What does the JSON output structure look like?

Each file produces a record with name, size_bytes, mime_type, and filesystem timestamps. The format is consistent across all file types, making it easy to parse programmatically.

Can this catch file type mismatches before ingestion?

It will flag the MIME type as inferred from the file extension, which catches obvious mismatches like a .csv that was renamed from a .xlsx. It does not perform deep binary inspection of file contents.

What is the batch limit?

Up to 50 files or 2GB total per batch, whichever is reached first. For larger deliveries, split into multiple batches.

Is this a replacement for a proper schema validation step?

No — it is a complement. Metadata inspection tells you what you have before you parse it. Schema validation happens after ingestion. Together they reduce the failure surface at both stages.

File Metadata Reports for Data Engineers Validating Pipeline Inputs

Before a file enters a pipeline, you need to know what you are working with. The File Metadata Report tool generates a structured JSON record of filename, size in bytes, MIME type, and filesystem timestamps for every file in a batch — a fast, reliable pre-ingestion inspection step.

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Data engineers regularly receive file dumps from external partners, internal teams, or automated exports. Blindly ingesting these without confirming file types and sizes is a common source of pipeline failures. A quick metadata scan catches misnamed files, unexpected formats, or suspiciously small files before they cause downstream issues. The JSON output structure maps cleanly to logging schemas and can be piped into validation checks or stored alongside ingestion records.

Deliteful supports all the formats that show up in typical data pipelines: CSV, Excel, JSON, PDF, DOCX, ZIP, and common image types. You can inspect up to 50 files per batch. The tool does not modify originals, and the JSON report is returned immediately — no transformation step, no format negotiation.

How it works

1
Upload the incoming file batch
Drop up to 50 files from the partner delivery, export, or staging directory.
2
Run the metadata report
Deliteful returns filename, size in bytes, MIME type, and creation and modification timestamps for each file.
3
Use the JSON output in your validation workflow
Feed the report into your pre-ingestion checks, store it alongside the pipeline run log, or flag anomalies before processing begins.

Frequently asked questions

What does the JSON output structure look like?: Each file produces a record with name, size_bytes, mime_type, and filesystem timestamps. The format is consistent across all file types, making it easy to parse programmatically.
Can this catch file type mismatches before ingestion?: It will flag the MIME type as inferred from the file extension, which catches obvious mismatches like a .csv that was renamed from a .xlsx. It does not perform deep binary inspection of file contents.
What is the batch limit?: Up to 50 files or 2GB total per batch, whichever is reached first. For larger deliveries, split into multiple batches.
Is this a replacement for a proper schema validation step?: No — it is a complement. Metadata inspection tells you what you have before you parse it. Schema validation happens after ingestion. Together they reduce the failure surface at both stages.

Create your free Deliteful account with Google and start inspecting file batches before they hit your pipeline — takes about 30 seconds to set up.