We're building an AI training data pipeline from public websites, what infrastructure makes sense?

A sensible infra stack depends mostly on scale, freshness, and compliance. For public-web training data, I’d design around a pipeline that is crawl → fetch → normalize → dedupe/filter → quality checks → store/version → export to training jobs.

1) Core architecture

A. Discovery / URL sourcing

• Seed manager: starts from known domains, sitemaps, RSS feeds, public indexes, or curated URL lists
• Frontier service: maintains crawl priority, politeness, recrawl schedules, and per-domain limits
• URL deduper: prevents re-fetching the same canonical URL repeatedly

B. Crawl / fetch layer

• Distributed fetchers running in containers or VMs
• Use:
  • standard HTTP client for most pages
  • headless browser only when necessary
• Politeness controls:
  • robots.txt checks
  • rate limiting per host
  • backoff / retry policies
  • user-agent and contact info
• Proxy management if needed for reliability, but avoid anything that looks like bypassing access controls

C. Parsing / extraction

• HTML-to-text and structured extraction
• Preserve:
  • raw HTML snapshot
  • cleaned text
  • metadata (title, canonical URL, language, timestamp, headers)
• Optional:
  • boilerplate removal
  • main-content extraction
  • language detection
  • document type classification

D. Quality / safety filters

• Deduplication
  • exact dedupe via hashes
  • near-dedupe via SimHash/MinHash
• Content filtering
  • spam, templated pages, low-text pages
  • adult content, malware, profanity, etc. depending on policy
• PII detection/redaction
• Copyright/licensing metadata tagging
• Domain reputation / allowlist / blocklist
• Model training suitability scoring

E. Storage / dataset layer

Use multiple stores:

• Object store for raw crawl artifacts and parsed documents
  • S3/GCS/Azure Blob or on-prem equivalent
• Metadata DB for crawl state and document metadata
  • Postgres for simpler setups
  • DynamoDB/Cassandra/Bigtable for very large frontier state
• Search/index for debugging and review
  • OpenSearch/Elasticsearch
• Warehouse/lakehouse for analytics and dataset builds
  • Iceberg/Delta Lake/Hudi on object storage

F. Dataset build / export

• Build versioned dataset snapshots:
  • train / validation / test splits
  • source-domain-balanced subsets
  • language or topic slices
• Export to:
  • Parquet / JSONL
  • sharded object-store files
  • manifest files for training jobs
• Track dataset lineage:
  • source crawl version
  • filters used
  • hashes
  • transform code version

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2) Recommended infrastructure patterns

Small-to-medium scale

If you’re just getting started:

• Kubernetes for fetchers and processors
• Postgres for crawl state and metadata
• S3-compatible object storage for raw and processed docs
• Airflow or Dagster for orchestration
• Spark or Ray for batch processing
• OpenSearch for indexing/debugging

This is a good balance of simplicity and flexibility.

Large scale

If you’re crawling millions to billions of pages:

• Kubernetes + autoscaling workers
• Kafka/PubSub/SQS for URL/task queues
• Redis for fast frontier state / rate limiting
• Object storage + lakehouse tables (Iceberg/Delta)
• Trino/Presto/Spark/Ray for large-scale processing
• Feature flags/config service for filter policy changes
• Strong observability: Prometheus, Grafana, Loki/ELK, OpenTelemetry

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3) Key services to include

Frontier manager

Responsibilities:

• normalize URLs
• schedule recrawls
• enforce politeness and per-site quotas
• prioritize important domains/pages
• maintain crawl status

Document processing workers

Responsibilities:

• fetch page
• extract text and metadata
• classify content
• compute hashes
• emit structured records

Policy engine

Rules such as:

• block/allow domains
• accept only certain content types
• reject login-gated / paywalled / robots-blocked content
• PII/redaction settings
• retention rules

Lineage and audit system

Track:

• source URL
• fetch time
• headers and content hash
• processing steps
• dataset inclusion/exclusion reason

This becomes crucial when someone asks, “Why is this page in the dataset?”

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4) Data model suggestion

Store each page/document as:

• url
• canonical_url
• domain
• fetch_timestamp
• http_status
• content_type
• language
• raw_html_uri
• extracted_text_uri
• text_hash
• simhash
• license_hint
• robots_allowed
• pii_flags
• quality_score
• filter_decisions
• crawl_job_id
• pipeline_version

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5) Operational concerns

Cost control

• avoid headless browser use unless necessary
• compress artifacts
• keep raw + processed tiers
• de-duplicate aggressively
• recrawl only valuable pages

Reliability

• idempotent processing
• retry with jitter
• checkpointing
• dead-letter queues
• per-domain circuit breakers

Observability

Track:

• fetch success rates
• bytes downloaded
• pages/sec per worker
• extraction failure rates
• content filter rejection rates
• duplicate rates
• domain-level throttling stats

Governance/compliance

For public websites, make sure the system supports:

• robots.txt compliance
• honoring site policies where required
• takedown handling
• provenance tracking
• jurisdiction-aware retention controls
• review of sensitive content sources

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6) A pragmatic default stack

If I had to pick a practical starting stack:

• Kubernetes for compute
• Kafka or SQS/PubSub for queues
• Postgres for metadata and crawl frontier
• S3 + Parquet for artifacts and dataset shards
• Airflow/Dagster for orchestration
• Spark or Ray for batch extraction/dedup/filtering
• OpenSearch for inspection
• Prometheus/Grafana for metrics
• Great Expectations or custom checks for data validation
• Iceberg if you want table versioning and reproducible dataset snapshots

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7) If you want the simplest viable system

A very lean version:

• one queue
• one crawler service
• one parser service
• one object store
• one metadata DB
• one orchestration tool
• one dataset builder

That’s often enough to prove the workflow before scaling.

If you want, I can also propose:

1. a reference architecture diagram,
2. a cloud-native stack on AWS/GCP/Azure, or
3. a minimal MVP design for your first 90 days.