entries - six entries from each of the two
 | 11 | 1497316344339
 | 12 | 1497316349361
 
-The sliding window functions available in the Spark Streaming API include window, countByWindow, reduceByWindow, and countByValueAndWindow. For details on these functions, see [Transformations on DStreams](https://people.apache.org/~pwendell/spark-releases/latest/streaming-programming-guide.html#transformations-on-dstreams).
+The sliding window functions available in the Spark Streaming API include window, countByWindow, reduceByWindow, and countByValueAndWindow. For details on these functions, see [Transformations on DStreams](https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html).
 
 ## Checkpointing
 
@@ -246,5 +246,5 @@ The status of all applications can also be checked with a GET request against a
 ## Next steps
 
 * [Create an Apache Spark cluster in HDInsight](../hdinsight-hadoop-create-linux-clusters-portal.md)
-* [Apache Spark Streaming Programming Guide](https://people.apache.org/~pwendell/spark-releases/latest/streaming-programming-guide.html)
+* [Apache Spark Streaming Programming Guide](https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html)
 * [Overview of Apache Spark Structured Streaming](apache-spark-structured-streaming-overview.md)