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GCP Services Cloud

9 Blueprints

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Download a file from Google Cloud Storage, clean it with Python and pandas, and load the curated result into BigQuery on a schedule.

Check BigQuery for fresh source rows and submit a Dataproc Serverless PySpark batch only when the data is ready.

Orchestrate the GCS to Dataproc to BigQuery lakehouse chain in one flow, with a serverless PySpark transform and a gold table load.

Build a Docker image and push it to Google Cloud Artifact Registry using short-lived OAuth credentials.

Auto-load files into BigQuery the moment they land in a Google Cloud Storage bucket, then archive each object so it never re-triggers.

Stream Google Cloud Pub/Sub messages into Postgres in real time, one governed Kestra execution per message, with safe upserts and Teams alerts.

After a scheduled BigQuery aggregation, publish a completion event to a Google Cloud Pub/Sub topic so downstream consumers and flows react.

Submit a Spark job to Dataproc Serverless on a schedule, with no cluster to manage and cost that tracks the work.

Run a Python data script in a Docker container pulled from a private Google Artifact Registry repository.