1. Centralized Data Warehousing for AI Analytics with Databricks

    Python

    To set up centralized data warehousing for AI analytics using Databricks, one of the primary components you need is a Databricks workspace. This workspace is where data processing and analytics jobs will run. You also need to set up Databricks clusters within the workspace that can execute various data tasks. A cluster is a set of computation resources where Databricks jobs are executed.

    Here’s how you would set up these main components in Pulumi using Python:

    1. Databricks Workspace: This is the environment where your data engineering teams can collaborate and run analytics workloads.
    2. Databricks Cluster: Within the workspace, you'll create clusters which are computing environments made of multiple nodes to distribute your computational load.
    3. Databricks Jobs: You can also define jobs that run on the clusters for processing and analyzing your data.
    4. Databricks Tables: These are tables within the Databricks ecosystem where you can store and query structured data, similar to traditional database tables.

    Below is a Pulumi program written in Python to specify this infrastructure as code. The program assumes that you have already set up and configured the necessary cloud provider (e.g., AWS, Azure, GCP) and have installed the pulumi-databricks provider.

    Keep in mind that you will need to replace placeholders like <YOUR-SPARK-VERSION>, <YOUR-NODE-TYPE-ID>, <YOUR-AWS-REGION>, etc., with actual values that suit your requirements.

    import pulumi import pulumi_databricks as databricks # Create a Databricks workspace workspace = databricks.Workspace("ai-analytics-workspace", location="<YOUR-AWS-REGION>", # or Azure/GCP region sku="standard" # Determines the Databricks tier. Options: 'standard', 'premium', etc. ) # Create a Databricks cluster cluster = databricks.Cluster("ai-analytics-cluster", cluster_name="data-warehouse-cluster", spark_version="<YOUR-SPARK-VERSION>", # The runtime version of the cluster. node_type_id="<YOUR-NODE-TYPE-ID>", # The type of node to use for creating the cluster. autotermination_minutes=20, # Automatically terminate the cluster after it's inactive for this time. num_workers=2 # Number of worker nodes in this cluster. ) # Example to define a Databricks Job (here, for demonstration, a simple Spark Python task) job = databricks.Job("analysis-job", new_cluster=databricks.JobNewClusterArgs( spark_version="<YOUR-SPARK-VERSION>", node_type_id="<YOUR-NODE-TYPE-ID>", num_workers=2 ), spark_python_task=databricks.JobSparkPythonTaskArgs( python_file="dbfs:/mnt/my-notebooks/main.py" ) ) # Export the workspace URL, which can be used to access the Databricks console pulumi.export("workspaceUrl", workspace.workspace_url) # Export the job URL, which can be used to access the job details in the Databricks console pulumi.export("jobUrl", pulumi.Output.concat("https://", workspace.workspace_url, "/?o=", workspace.workspace_id.apply(lambda id: id), "#job/", job.job_id))

    Here's what the code is doing:

    • It creates a Databricks workspace which is the central place where you perform data analysis. The sku property determines the tier and features accessible in the workspace.
    • It then defines a Databricks cluster with a specified Spark version and node type. The autotermination_minutes helps to save costs by turning off the cluster when it's idle.
    • A Spark Python task is set up as a Databricks job that points to a Python file in DBFS (Databricks File System). This is where your data processing logic would be coded.
    • Finally, the program exports URLs for the workspace and job that you can use to navigate to your Databricks console and directly access the job details.

    To apply this Pulumi program:

    • Write this code into a file, for example main.py.
    • Ensure Pulumi CLI is installed and configured for the appropriate cloud provider.
    • Install the necessary Pulumi packages by running pulumi up.

    This will deploy the above-defined resources in your cloud provider's environment. Make sure to monitor the Pulumi CLI's output for the URLs to access your Databricks workspace and the job details.


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