AEP Viewer

AEP Viewer helps engineers and analysts inspect, validate, and visualize event payloads and schemas for debugging and data quality.

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About This Tool

The AEP Viewer is a data inspection tool that parses input JSON payloads containing events or attributes, infers a schema, and highlights schema conformity and data quality. It renders a concise overview of field types, counts, and missing values, while surfacing potential anomalies. Conceptually, it treats the payload as a data contract and uses rule-based checks to identify type mismatches, required fields, and unexpected keys. This supports data engineers during ingestion, validation, and debugging. Unique value comes from automatic schema inference, per-event consistency checks, and exportable summaries suitable for data catalogs, governance, and automation workflows. It integrates into data pipelines by providing structured outputs and a reproducible validation trail.

How to Use

  1. Provide inputs: paste or upload the AEP payload JSON and optional schema_version.
  2. Configure view_mode and max_items if needed.
  3. Run parsing, schema inference, and validation.
  4. Review outputs: field types, counts, and validation_errors.
  5. Export results as JSON or CSV for downstream tasks.
How to use aep viewer online

Frequently Asked Questions

Find Quick Answers

What is AEP Viewer used for?
AEP Viewer is used to inspect and validate event payloads and their schemas. It highlights field types, counts, missing values, and inconsistencies, enabling engineers and analysts to debug data pipelines, verify onboarding data, and produce exportable reports without writing code.
Which payload formats are supported?
Supports JSON payloads consisting of events or attributes; accepts arrays or nested objects; validates against inferred schemas; timestamps must be ISO 8601; large payloads are processed in streaming chunks where possible.
Can I export results?
Yes; you can export a validation report, inferred schema, and summary as JSON or CSV; exports are suitable for data catalogs, governance docs, and automation pipelines.
Does it validate against a schema?
It can infer a schema from the payload or accept a user-specified version. It checks required fields, types, and unexpected keys, and reports discrepancies with actionable details.

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