← Data Engineering CopilotCONTENT HISTORY

Update to Data Engineering Copilot

Snapshot Oct 9, 2026 · 00:12 UTC · version 0.1.0

WHAT CHANGED · RULE-BASED ANALYSIS

Package or technical metadata updated

Discoverability changed from “UNLISTED” to “LISTED”.

Observed in package metadata. These changes alone do not establish a new customer-facing feature.

Discoverability

Before

UNLISTED

After

LISTED

Share url

Before

Not present

After

https://chatgpt.com/plugins/plugins_6ab424ad5b448191b8fede2ebf569333?open_in_app

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Full technical diff · 2 changed fields

changed /discoverability

BEFORE
"UNLISTED"
AFTER
"LISTED"

changed /share_url

BEFORE
null
AFTER
"https://chatgpt.com/plugins/plugins_6ab424ad5b448191b8fede2ebf569333?open_in_app"
Full snapshot data
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  "discoverability": "LISTED",
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  "is_template": false,
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        "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"
      ],
      "category": "Data & Analytics",
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      "default_prompt": "Debug why this production pipeline is failing or producing wrong data.",
      "default_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."
      ],
      "developer_name": "Krishna Sathvik",
      "logo_url": "https://files.openai.com/content?id=file_00000000524481f890727a5fa01bd762",
      "logo_url_dark": null,
      "long_description": "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.",
      "plugin_category_id": "data & analytics",
      "privacy_policy_url": null,
      "screenshot_urls": [],
      "short_description": "Debug production data systems",
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      "website_url": null
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    "onboarding_skill_name": null,
    "requires_local_executor": false,
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        "interface": {
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          "default_prompt": "Debug why this production pipeline is failing or producing wrong data.",
          "display_name": "Data Engineering Copilot",
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          "short_description": "Debug production data systems"
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SHA-256 of public snapshot: 874a37411dca48d80547b239d38e93d880460084c83ee151fcf0b50ba05d0929