Spark code execution on Azure Machine Learning workspace allows us to leverage the power of Apache Spark for big data processing and analytics tasks. Here are some key points to know about Spark code execution on Azure Machine Learning workspace:

1.      Integration: Azure Machine Learning workspace provides seamless integration with Apache Spark, allowing us to run Spark code within the workspace. This integration simplifies the process of running Spark jobs, as us don't need to set up and manage a separate Spark cluster.

2.      Scalability: Azure Machine Learning workspace enables us to scale our Spark jobs easily. We can choose the appropriate cluster size based on our workload requirements, and Azure will automatically provision and manage the necessary resources. This scalability ensures that us can handle large-scale data processing tasks efficiently.

3.      Notebook support: Azure Machine Learning workspace supports Jupyter notebooks, which are commonly used for interactive data exploration and analysis with Spark. We can write and execute Spark code in a Jupyter notebook within the workspace, making it convenient to prototype and experiment with our Spark code.

4.      Parallelism and distributed computing: Spark code execution on Azure Machine Learning workspace takes advantage of the parallel processing capabilities of Spark. It allows us to distribute our data across multiple nodes in a cluster and perform computations in parallel, thereby accelerating the processing of large datasets.

5.      Data integration: Azure Machine Learning workspace provides easy integration with various data sources, including Azure Data Lake Storage, Azure Blob Storage, and Azure SQL Database. We can seamlessly read data from these sources into Spark, perform transformations and analytics, and write the results back to the desired output location.

6.      Monitoring and management: Azure Machine Learning workspace offers monitoring and management capabilities for Spark code execution. We can track the progress of our Spark jobs, monitor resource usage, and diagnose any issues that may arise. Additionally, us can schedule and automate the execution of Spark jobs using Azure Machine Learning pipelines.

7.      Collaboration and version control: Azure Machine Learning workspace enables collaboration and version control for Spark code. We can work with our team members on Spark projects, track changes made to the code, and manage different versions of our Spark scripts. This facilitates teamwork and ensures that us can easily revert to previous versions if needed.

Overall, Spark code execution on Azure Machine Learning workspace provides a powerful and flexible platform for running large-scale data processing and analytics workloads using Apache Spark. It simplifies the management of Spark clusters, provides integration with other Azure services, and offers monitoring and collaboration capabilities to streamline our Spark-based projects.

Sample Spark Session with downloaded jars can be invoked using:

spark = (

    SparkSession.builder \

    .appName('SnowflakeSample') \

    .config("spark.jars","/anaconda/envs/azureml_py38/lib/python3.8/site-packages/pyspark/jars/snowflake-jdbc-3.13.29.jar,/anaconda/envs/azureml_py38/lib/python3.8/site-packages/pyspark/jars/spark-snowflake_2.13-2.13.0-spark_3.3.jar,/anaconda/envs/azureml_py38/lib/python3.8/site-packages/pyspark/jars/snowflake-common-3.1.19.jar,/anaconda/envs/azureml_py38/lib/python3.8/site-packages/pyspark/jars/scala-library-2.13.9.jar")

    .config(conf = conf) \

    .getOrCreate()