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Data Engineering Copilot

Fix production pipelines without losing or duplicating data.

A production-minded assistant for Spark, Databricks, Delta Lake, CDC, streaming, Airflow, dbt, and Microsoft Fabric. It works from your logs, plans, and code to a leading hypothesis, one decisive check, and a safe next step.

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01 — The product

What is Data Engineering Copilot?

Data Engineering Copilot helps you design, build, debug, recover, and tune data pipelines. It covers batch, CDC, streaming, lakehouse, and warehouse systems, plus dbt models, backfills, data quality, observability, governance, and platform migrations.

It’s written for data engineers and platform owners who run pipelines with SLAs, downstream consumers, and real recovery costs. Simple syntax or concept questions get a direct answer.

For incidents, it states a leading hypothesis with a confidence level and the observation that would disprove it, and keeps facts, assumptions, and unknowns apart. Correctness and recoverability come before performance and cost. It doesn’t reach for bigger clusters by default.

02 — Real workflow

Silver rows double after an Airflow retry

The problem

An hourly load task failed partway through and Airflow retried it. Downstream counts for that interval are now roughly twice what the source shows.

Load taskpython
# load_orders: retries=2, schedule='@hourly'
df = (spark.read.table('bronze.orders_cdc')
      .where(col('ingest_hour') == run_hour))

df.write.mode('append').saveAsTable('silver.orders')

How Data Engineering Copilot approaches it

  1. 1Names the most likely cause, a non-idempotent append rerun on the same interval. It gives a confidence level, the facts behind it, and what would disprove it.
  2. 2Gives the decisive read-only check: a duplicate-key count on silver.orders, matched against the retry and run history.
  3. 3Proposes reversible containment. Pause exact-count consumers and quarantine the affected interval before any repair.
  4. 4Replaces the append with a deduplicated MERGE ordered by source sequence. It adds a replay plan, validation queries, and a rollback trigger.

Expected result

An incident write-up covering cause, verification, containment, permanent fix, and risk if wrong. It includes the SQL and PySpark needed to carry it out.

Illustrative workflow. It doesn’t depict a real session, system, or result.

03 — Areas of expertise

Where it helps

  • Streaming and checkpoints

    Treats checkpoints as production state. Covers watermarks, state growth, backlog, and sink semantics, and separates engine fault tolerance from end-to-end exactly-once for external writes.

  • Spark performance

    Diagnoses from stage metrics and physical plans: skew, shuffle, spill, file layout, and memory pressure. No arbitrary partition counts, forced broadcasts, blind scaling, or untargeted table maintenance.

  • Microsoft Fabric and OneLake

    Lakehouse versus Warehouse choices, shortcuts versus physical copies, and medallion layers with explicit grain. Plans deployments knowing Lakehouse definitions don’t copy table data by default.

  • dbt and warehouse models

    Incremental models with a unique key, late-arrival window, strategy, and full-refresh conditions. Adds grain, relationship, freshness, and reconciliation tests, without assuming unique_key enforces uniqueness.

04 — Working with Data Engineering Copilot

What to provide and what to expect

Share

  • Error messages, driver or task logs, and Airflow task history
  • Spark UI metrics or physical plans
  • Pipeline code, SQL, dbt models, MERGE logic, and configs
  • Runtime, platform, and version details, such as Databricks, Fabric, or Airflow
  • A timeline of what changed before the failure

Expect

  • A most likely cause, a decisive check, and containment kept apart from the permanent fix
  • Production-oriented SQL, PySpark, dbt, or Airflow code
  • Replay and backfill plans with validation and rollback
  • Data quality test matrices with thresholds, owners, and actions

It doesn’t reach clusters, run jobs or queries, trigger Airflow, change checkpoints, or run backfills on its own. It works from the evidence you share and won’t call a fix verified without results.