Considerations when using APIs as data source

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Nice video overview of challenges associated with extracting data from APIs

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Using APIs as a data source is a standard part of modern data engineering, but they function very differently from direct database connections (JDBC/ODBC) or file extracts. APIs are designed for application integration, not bulk data transfer, which introduces specific constraints.

Here are the critical considerations for data engineers when building pipelines against APIs.

Ingestion Mechanics

The "plumbing" of connecting to an API is often the biggest hurdle. You cannot simply "SELECT *" like in a database.

• Rate Limiting (Throttling): Most public APIs limit the number of requests you can make per minute (e.g., 60 requests/min).

  • Challenge: Hitting a 429 Too Many Requests error will crash a naive pipeline.

  • Solution: Implement Exponential Backoff. When you hit a limit, your script should sleep for increasing durations (1s, 2s, 4s, 8s) before retrying.

  • Advanced: Use a "Token Bucket" algorithm to locally throttle your requests so you never hit the server's limit in the first place.

• Pagination: APIs rarely return all data in one response. They split it into pages.

  • Offset-based: (e.g., ?limit=100&offset=200) Simple, but performance degrades deep into the dataset (the "deep paging" problem).

  • Cursor-based: (e.g., ?after=cursor_id_123) Much more robust for reliable data extraction. Always prefer cursor-based if the API offers it, as it handles data that changes order while you are paginating.

Data Consistency & Integrity

Because APIs are stateless, ensuring you have a complete and accurate dataset is difficult.

• Incremental Loading (CDC): Extracting the full dataset every day is often impossible due to volume and rate limits. You need a strategy to fetch only new or changed records.

  • High Watermark: Track the updated_at timestamp of the last record you ingested and request ?updated_after={last_run_time}.

  • Hard Deletes: APIs rarely tell you when a record is deleted. If you only pull incremental updates, your warehouse will retain "zombie" records that no longer exist in the source. You may need periodic full reloads or a "soft delete" inference strategy (if a record isn't seen in a full list fetch, mark it as deleted).

• Idempotency: Your pipeline will fail halfway through. If you restart it, will you create duplicates?

  • Best Practice: Design your loading layer to be idempotent. Use "Upsert" (Update/Insert) logic based on the record's Unique ID (primary key), so re-processing the same JSON object updates the existing row rather than creating a duplicate.

Data Structure & Schema Drift

APIs return semi-structured data (usually JSON), which doesn't fit neatly into the rigid rows and columns of a Data Warehouse.

• Nested Structures: A single API response might contain a Customer object with a nested list of Orders and Addresses.

  • Consideration: Do you flatten this into one wide table (denormalization) or explode it into three separate tables (Customer, Orders, Address)?

  • Inmon/Kimball Note: If you follow Inmon, you might normalize this into 3NF in the warehouse. If Kimball, you might flatten it into a dimension table. A common pattern is to dump the raw JSON into a "Variant" column (in Snowflake) or a Staging table first, then parse it downstream.

• Schema Evolution: APIs change silently. A field might change from an Integer to a String, or a new field might appear.

  • Risk: A rigid schema definition will cause the pipeline to fail on type mismatches.

  • Mitigation: Use "Schema-on-Read" where possible, or implement a validation layer (like Pydantic or Great Expectations) that alerts you to schema changes without crashing the critical path.

Security & Authentication

• Secret Management: Never hardcode API keys or Bearer tokens in your code. Use environment variables or a secrets manager (like AWS Secrets Manager or HashiCorp Vault).

• Token Rotation: Many modern APIs (like Salesforce or Google) use OAuth2, which requires exchanging a refresh token for a short-lived access token. Your pipeline must be able to handle this "handshake" automatically before requesting data.

Summary Checklist for Engineers

Consideration	Warning Sign to Watch For	Mitigation Strategy
Volume	"We have 10 million records."	Do not use Offset pagination. Use Cursors or request bulk export files instead of REST.
Latency	"We need this data every 5 minutes."	Check API rate limits closely. Polling too fast is the #1 cause of IP bans.
Reliability	"The API is flaky / 500 errors."	Implement "Jitter" (randomized delays) in your retry logic to avoid hammering the server during recovery.
Deletes	"Why is this deleted user still in our dashboard?"	Incremental loads miss deletes. Schedule a monthly "Full Reload" to sync deletions.

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