1. Genomics Data Analysis using Databricks Clusters


    To perform genomics data analysis using Databricks clusters, you would want to set up an environment on a cloud platform where Databricks is supported, such as AWS, Azure or GCP. Databricks offers a unified data analytics platform for massive scale data engineering and collaborative data science.

    For this example, we'll use the databricks.Cluster resource to create a new Databricks cluster that is configured for data analysis. We'll also need to attach a library of genomics tools to the cluster, which can be done using the databricks.Library resource.

    Here's how you would do it in Pulumi using Python:

    1. Create a Databricks Cluster: A cluster in Databricks is a set of computation resources and configurations on which you run your data analysis jobs. The databricks.Cluster resource allows you to create and manage a Databricks cluster.

    2. Attach Libraries to the Cluster: Libraries are packages or modules that provide additional functionality needed for your analysis, such as bioinformatics tools for genomics data. The databricks.Library resource can install various types of libraries onto a Databricks cluster.

    3. Configure Cluster with Genomics Tools: To perform genomics data analysis, your cluster might require specific tools which can be installed by specifying them in the libraries associated with the databricks.Cluster resource.

    Below, we will create a Pulumi program that sets up a Databricks cluster with a genomics library attached to it:

    import pulumi import pulumi_databricks as databricks # Initialize a Databricks cluster configuration with necessary nodes and spark version. cluster = databricks.Cluster("genomics-cluster", # Specify the node type used by the driver and the workers. node_type_id="r3.xlarge", driver_node_type_id="r3.xlarge", # Define the number of workers. num_workers=2, # Specify the Databricks runtime version for the cluster. spark_version="7.3.x-scala2.12", # Autoscaling options can be specified if desired. autoscale=databricks.ClusterAutoscaleArgs( min_workers=2, max_workers=10, ), # Provide the cluster name for easier identification. cluster_name="genomics-analysis-cluster", # Enable auto termination to minimize costs for the analysis. autotermination_minutes=20, ) # Attach a library containing genomics tools to the cluster. library = databricks.Library("genomics-library", cluster_id=cluster.id, pypi=databricks.LibraryPypiArgs( # Specify the package to install from PyPI (you would replace "genomics-package" with the actual package name) package="genomics-package" ) ) # Export the cluster URL which you can use to access your Databricks workspace. pulumi.export("cluster_url", cluster.url)

    In this program:

    • We created a Databricks cluster named genomics-cluster with the databricks.Cluster resource. The cluster has 2 worker nodes (num_workers=2) and uses the r3.xlarge instance type for both driver and worker nodes. It runs a Databricks runtime version 7.3 with support for Scala 2.12.

    • Autoscaling is configured with a minimum of 2 workers and a maximum of 10 workers, allowing the cluster to adjust based on the workload.

    • The cluster is configured to automatically terminate after 20 minutes of inactivity to save on costs.

    • A Python library for genomics (represented here as genomics-package) is attached to the cluster using the databricks.Library resource. You would replace this with the name of a real genomics-related Python package.

    • Finally, we export the URL of the cluster, which can be used to access the Databricks workspace and interact with the cluster.

    The actual genomics packages you use would depend on your specific data analysis needs. You would need to replace "genomics-package" with the name of the actual Python package or library you want to use for analysis.

    To use this code:

    1. Make sure you have Pulumi installed and set up for the cloud provider of your choice (AWS, Azure, GCP).
    2. Ensure that you've configured Pulumi with access to your Databricks workspace (using credentials such as Databricks access tokens).
    3. Replace "genomics-package" with the actual genomics library you want to install.
    4. Run pulumi up to deploy the cluster and the genomics library onto your cloud environment.