1. Real-time Predictive Analytics with Databricks Delta


    To achieve real-time predictive analytics with Databricks Delta, you will need to set up a Databricks workspace using Pulumi and configure a Databricks pipeline capable of handling Delta Live Tables (DLT). The Delta Live Tables is a feature of Databricks that allows you to perform ETL (Extract, Transform, Load) operations on streaming data and then proceed to run predictive analytics using machine learning models that you've created.

    Below is a high-level overview of the steps and resources involved:

    1. Databricks Workspace: This is required to run any Databricks code. It's where all your notebooks, clusters, and other Databricks resources live.

    2. Databricks Cluster: This will be used to execute your Delta Live Tables and predictive analytics jobs.

    3. Delta Live Tables: These are used for creating reliable ETL pipelines using simple declarative constructs. DLT manages the flow of data and maintains the integrity of data products.

    4. Databricks Pipeline: It can be used to deploy and manage Delta Live Tables by setting up continuous integration and delivery for your ETL workloads. The pipeline constructs a directed acyclic graph (DAG) of computation that will efficiently process your data in real-time.

    To create these resources using Pulumi, we'll start by setting up the Databricks provider, then we'll provision the necessary resources. We'll be using a DatabricksPipeline resource to deploy a pipeline that will be configured for real-time data processing and analytics.

    Here's how you'd start writing your Pulumi program in Python to accomplish this:

    import pulumi import pulumi_databricks as databricks # Replace these with appropriate values databricks_workspace_name = "my-databricks-workspace" databricks_resource_group_name = "my-databricks-resource-group" # Provision a Databricks workspace databricks_workspace = databricks.Workspace("workspace", name=databricks_workspace_name, resource_group_name=databricks_resource_group_name, sku="premium" # or choose a different SKU based on your needs ) # Set up a new Databricks cluster databricks_cluster = databricks.Cluster("cluster", workspace_id=databricks_workspace.id, num_workers=3, # this is an example; set num_workers as you need spark_version="7.3.x-scala2.12", # specify the Spark version to match your requirements node_type_id="Standard_D3_v2", # example node type, select the appropriate node type for your workload autotermination_minutes=20 # automatically terminate the cluster after a period of inactivity ) # Define a Databricks pipeline to deploy Delta Live Tables # For a real-world use case, you might want to read configurations from an external file or environment variables. # You would also need to specify 'storage', 'clusters', 'configuration', etc., based on your specific ETL processing logic. pipeline = databricks.Pipeline("pipeline", workspace_id=databricks_workspace.id, name="my-delta-pipeline", configuration={ "channels": "CURRENT", "edition": "advanced", # or standard, based on your need # Additional configurations go here }, clusters=[{ # Configure the cluster as needed }], libraries=[{ # Specify the libraries required for your predictive models }], # Define continuous=True to have a real-time pipeline continuous=True, # Define your ETL and predictive analytics code here # A full definition would include the path to your data, transformation logic, machine learning model references, etc. ) # Export the URL of the Databricks workspace to view it in your web browser pulumi.export('databricks_workspace_url', databricks_workspace.workspace_url)

    In this code:

    • We start by defining a Databricks workspace resource, which is the foundation for running any Databricks services.
    • Next, we set up a Databricks cluster with a specified number of workers and other configuration details like node type and Spark version.
    • Then we define a DatabricksPipeline resource to deploy the Delta Live Tables pipeline, which is outlined with high-level configurations.

    Remember that you'll need to complete the pipeline definition with appropriate settings reflecting your specific ETL processes and predictive analytics needs, including the source data, transformations, and machine learning models.

    Please adjust the clusters, libraries, and other configurations according to the requirements of your data processing workload and the predictive analytics you aim to perform.