What is API-based Data Ingestion?

The Core Problem: You need data from Salesforce, Shopify, Stripe, or internal microservices for analytics or machine learning. But you cannot connect directly to their databases. You cannot get nightly file dumps. The only interface you have is an API contract, maybe REST or GraphQL, with authentication, rate limits, and pagination. This is the reality for most SaaS integrations. Tools like Fivetran, Airbyte, and Stitch exist specifically because hundreds of platforms only expose data through APIs. Three Core Pillars: First is extraction. You make HTTP requests with proper authentication tokens. The API returns data in pages, maybe 100 or 1000 records at a time. You must handle pagination cursors, respect rate limits like 200 requests per minute, and deal with eventual consistency where recently created records might not appear immediately. Second is staging and transformation. Raw API responses, typically JSON, need validation. Field names might change. New fields appear. You land this raw data first, then normalize it into typed schemas that your warehouse or search index expects. Third is orchestration and resilience. You schedule sync jobs, maybe every 15 minutes or hourly. You track checkpoints so you only fetch changed data. When requests fail due to timeouts or rate limits, you retry with exponential backoff. You monitor freshness, ensuring data is no more than 10 minutes stale. Why This Matters: A large company might ingest from 20 to 100 external tools plus hundreds of internal services. Each has different limits and schemas. Your ingestion layer sits between these APIs and your data lake, which might receive tens of terabytes daily. Getting this wrong means broken dashboards, stale metrics, or exceeding API quotas and getting blocked.