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EXAMPLE QUESTIONS
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175 Blueprints
Export a Slack channel's history to internal storage and load it into BigQuery on a schedule.
Extract sales data to S3, aggregate it with DuckDB, and email a CSV report on the first of every month.
Extract data from a REST API, reshape it with Python and Polars, then run analytical SQL on it with DuckDB, all in one Kestra flow.
Run a chain of SurrealDB CRUD queries with Kestra and post the final result to Slack.
Query Snowflake every morning, format the metrics as Markdown, and publish them as an editable Slack Canvas.
Poll a SQL Server queue-style table for pending rows, flip them to processed in the same transaction, and fan out to a per-row task the moment work appears.
Run a Kestra workflow automatically whenever a Snowflake query returns matching rows.
Extract CSV data, mask PII columns with DuckDB SQL, then load the anonymized result into BigQuery.
Extract JSON from a REST API, enrich it, and load it in parallel to Postgres and Amazon S3.
Run an Apify actor under hard cost and run caps, then branch on the returned item count so a degraded scrape pages the on-call instead of silently shipping bad data.
Purge expired transients, spam comments, and old post revisions from a MariaDB-backed WordPress database on a schedule, then reclaim disk space with OPTIMIZE TABLE.
Clone a SQLMesh project from Git, run `sqlmesh plan --auto-apply`, and query the resulting DuckDB database with Kestra.
Run a SQL query against your Snowflake data warehouse and capture the results in Kestra.
Insert records into SurrealDB and store query results as downloadable artifacts in Kestra internal storage.
Call a REST API on a daily schedule, reshape the JSON with jq, bulk load it into Postgres with COPY, and notify Slack on success or failure.
Nightly job that lists a self-hosted MinIO bucket, filters objects older than a retention window in Python, and deletes only the expired ones.
Run a daily SQL Server aggregate query, convert it to CSV, and email it as an attachment, skipping empty days and alerting Slack on failure.
End-to-end Snowflake ETL with Kestra: create a database and table, stage a CSV, COPY INTO Snowflake, and run analytical SQL.
Stage a delta file, COPY into a staging table, and MERGE into the Snowflake target as one atomic transaction for an idempotent upsert.
Poll a Snowflake stream for new rows, load each batch into Postgres in parallel, run dbt transformations, and alert Slack on failure.
Stream SQL Server row-level changes in real time with the Debezium SQL Server connector and alert Microsoft Teams on high-value changes, no Kafka Connect required.
Extract changed rows from a source SQL Server table since the last KV-tracked watermark and bulk-insert them into a destination instance with chunked JDBC batches.
Stage changed rows since a KV-tracked watermark, MERGE them into a target table with dedup, and advance the watermark, all inside one SQL Server transaction.
Fetch Stripe charges and customers, enrich them in Python, and upsert into a PostgreSQL CRM table on a daily schedule with idempotent deduplication.
