Spark Transformation Helper
Data Engineering
Write efficient PySpark transformations with partitioning, skew notes, and a way to validate row counts without changing grain.
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Curated AI prompts for data engineering, software engineering, and AI engineering.
Data Engineering
Write efficient PySpark transformations with partitioning, skew notes, and a way to validate row counts without changing grain.
Data Engineering
Design a star or snowflake schema with fact grain, measures, dimensions, and SCD recommendations for a defined analytics process.
Data Engineering
Scaffold a clean Airflow DAG with retries, timeouts, and task boundaries an operator can run without guessing hidden state.
Data Engineering
Define practical data-quality checks and thresholds for a critical table, including fail vs warn severity and owner next steps.
Data Engineering
Design a reliable batch or streaming pipeline from source to warehouse, including load pattern, idempotency, and failure handling.
Data Engineering
Generate a production-ready dbt model with tests, docs, and naming that match the stated warehouse grain, without inventing columns.
Software Engineering
Turn incident evidence into a blameless RCA with a causal chain, contributing factors, and corrective actions that owners can actually execute.
Software Engineering
Review a proposed API change for compatibility, versioning, client blast radius, and a rollback path that does not strand old callers.
Software Engineering
Audit a pull request for production risk, missing tests, observability gaps, and a rollback plan instead of style-only review comments.
Software Engineering
Map timeouts, retries, queues, and partial failure across a service graph so retry storms and poison messages are visible before they page you.