How to Scrape User Accounts on Instagram and TikTok with AWS

Looking for a clean, compliant way to collect public Instagram and TikTok account data with AWS? This guide gives you a simple, production-ready path. It focuses on public pages only, steady throughput, low cost, and clear operational guardrails.

Who this is for

Growth teams and analysts needing reliable, structured public profile data.
Engineers building ETL pipelines without heavy browser automation.
Product teams validating competitors and market trends at modest scale.

Legal & Ethical Boundaries

Collect public pages only; do not bypass logins, permissions, or private content.

Follow platform terms and robots guidance; keep your rate and concurrency reasonable.

Document business purpose and retain audit evidence for compliance.

Architecture Overview (Minimal, Proven)

Entry

API Gateway exposes a controlled ingest endpoint and applies throttling.

Workers

AWS Lambda (Python) fetches public profiles and parses visible fields.

Storage

DynamoDB for structured profile snapshots; S3 for raw page fragments.

Decoupling

SQS queues absorb spikes; ingestion and persistence stay independent.

Observability

CloudWatch metrics/alerts; orchestration with Step Functions if needed.

Why this works

Small, fast Lambdas keep cold starts low and failures contained.
Queue-based flow turns bursts into steady workloads.
DynamoDB offers cheap point lookups and easy upserts; S3 gives long-term traceability.

Data Model Quick Reference

Instagram profile snapshot (example)

{
  "username": "acme",
  "name": "Acme Studio",
  "followers": 12450,
  "following": 315,
  "bio": "Design, motion, and daily experiments",
  "external_url": "https://acme.example",
  "is_private": false,
  "last_seen_at": "2025-10-18T09:00:00Z",
  "etl_version": "v1"
}

TikTok profile snapshot (example)

{
  "handle": "acme",
  "followerCount": 89214,
  "followingCount": 105,
  "heartCount": 124019,
  "bioLink": "https://acme.example",
  "region": "US",
  "last_seen_at": "2025-10-18T09:00:00Z",
  "etl_version": "v1"
}

Rate Limiting & Reliability Principles

Concurrency caps per domain; exponential backoff on 4xx/5xx.

Three retries then dead-letter (DLQ); sample 1–2% of successes to S3 for audits.

Idempotent upserts by `username`/`handle`; version changes tagged with `etl_version`.

Implementation: Instagram (Practical Steps)

Step 1 — Inputs

Supply a username list (CSV/table). Batch them through SQS or scheduled triggers.

Step 2 — Fetch

Request https://www.instagram.com/{username}/ public page; parse visible JSON or structured HTML blocks.

Step 3 — Parse

Extract name, username, follower_count, following_count, bio, external_url, is_private.

Step 4 — Store

Upsert into DynamoDB (PK=username). Save raw page slices or JSON fragments to S3 for audits.

Step 5 — Refresh

Schedule via CloudWatch (e.g., daily or weekly) with jitter to avoid thundering herds.

Notes

Normalize counts; some pages hide or delay numbers. Fall back to cached values when needed.
Track latency and status in logs: target, status, duration_ms, error_code.
Respect regional CDNs; adjust timeouts and user agents to reduce transient failures.

Implementation: TikTok (Practical Steps)

Step 1 — Inputs

Provide @handle list such as https://www.tiktok.com/@{handle}.

Step 2 — Fetch

Visit the public profile page and parse visible JSON/structured segments (A/B or locale variants may differ).

Step 3 — Parse

Extract id, followerCount, followingCount, heartCount, bioLink, region.

Step 4 — Store

Same pattern as Instagram — structured in DynamoDB, raw fragments in S3.

Step 5 — Refresh

Scheduled updates plus event-driven rechecks; errors go to DLQ and alerting.

Notes

Handle field presence carefully; keep parsers tolerant to missing or renamed keys.
Debounce repeated requests for the same account; merge duplicates within short windows.
Alert when error rates exceed 2%; include sample payloads in notifications for fast triage.