I'm building a data enrichment workflow from scraped websites, what stack fits?

A good stack for a website scraping → data enrichment → downstream use workflow usually depends on scale, reliability, and how much “human-in-the-loop” cleanup you need. A solid default stack looks like this:

Recommended stack

1) Scraping / ingestion

• Python for orchestration and extraction logic
• Playwright for JS-heavy sites and dynamic pages
• BeautifulSoup / lxml for static HTML parsing
• Scrapy if you need large-scale crawling and scheduling
• httpx / requests for simple fetches

2) Data processing / enrichment

• Pandas for light-to-medium transformations
• Polars if you want faster columnar processing at scale
• DuckDB for ad hoc local analytics and fast joins
• Great Expectations or Pandera for data validation
• RapidFuzz for fuzzy matching / deduplication
• spaCy or transformers if you need NLP entity extraction

3) Storage

• PostgreSQL as the main system of record
• S3 / GCS / Azure Blob for raw HTML, screenshots, and intermediate files
• Parquet files for cheap analytics-friendly storage
• Redis if you need queues, caching, or rate-limit coordination

4) Workflow orchestration

• Prefect for simpler modern orchestration
• Airflow if you need heavier enterprise scheduling and dependencies
• Dagster if you want strong typing and asset-based pipelines

5) Data enrichment sources

Depending on enrichment type:

• Clearbit / People Data Labs / FullContact for company/person enrichment
• OpenAI API or another LLM for semantic classification, summarization, normalization
• Geo APIs for address normalization/geocoding
• Search APIs if you need entity resolution against public sources

6) Serving / access

• FastAPI for internal APIs
• Metabase / Superset for dashboards
• dbt if you want reproducible transformation layers in SQL

7) Deployment / infra

• Docker everywhere
• Kubernetes if you need scaling and isolation
• Celery + Redis/RabbitMQ for background jobs
• GitHub Actions for CI/CD

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A practical “best default” stack

If you want something modern, flexible, and not overly complex:

• Python
• Playwright + BeautifulSoup
• Prefect
• PostgreSQL + S3
• Pandas or Polars
• dbt for structured transforms
• FastAPI for internal use
• Docker
• OpenAI API / enrichment APIs as needed

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If you’re at different stages

MVP / small scale

• Python
• Playwright
• Pandas
• PostgreSQL
• Prefect or even cron
• Docker

Mid-scale production

• Python
• Scrapy + Playwright
• PostgreSQL + S3 + Parquet
• Prefect/Dagster
• Great Expectations
• Redis/Celery

Large scale / many sites

• Scrapy cluster or distributed crawling
• Kubernetes
• Queue-based architecture
• S3 + data lake format (Parquet/Delta/Iceberg)
• Airflow/Dagster
• Strong observability and retries

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Key design advice

For scraped data enrichment, make sure your pipeline is split into:

1. Raw capture: store original HTML/response payloads
2. Parse/extract: normalize into structured records
3. Enrich: add external/API-derived fields
4. Validate/dedupe: catch bad merges and duplicates
5. Serve: push to DB/API/warehouse

This helps when source sites change or enrichment providers are noisy.

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If you want, I can recommend a stack based on:

• your expected volume,
• whether sites are JS-heavy,
• whether enrichment is via APIs or LLMs,
• and your preferred cloud/provider.