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17 Cloud Data

15 Blueprints

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Detect data anomalies in new S3 files with DuckDB SQL and email a CSV of the bad rows.

Watch a CloudWatch backlog metric and fire an EMR Serverless Spark job only when work is pending.

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

Trigger a Python data pipeline automatically whenever a new file lands in an Amazon S3 bucket.

Extract CSV data, mask PII columns with DuckDB SQL, then load the anonymized result into BigQuery.

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.

Run a Glue ETL job, validate the output with Athena, then cache the headline KPI in DynamoDB.

Sync AWS resource metadata into Postgres with CloudQuery, then flag public S3 buckets with SQL.

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

Reprocess an S3 file only when it changed, on a schedule, using Python and boto3.

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.

Query Apache Druid, convert results to CSV, stage them on S3, and load into Amazon Redshift.

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