1. Incremental Learning with Delta Lake on Databricks


    Delta Lake is an open-source storage framework that enables building a Lakehouse architecture on top of data lakes. Delta Lake provides ACID transactions, scalable metadata handling, and unifies streaming and batch data processing on top of existing data lakes.

    Pulumi allows you to manage cloud infrastructure using code, and it supports deploying resources on Databricks, which can run Delta Lake for data engineering, data science, and machine learning workloads.

    To perform incremental learning with Delta Lake on Databricks using Pulumi, you would need to:

    1. Set up a Databricks workspace.
    2. Create a Databricks cluster where Delta Lake will run.
    3. Create a Databricks job to handle the incremental learning tasks.

    Below is an example of how to use Pulumi to create these resources in Python. We will use the pulumi_databricks and pulumi_azure_native packages to manage Databricks resources on Azure cloud. If you’re using a different cloud provider, the code will differ slightly: you would need to use the corresponding Pulumi provider package for AWS or GCP.

    import pulumi import pulumi_azure_native as azure_native import pulumi_databricks as databricks # Set up the Databricks workspace workspace = azure_native.databricks.Workspace("myWorkspace", resource_group_name="myResourceGroup", sku=azure_native.databricks.SkuArgs( name="standard" ), location="West US" ) # Create a Databricks cluster cluster = databricks.Cluster("myCluster", autoscale=databricks.ClusterAutoscaleArgs( min_workers=1, max_workers=2 ), node_type_id="Standard_D3_v2", spark_version="7.3.x-scala2.12", spark_conf={ "spark.databricks.delta.preview.enabled": "true" } ) # Define a Delta Lake incremental learning job job = databricks.Job("deltaJob", new_cluster=databricks.JobClusterArgs( spark_version=cluster.spark_version, node_type_id=cluster.node_type_id, autoscale=databricks.ClusterAutoscaleArgs( min_workers=1, max_workers=2 ) ), notebook_task=databricks.JobNotebookTaskArgs( notebook_path="/Workspace/path/to/my/notebook" ), ) pulumi.export('workspaceUrl', workspace.workspace_url) pulumi.export('clusterId', cluster.cluster_id) pulumi.export('jobId', job.job_id)

    In the script above:

    • We define a Databricks workspace that's the fundamental container for other Databricks assets, like clusters and notebooks.
    • We create a cluster using pulumi_databricks.Cluster, which is a set of computation resources and configurations on which you can run Delta Lake jobs.
    • In pulumi_databricks.Job, we define a job that refers to a Databricks notebook where the code for incremental learning using Delta Lake will be written. This could contain code that updates a Delta Lake table incrementally as new data arrives.

    Make sure to replace "myResourceGroup" and "/Workspace/path/to/my/notebook" with your own resource group and notebook path in Databricks workspace.

    To run this Pulumi program:

    1. Install Pulumi and the Databricks CLI.
    2. Configure Pulumi to use your cloud provider (in this case, Azure).
    3. Write the above code into a file (e.g., main.py).
    4. Run pulumi up to create the resources.

    Please note, the actual code for processing incremental learning would be in the Databricks notebook, which you would need to develop according to your specific machine learning model and data ingestion process. The Pulumi script only sets up the necessary infrastructure on Databricks where this code will run.