Zapier Scraper

Zapier Scraper collects structured data about integrations and automation templates available across Zapier’s ecosystem. It helps developers, product teams, and researchers explore apps, categories, and ready-made workflows in one clean dataset. The scraper is designed for speed, consistency, and easy downstream analysis.

     

Created by Bitbash, built to showcase our approach to Scraping and Automation!
If you are looking for zapier you've just found your team — Let’s Chat. 👆👆

Introduction

This project extracts detailed information about Zapier integrations and app templates in a structured, machine-readable format. It solves the problem of manually browsing hundreds of apps and workflows by turning them into queryable data. It’s ideal for developers, SaaS founders, automation consultants, and analysts working with automation ecosystems.

Automation Ecosystem Insights

• Collects a complete catalog of Zapier integrations with metadata.
• Retrieves automation templates tied to specific apps.
• Supports restartable runs using pagination offsets.
• Outputs clean JSON suitable for analytics or indexing.
• Scales efficiently across large datasets.

Features

Feature	Description
App Catalog Extraction	Retrieves the full list of Zapier integrations with names, slugs, categories, and metadata.
Template Discovery	Collects automation templates available for specific apps.
Pagination Control	Restart jobs from a defined page to recover from interruptions.
Structured Output	Returns normalized JSON ready for storage or processing.
High Throughput	Optimized for fast data collection with minimal overhead.

---

What Data This Scraper Extracts

Field Name	Field Description
id	Unique identifier of the integration or template.
legacyId	Historical numeric identifier used internally.
name	Name of the Zapier integration or app.
description	Short explanation of the app or workflow.
slug	URL-friendly identifier for the app.
isPremium	Indicates whether the integration requires a paid plan.
categories	App categories such as CRM, Marketing, or Collaboration.
profileUrl	Public profile page of the integration.
apps	Apps involved in a given automation template.
editorUrl	Link to open the automation template editor.

---

Example Output

[
    {
        "id": "3a3e2011-1d79-4dcb-bf02-13f7fae07fc7",
        "name": "Microsoft SharePoint",
        "slug": "sharepoint",
        "isPremium": true,
        "categories": [
            {
                "id": "55",
                "title": "Microsoft",
                "slug": "microsoft"
            }
        ],
        "profileUrl": "https://zapier.com/apps/sharepoint/integrations"
    }
]

---

Directory Structure Tree

Zapier/
├── src/
│   ├── main.py
│   ├── collectors/
│   │   ├── apps_collector.py
│   │   └── templates_collector.py
│   ├── parsers/
│   │   └── response_parser.py
│   ├── utils/
│   │   └── pagination.py
│   └── config/
│       └── settings.example.json
├── data/
│   ├── samples/
│   │   └── output.sample.json
│   └── logs/
├── requirements.txt
└── README.md

---

Use Cases

• Product managers use it to analyze Zapier integrations, so they can identify ecosystem gaps and opportunities.
• Automation consultants use it to discover templates, so they can design workflows faster for clients.
• Developers use it to build internal tooling, so they can integrate automation data into dashboards.
• Market researchers use it to study trends, so they can track category growth over time.
• SaaS founders use it to monitor competitors, so they can position their apps strategically.

---

FAQs

Does this scraper collect both apps and templates? Yes, it supports extracting standalone integrations as well as automation templates associated with specific apps.

Can a failed run be resumed? Yes, pagination controls allow restarting from a defined page without duplicating earlier results.

What output format is supported? The scraper outputs structured JSON, making it easy to store, filter, or transform.

Is this suitable for large-scale analysis? Yes, the data model and performance characteristics are designed for large datasets.

---

Performance Benchmarks and Results

Primary Metric: Processes several hundred integrations per minute under normal conditions.

Reliability Metric: Maintains a high success rate across long-running jobs with restart support.

Efficiency Metric: Uses minimal memory by streaming results incrementally.

Quality Metric: Consistently returns complete records with validated fields and normalized structures.

"Bitbash is a top-tier automation partner, innovative, reliable, and dedicated to delivering real results every time." Nathan Pennington Marketer ★★★★★

"Bitbash delivers outstanding quality, speed, and professionalism, truly a team you can rely on." Eliza SEO Affiliate Expert ★★★★★

"Exceptional results, clear communication, and flawless delivery. Bitbash nailed it." Syed Digital Strategist ★★★★★