Data Engineering Copilot
ChatGPT pluginDebug production data systems
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Rank in Data & analytics
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Data & analytics262nd of 307 by placement
| # | Plugin | Position | 7d |
|---|---|---|---|
| 259 | #5014 | — | |
| 260 | #5048 | — | |
| 261 | #5121 | — | |
| 262 | #5292 | — | |
| 263 | #5373 | — | |
| 264 | #5407 | 794places down | |
| 265 | #5416 | — |
Data Engineering Copilot helps you design, debug, recover, and improve production data systems across Spark, Databricks, Delta Lake, SQL, CDC, Structured Streaming, Airflow, backfills, data quality, observability, and lakehouse architectures. It can diagnose failed or slow pipelines, reason about duplicate and late data, plan safe replay and backfill strategies, review checkpoint and streaming-state risks, tune Spark from execution evidence, and create production-oriented SQL, PySpark, and orchestration patterns. It prioritizes correctness, recoverability, SLA, security, and cost before scaling or redesigning.
Example prompts
- “Debug why this production pipeline is failing or producing wrong data.”
- “Design a reliable CDC or streaming pipeline for this use case.”
- “Plan a safe backfill or replay without duplicating or losing data.”
Skills
data-engineering
Production-focused workflow for designing, debugging, recovering, tuning, and operating data pipelines across Spark, Databricks, Delta Lake, SQL, CDC, Structured Streaming, Airflow, backfills, quality, observability, and lakehouse systems.
- Categories
- Made by
- Krishna Sathvik
- Sign-in
- On Install
- Capabilities
- Design reliable batch, CDC, streaming, and lakehouse architectures, Debug failed, slow, duplicated, stale, or backlogged production pipelines, Plan safe replay, backfill, recovery, and checkpoint strategies, Review Delta MERGE logic for keys, ordering, duplicates, updates, and deletes, Diagnose Spark and Databricks performance using plans, shuffle, spill, skew, and file evidence, Design Structured Streaming state, watermark, checkpoint, and sink behavior, Create production-oriented SQL, PySpark, Databricks, and Airflow patterns, Build data-quality gates, reconciliation, freshness checks, and operational runbooks, Evaluate retry safety, idempotency, schema evolution, and downstream contracts, Use current official docs for Spark, Databricks, Delta Lake, Airflow, and runtime-specific behavior
- Availability
- Available
- Version
- 0.1.0
- Keywords
- data-engineering, spark, databricks, delta-lake, cdc, streaming, airflow, data-quality
- Created
- September 23, 2026