Real-time Analytics Pipelines with Databricks Delta Lake

Question

Pulumi · Accepted Answer

To create a real-time analytics pipeline using Databricks Delta Lake, you need to set up several components: Databricks clusters to process the data in real-time, a Delta Lake to store and manage your data, and potentially a streaming source like Apache Kafka or AWS Kinesis.

For the sake of simplicity and building upon the Pulumi registry results, we'll focus on setting up a Databricks pipeline and a Delta Lake table. The Databricks pipeline allows continuous processing of streaming data, and the Delta Lake table enables ACID transactions and scalable metadata handling for your data lake.

Below is a Pulumi program written in Python that performs the following actions:

1. **Create a Databricks workspace** - Workspaces in Databricks contain all the components like notebooks, clusters, and jobs. The workspace serves as an environment for your data engineering tasks.
   
2. **Set up a Databricks pipeline** - A pipeline orchestrates the processing of streaming data. It defines the clusters and configurations necessary to run computations on inbound data in real-time.

3. **Create a Delta Lake table** - A table in Delta Lake is where the processed data will be stored. It is optimized for fast read and write operations, and it supports schema evolution and time-travel queries.

Remember that before running this program, you would need to have the appropriate cloud credentials and Pulumi configuration set up to interact with Databricks and your cloud storage.

```python
# Ensure you have the necessary imports for Databricks and cloud-specific providers.
import pulumi
import pulumi_databricks as databricks
import pulumi_aws as aws  # Assuming AWS as the cloud provider for storage

# Configure the AWS region and the Databricks workspace location.
aws_region = aws.get_region()
databricks_workspace_location = aws_region.name

# Create an AWS S3 bucket to act as the storage for Delta Lake.
delta_lake_storage_bucket = aws.s3.Bucket("delta-lake-storage")

# Create a Databricks workspace.
databricks_workspace = databricks.Workspace(
    "databricks-workspace",
    location=databricks_workspace_location,
    sku="premium"  # Choose the SKU based on your requirement; options could be "standard", "premium", etc.
)

# Define the Databricks cluster specification for the real-time pipeline.
# This specification is an example and should be adjusted to your actual workload requirements.
pipeline_cluster_spec = databricks.ClusterSpecArgs(
    instance_pool_id=databricks_workspace.instance_pool_id,
    spark_version="7.3.x-scala2.12",
    node_type_id="i3.xlarge",  # Choose a suitable node type.
    autoscale=databricks.AutoScaleArgs(
        min_workers=1,
        max_workers=5
    ),
)

# Create a Databricks pipeline to process real-time analytics.
databricks_pipeline = databricks.Pipeline(
    "analytics-pipeline",
    workspace_id=databricks_workspace.id,
    clusters=[pipeline_cluster_spec],
    configuration={
        "spark": {
            "sql.streaming.schemaInference": "true"
        }
    }
)

# Create Delta Lake table within the Databricks environment.
delta_lake_table = databricks.Table(
    "delta-lake-table",
    name="events",
    database="default",  # Specify the database where to create the table, or create a new one if needed.
    location=delta_lake_storage_bucket.bucket.apply(lambda bucket: f"s3://{bucket}"),  # Use the created S3 bucket for storage.
    schema="""id INT, event STRING, timestamp TIMESTAMP""",  # Define the table schema. Adjust with your data schema.
    format="delta",
    # Ensure the pipeline clusters have read-write access to the S3 bucket.
    clusters=[pipeline_cluster_spec],
)

# Export the URL of Delta Lake storage bucket and pipeline ID for later reference.
pulumi.export("delta_lake_storage_url", delta_lake_storage_bucket.bucket_regional_domain_name)
pulumi.export("databricks_pipeline_id", databricks_pipeline.id)
```

This Pulumi program outlines the necessary components and steps to set up a real-time analytics pipeline using Databricks Delta Lake. The cluster settings, Delta Lake tables schema, and other configurations should be tailored to fit the specifics of the data being processed and analyzed.

For more details on creating Delta Lake tables, refer to the [Databricks Table documentation](https://www.pulumi.com/registry/packages/databricks/api-docs/table/). For setting up pipelines, consult the [Databricks Pipeline documentation](https://www.pulumi.com/registry/packages/databricks/api-docs/pipeline/).

Before running this code, ensure that your Pulumi CLI is authenticated with the cloud provider (like AWS) where your Delta Lake and Databricks workspace will be hosted. Additionally, ensure that your Pulumi configuration includes all the necessary information for Databricks authentication.