Batch PDF generation from one upload one upload

Batch PDF Generation as a Professional Workflow

Batch PDF generation is the practice of running a document workflow on a scheduled or recurring basis — the same template, the same data source, the same delivery method — reliably, every time. Where bulk generation is about hitting a high volume in a one-off run, batch processing is about making that run a defined, repeatable job.

What Makes a Batch Workflow Different

A batch workflow has three fixed pieces: the template (the document design), the data source (the spreadsheet or API feed), and the delivery method (ZIP, email, Drive, or SFTP). Once those three are configured, triggering the batch is mechanical — you're not redesigning or remapping, you're running a job. Monthly invoices for 400 clients, end-of-quarter certificates for a training cohort, weekly compliance reports — these are batch workflows, not one-time bulk runs.

The EZdoc Batch Model

EZdoc renders one PDF per data row in parallel. The template handles the design; the data handles the personalization. Column headers in your spreadsheet map automatically to fields in the template, so setup is a one-time task. Once the template is configured, swapping in the next month's data file and re-running takes seconds. For automated pipelines, the REST API accepts the dataset as an asynchronous merge job; clients poll its status and download the finished output.

When to Use Batch vs. On-Demand

Use a multi-row merge job when your document workflow is periodic and data-driven: billing runs, cohort certificates, scheduled reports, roster-based mailings. For an event-driven workflow, your system can submit a one-row merge job against the same reusable template. Public API v1 remains asynchronous in both cases: create the job, poll its status, and download the output when it is complete.

Getting the Most from a Batch Run

The quality of a batch run is determined largely by the quality of the template. A well-structured template with clean variable placement, consistent typography, and sensible defaults for missing fields will produce a professional result across every row, including rows where some fields are empty. Build and test the template against a small sample first — five or ten rows — before running the full dataset. Once the template is right, the batch output will be right at any size.