Frankfurter + Bruin

Source

Ingest Frankfurter data into your warehouse with incremental loading, quality checks, and full lineage. Defined in YAML, version-controlled in Git.

For business teams

What you get

API data, on schedule
Frankfurter data lands in your warehouse automatically. No scripts to maintain, no pagination to handle.
Only fetch what changed
Incremental sync means no re-processing. Bruin tracks watermarks so you only get new and updated records.
Catch API changes early
Quality checks validate response data on every sync. Schema changes or missing fields get caught before they break models.
Transform in the same pipeline
Reshape Frankfurter API data with SQL or Python. Compute metrics, normalize schemas, and build models, all version-controlled.

For data & engineering teams

How it works

Managed pagination & retries
Bruin handles Frankfurter API pagination, rate limiting, and retries. You define the source, Bruin does the rest.
YAML-defined, Git-versioned
Your Frankfurter pipeline is a YAML file. Review in PRs, deploy with CI/CD, roll back with git revert.
Incremental with watermarks
Bruin tracks cursor positions and watermarks. Only new and updated Frankfurter records get fetched on each run.
Schema validation on responses
Quality checks validate Frankfurter API response structure on every sync. Catch breaking API changes early.

Before you start

None - public API

Step 1

Add your Frankfurter connection

No authentication required for public API. Add this to your Bruin environment file, credentials are stored securely and referenced by name in your pipeline YAML.

connections:
  frankfurter:
    type: frankfurter
    uri: "frankfurter://"

Step 2

Create your pipeline

Define a YAML asset that tells Bruin what to pull from Frankfurter and where to land it. This file lives in your Git repo, reviewable, version-controlled, and deployable with CI/CD.

Available tables

exchange_ratescurrencies

name: raw.frankfurter_exchange_rates
type: ingestr

parameters:
  source_connection: frankfurter
  source_table: 'exchange_rates'
  destination: bigquery

Step 3

Add quality checks

Add column-level and custom SQL checks to your Frankfurter data. If a check fails, the pipeline stops, bad data never reaches downstream models or dashboards.

Validate API data freshness on every sync

Ensure record IDs are unique across fetches

Catch missing fields from API response changes

columns:
  - name: id
    checks:
      - name: not_null
      - name: unique
  - name: fetched_at
    checks:
      - name: not_null

custom_checks:
  - name: API data is fresh
    query: |
      SELECT MAX(fetched_at) >
        TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 24 HOUR)
      FROM raw.frankfurter_exchange_rates

Step 4

Run it

One command. Bruin connects to Frankfurter, pulls data incrementally, runs your quality checks, and lands clean data in your warehouse. If a check fails, the pipeline stops, bad data never reaches downstream.

Backfill historical data with --start-date

Schedule with cron or trigger from CI/CD

Full lineage from Frankfurter to your dashboards

$ bruin run .

Running pipeline...

  frankfurter_exchange_rates
    ✓ Fetched 2,847 new records
    ✓ Quality: campaign_id not_null     PASSED
    ✓ Quality: spend not_null           PASSED
    ✓ Quality: no negative ad spend     PASSED
    ✓ Loaded into bigquery

  Completed in 12s

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Ready to connect Frankfurter?

Start for free, or book a demo to see how Bruin handles ingestion, quality, lineage, and scheduling for your entire data stack.

Book a demo Read the docs