FOR DATA ENGINEERS

Debug the payload
before it breaks the DAG.

Event streams, webhook payloads, dead-letter records, config blobs — inspect the one malformed message, count events by type, or catch schema drift by asking. QDev writes the jq and runs it locally.

✓Find the record with the null field without a scratch script
✓Counts & rollups are exact — jq computes them, not a model
✓Production data & PII stay on your machine — never uploaded

Add to Chrome Add to Firefox

⌘K

try

● EVENTS.NDJSON — 128,400 RECORDS · LOCAL

{
  "events": [
    {
      "ts": "2026-07-04T09:14:02Z",
      "type": "page_view",
      "user_id": "u_1204",
      "status": "ok"
    },
    {
      "ts": "2026-07-04T09:14:03Z",
      "type": "purchase",
      "user_id": null,
      "status": "ok"
    },
    {
      "ts": "2026-07-04T09:14:05Z",
      "type": "checkout",
      "user_id": "u_0098",
      "status": "error"
    }
  ]
}

↑ Ask a rollup or a validation — jq scans all 128k records locally. Only the schema is ever sent.

exact rollups · reproducible jq · nothing uploaded

SOUND FAMILIAR?

3am, the pipeline's red

Schema drift, silently

An upstream service started sending user_id: null and half a partition got dropped before anyone noticed.

The dead-letter queue

A few thousand records failed to parse. You need the one field that's wrong across all of them — fast.

NDJSON too big to grep

A 300 MB export won't open, and jq on the CLI means quoting a filter three times to get the escaping right.

ASK, DON'T SSH INTO A NOTEBOOK

From a bad record to a root cause

Catch schema drift

Find records missing a field, with a wrong type, or a value outside the enum you expected.

events where status isn't ok or error

Roll up & count

Events by type, error rate per source, volume per hour — exact counts, computed by jq.

count events by type per hour

Triage the DLT

Isolate the malformed records and see exactly which field failed, across the whole batch.

records where amount is not a number

Flatten nested records

Explode a nested payload into flat rows ready to reload or diff against the source.

flatten payload.items into one row each

FITS YOUR STACK

Wherever your JSON lands

STREAMS

Kafka / events

Event and message payloads — grouped, counted, and validated locally.

NDJSON

Line-delimited

Log exports and batch files too big to open elsewhere.

WEBHOOKS

API payloads

Inspect the exact body that failed a downstream contract.

CONFIG

Configs & manifests

dbt, Airflow, package manifests — read the shape at a glance.

# QDev: "records where user_id is null" → paste the jq into your DLT replay [.events[] | select(.user_id == null)] | length # 412 dropped

● PROD DATA STAYS IN PROD

Payloads never leave the tab.

Customer records, PII, tokens caught in an event — the values stay local. Only the schema (field names & types) and your question are ever sent, and only on the hosted tier. Handling regulated data? Run local AI, free, and nothing leaves at all.

✓jq runs in a sandboxed Web Worker — no network access

✓Hosted proxy is stateless & zero-retention by default — logs nothing unless you opt in to training sharing

✓No data pipeline to a vendor, no sampling, no copy of your stream

One record is malformed. Find it.

Install free, keep your stream local, and let ⌘K write the jq that isolates the bad payload.

Add to Chrome