插件

Data Engineering Copilot

Tekris Labs · tekrislabs.com

ChatGPT

Design, build, debug, recover and optimize dependable data pipelines and platforms. Work across Apache Spark, Databricks, Delta Lake, structured streaming, CDC, orchestration, dbt, SQL warehouses, Microsoft Fabric/OneLake, data quality, lineage and incident response. Inspect actual code, logs, query plans and data contracts where the user supplies them or authorized tools are available; identify root causes, plan safe replay and backfills, check schema/row grain, and validate fixes. Includes multi-agent coordination guidance when genuine subagent tools exist. Skills-only: no automatic access to clusters, credentials, datasets or production systems; executions and changes require enabled tools and permissions.

How to install Data Engineering Copilot in ChatGPT

  1. Open the official listing for Data Engineering Copilot in the ChatGPT workspace you want to use.
  2. Choose Install plugin if available. Follow any connection steps for the apps listed on the page.
  3. Open a new conversation on a supported ChatGPT surface. Select Data Engineering Copilot from the plugin options or mention it by name.
Official ChatGPT setup guide

How to use Data Engineering Copilot

Check the marketplace listing or publisher’s documentation for supported actions and examples.

历史排名

按采集日期查看 ChatGPT 公开插件目录的展示顺序。热门分类是官方精选,目录未公布安装量或使用量。

– 名次 #196
采集明细 (3)
采集明细
采集日期分类名次
Data & Analytics#196
Data & Analytics#197
Data & Analytics#198

Frequently asked questions

Why is Install plugin missing?

Check your account, workspace, plan and region. If the listing says Desktop only, open it in the ChatGPT desktop app.

功能

  • Architect batch
  • CDC
  • streaming
  • lakehouse and warehouse data systems
  • Debug duplicates
  • data loss
  • latency
  • failures and stale pipelines from evidence
  • Plan safe backfills
  • replay
  • checkpoint recovery and rollback strategies
  • Review CDC merge ordering
  • delete propagation and idempotent writes
  • Diagnose Spark and Databricks query plans
  • shuffle
  • skew
  • spill and file layout
  • Design Microsoft Fabric Lakehouse
  • OneLake
  • shortcuts and deployment flows
  • Design dbt/SQL transformations
  • model grain
  • incremental updates and contracts
  • Review Airflow orchestration
  • retries
  • scheduling and backfill safety
  • Build data quality
  • freshness
  • reconciliation and observability checks
  • Assess access control
  • governance
  • PII handling and data lineage
  • Coordinate supported specialist agents and evidence-based verification
  • Prepare version-aware implementation plans and release/incident evidenceShow more