1. Streaming Data Ingestion for Real-Time Analytics in Databricks.


    Streaming data ingestion for real-time analytics is a common use case for businesses that need to process and analyze data as it arrives in near real-time. In the context of Databricks, this typically involves setting up structured streaming jobs that can read from various sources, transform the data as needed, and then load the data into a destination where it can be analyzed, often in real time.

    Databricks is a powerful platform that offers a unified analytics engine for data processing and machine learning. To achieve streaming data ingestion in Databricks, you would typically use a combination of Databricks Jobs for orchestrating your data pipelines, Databricks Clusters for running your data processing tasks, and Databricks Tables for storage and query purposes.

    Here is a Pulumi program written in Python that sets up the necessary infrastructure for streaming data ingestion in Databricks on AWS. In this program, we use the databricks.Job resource to define a job that will execute a streaming ingest task, a databricks.Cluster for the compute resources to run our job, and databricks.Table for storage. Additionally, we may set up other integrations such as Kafka or AWS Kinesis (not shown here) for the actual data streams.

    import pulumi import pulumi_databricks as databricks # Configure the Databricks cluster to process streaming data. # The cluster configuration here should match the requirements of your streaming data application, # including the correct node type, number of worker nodes, and specific Databricks runtime version. cluster = databricks.Cluster("streaming-cluster", autoscale=databricks.ClusterAutoscaleArgs( min_workers=1, max_workers=3, ), node_type_id="r3.xlarge", spark_version="7.3.x-scala2.12", ) # Define a Databricks job that performs streaming data ingestion. # This configuration assumes you have defined a notebook or JAR with your structured streaming logic. # You must configure the path to your notebook or the parameters to your JAR appropriately. job = databricks.Job("streaming-job", new_cluster=databricks.JobClusterArgs( spark_version=cluster.spark_version, node_type_id=cluster.node_type_id, nums_worker=2, # Example, adjust to the number of worker nodes necessary for the job ), notebook_task=databricks.JobNotebookTaskArgs( notebook_path="/Workspace/path/to/your/notebook", ), ) # Create a table to hold ingested data for analysis. # The schema should match the format of the data you are ingesting. # Note this example uses a simple schema for demonstration purposes. table = databricks.Table("ingested-data-table", # The name of the database where the table should be created. database="analytics", # Define the schema of the table. # Schema attributes should match the format of ingested data. schema=[ databricks.TableSchemaArgs( column_name="data", data_type="STRING", ), ], # Set the table properties based on your use case. table_properties={ "type": "delta", # Using Delta Lake for versioning and other features }, ) # Output the job and table information. pulumi.export("cluster_id", cluster.id) pulumi.export("job_id", job.id) pulumi.export("table_id", table.id)

    The code above sets up an infrastructure that would typically be required for streaming data ingestion in Databricks:

    • A cluster is set up with autoscaling enabled to efficiently manage compute resources based on the load.
    • A job is defined to handle the execution of a streaming data ingestion task. In the example, it is assumed you have a notebook with the relevant logic, and we set the path to this notebook. If you’re using a JAR, you would set the spark_jar_task configuration instead.
    • A table is created to store the ingested data. The schema of this table must match the format of the data you are streaming.

    Remember, this code is an illustration of how you would use Pulumi with Databricks resources to set up streaming data ingestion. The real logic for data ingestion would be within the Databricks notebook or JAR file that you provide to the job.

    To use this Pulumi program:

    1. Adjust the configurations like node_type_id and spark_version to match the requirements of your specific streaming data application.
    2. Set the notebook_path to the path of your Databricks notebook that contains your streaming logic. If using a JAR, configure the spark_jar_task parameter with the details of your JAR.
    3. If you need to create a database, use the databricks.Database resource before creating tables.
    4. Tailor the schema in databricks.Table to match your ingested data.
    5. You need an appropriate streaming source, like Kafka or AWS Kinesis streams, which is not covered in this program.
    6. Ensure your Pulumi and cloud provider credentials are set up properly in your environment before running pulumi up to provision the resources.

    After making these adjustments and running the program with Pulumi CLI, you will have the infrastructure ready for streaming data ingestion within a Databricks environment on AWS.