1. Data Lake for BigQuery ML on GCP Storage


    To create a Data Lake for BigQuery ML on GCP using Pulumi, we will need to set up Google Cloud Storage to store our large datasets, and then integrate with BigQuery ML for analytics and machine learning.

    Here's what we'll do:

    1. Create a Google Cloud Storage Bucket: This bucket will serve as our data lake, storing the raw data that we will process with BigQuery ML.

    2. Set up Google Cloud BigQuery Dataset: We will need a BigQuery dataset where we can create our machine learning models.

    3. Integrate Storage with BigQuery: Finally, we connect our GCP Storage with BigQuery to allow for analyzing the stored data using BigQuery's ML capabilities.

    Let's dive into the Pulumi program which accomplishes this:

    import pulumi import pulumi_gcp as gcp # 1. Create a Google Cloud Storage Bucket to store the data data_lake_bucket = gcp.storage.Bucket("data_lake_bucket", location="US", storage_class="STANDARD", uniform_bucket_level_access=True, lifecycle_rules=[ gcp.storage.BucketLifecycleRuleArgs( action=gcp.storage.BucketLifecycleRuleActionArgs( type="Delete", storage_class="NEARLINE", ), condition=gcp.storage.BucketLifecycleRuleConditionArgs( age=365, matches_storage_classes=["NEARLINE"] ) ) ] ) # 2. Set up a BigQuery Dataset for analytics and ML bigquery_dataset = gcp.bigquery.Dataset("bigquery_dataset", location="US", description="Dataset for BigQuery ML", ) # 3. Create an External Data Source pointing to our GCS bucket. This allows BigQuery direct access to the files stored in GCS. external_data_source = gcp.bigquery.Table("external_data_source", dataset_id=bigquery_dataset.dataset_id, external_data_configuration=gcp.bigquery.TableExternalDataConfigurationArgs( source_format="PARQUET", source_uris=[pulumi.Output.concat("gs://", data_lake_bucket.name, "/*")], # Point to all files in the data lake bucket ) ) # Export the bucket name and the BigQuery dataset id pulumi.export("data_lake_bucket_name", data_lake_bucket.name) pulumi.export("bigquery_dataset_id", bigquery_dataset.dataset_id)

    In the program above, here's what we're doing in each step:

    1. Creating the Storage Bucket (data_lake_bucket): We instantiate a GCP storage bucket in the US location using the STANDARD storage class. Uniform bucket-level access is set to True for consistent access controls across all objects in the bucket. We also define a lifecycle rule to transition objects to NEARLINE storage after one year, or delete them if they are already in NEARLINE storage class.

    2. Setting Up BigQuery Dataset (bigquery_dataset): We create a BigQuery dataset in the same location as our storage bucket. This dataset is where BigQuery ML will process data.

    3. External Data Source (external_data_source): A BigQuery table is created, but instead of holding data itself, it points to the external data located in our GCS bucket. This allows BigQuery to query the data directly from the storage bucket using the table abstraction.

    At the end, we export the bucket name and the BigQuery dataset ID so that you can reference them later, e.g., when you want to load data into the bucket or run queries in BigQuery ML.

    Google Cloud Storage Bucket Google Cloud BigQuery Dataset Google Cloud BigQuery Table with External Data Source