We extract course structures, career paths, skill tags, syllabus metadata, and pricing from Codecademy. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Course Metadata objects from codecademy.com. All fields typed and schema-versioned.
"course_id": "learn-python-3", "title": "Learn Python 3", "category": "Programming", "difficulty": "Beginner", "duration_hours": 25, "is_pro": false, "certificate_available": true
| # | course_id | title | url | category | difficulty | duration_hours |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Syllabus & Modules objects from codecademy.com. All fields typed and schema-versioned.
"course_id": "learn-python-3", "module_id": "mod-101", "module_title": "Hello World", "module_order": 1, "lesson_count": 5, "quiz_count": 1, "project_count": 0
| # | course_id | module_id | module_title | module_order | lesson_count | quiz_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Career Paths objects from codecademy.com. All fields typed and schema-versioned.
"path_id": "full-stack-engineer", "title": "Full-Stack Software Engineer", "total_courses": 34, "total_hours": 150, "target_roles": "['Full-Stack Developer', 'Backend Engineer']", "includes_certification": true
| # | path_id | title | url | description | total_courses | total_hours |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Skill Paths objects from codecademy.com. All fields typed and schema-versioned.
"path_id": "analyze-data-with-sql", "title": "Analyze Data with SQL", "skill_focus": "SQL", "total_lessons": 12, "total_hours": 10, "is_pro_exclusive": true, "updated_at": "2026-03-12T00:00:00Z"
| # | path_id | title | url | skill_focus | total_lessons | total_hours |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Pricing & Plans objects from codecademy.com. All fields typed and schema-versioned.
"plan_id": "pro-annual", "plan_name": "Codecademy Pro", "billing_cycle": "Annual", "price_annual": 149.99, "currency": "USD", "free_trial_days": 7, "target_audience": "Individuals"
| # | plan_id | plan_name | billing_cycle | price_monthly | price_annual | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Codecademy scraper handles every layer of the platform: course catalogues, syllabi, career paths, and pricing data, with JavaScript rendering and anti-bot circumvention built in.
Extract the full catalogue, filters, and categorisation across all programming languages and disciplines.
Capture module structures, lesson counts, quiz distribution, and expected completion times per course.
Extract path structures, required courses, and target outcomes for structured learning tracks.
Map course dependencies and learning trees to understand structural progression.
Capture language, framework, and tool metadata associated with every lesson.
Track Pro, Plus, and Enterprise tier pricing changes across multiple geographic regions.
Extract off-platform project specifications and portfolio building requirements.
Capture aggregate ratings and student enrollment counts per course where available.
Identify which courses and paths yield professional certificates upon completion.
Brief in. Clean data out.
Provide target categories, career paths, or languages. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, and session management for codecademy.com.
Schema validation, null-rate checks, and path-mapping verification before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
EdTech platforms deploy strict rate limiting and rely heavily on dynamic frontend frameworks. Here is how we maintain reliable extraction.
Codecademy's catalogue uses React-based dynamic hydration. We use Playwright to execute JavaScript, wait for network idle states, and capture the fully rendered DOM.
Cloudflare protection and rate-limiting require careful request pacing. We use residential proxies and throttle concurrency to maintain continuous access without triggering blocks.
Syllabi are deeply nested. Our parsers flatten module, lesson, and quiz hierarchies into relational records suitable for SQL databases.
EdTech platforms update DOM structures frequently. We use fallback chains based on internal API responses and JSON-LD payloads to ensure stable data extraction.
We maintain a hash index of last-seen values per course. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Track Codecademy course launches, syllabus updates, and pricing changes to inform your own product roadmap.
Extract structured syllabi to map industry-standard learning paths against university or bootcamp curricula.
Analyse the volume and updates of specific tech stack courses to gauge emerging skill demand.
Feed course metadata, ratings, and pricing into course discovery and review engines.
Evaluate Codecademy's enterprise offerings against skill requirements to optimise L&D budgets.
Use prerequisite trees and path structures to train ML models on technical skill dependencies.
"Codecademy's curriculum maps the exact evolution of modern software engineering. Querying that syllabus data requires a dedicated pipeline."
Most teams underestimate the investment required: reliable Codecademy scraping requires residential proxies, full JavaScript rendering for React components, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our codecademy.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions where required to prevent IP bans.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About codecademy.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Codecademy is generally permissible under applicable law. DataFlirt targets only public, non-authenticated course metadata, syllabi, and pricing data. We do not extract personal user data or circumvent authentication walls to access paid Pro terminal environments.
We use Playwright to execute full browser sessions, allowing React components to hydrate and render before we extract the DOM or intercept the underlying JSON API payloads.
Yes. We capture the complete hierarchy of career paths, including all nested courses, modules, and prerequisites required for completion.
Pipelines can be configured to run daily or weekly. A full catalogue refresh typically completes within 2 to 4 hours.
No. We extract structural metadata, syllabi, text descriptions, and pricing. We do not extract proprietary video content or interactive terminal exercises hidden behind the Pro paywall.
Our packages start with full catalogue extraction delivered weekly. Contact us with your specific schema requirements for a scoped quote.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or a continuous curriculum monitoring feed, we scope, build, and operate the pipeline. Tell us what you need.
