Using aCloud Run functiontriggered by Cloud Storage to load the data into BigQuery is the best solution because it minimizes both cost and maintenance while providing low-latency data ingestion. Cloud Run is a serverless platform that automatically scales based on the workload, ensuring efficient use of resources without requiring a dedicated instance or cluster. It integrates seamlessly with Cloud Storage event notifications, enabling real-time processing of incoming files and loading them into BigQuery. This approach is cost-effective, scalable, and easy to manage.
The goal is to load small CSV files into BigQuery upon arrival (event-driven) with minimal latency, cost, and maintenance. Google Cloud provides serverless, event-driven options that align with this requirement. Let’s evaluate each option in detail:
Option A: Cloud Composer (managed Apache Airflow) can schedule a pipeline to check Cloud Storage every 10 minutes, but this polling approach introduces latency (up to 10 minutes) and incurs costs for running Composer even when no files arrive. Maintenance includes managing DAGs and the Composer environment, which adds overhead. This is better suited for scheduled batch jobs, not event-driven ingestion.
Option B: A Cloud Run function triggered by a Cloud Storage event (via Eventarc or Pub/Sub) loads files into BigQuery as soon as they arrive, minimizing latency. Cloud Run is serverless, scales to zero when idle (low cost), and requires minimal maintenance (deploy and forget). Using the BigQuery API in the function (e.g., Python client library) handles small CSV loads efficiently. This aligns with Google’s serverless, event-driven best practices.
Option C: Dataproc with Spark is designed for large-scale, distributed processing, not small CSV ingestion. It requires cluster management, incurs higher costs (even with ephemeral clusters), and adds unnecessary complexity for a simple load task.
Option D: The bq command-line tool in Cloud Shell is manual and not automated, failing the “upon arrival” requirement. It’s a one-off tool, not a pipeline solution, and Cloud Shell isn’t designed for persistent automation.
Why B is Best: Cloud Run leverages Cloud Storage’s object creation events, ensuring near-zero latency between file arrival and BigQuery ingestion. It’s serverless, meaning no infrastructure to manage, and costs scale with usage (free when idle). For small CSVs, the BigQuery load job is lightweight, avoiding processing overhead.
Extract from Google Documentation: From "Triggering Cloud Run with Cloud Storage Events" (https://cloud.google.com/run/docs/triggering/using-events): "You can trigger Cloud Run services in response to Cloud Storage events, such as object creation, using Eventarc. This serverless approach minimizes latency and maintenance, making it ideal for real-time data pipelines." Additionally, from "Loading Data into BigQuery" (https://cloud.google.com/bigquery/docs/loading-data-cloud-storage-csv): "Programmatically load CSV files from Cloud Storage using the BigQuery API, enabling automated ingestion with minimal overhead."
[References: Google Cloud Documentation - "Cloud Run Events" (https://cloud.google.com/run/docs), "BigQuery Load Jobs" (https://cloud.google.com/bigquery/docs/loading-data)., ]
