REST API → BigQuery (incremental)

This tutorial pulls records from a paginated REST API and streams them into a BigQuery table, then converts it to an incremental pipeline that only fetches new rows on each run.

Full-table version

version: 1
name: rest_to_bigquery

pipeline:
  source:
    type: rest
    config:
      base_url: https://api.example.com
      path: /v1/events
      method: GET
      name: events
      auth:
        type: basic
        config:
          username: ${env:API_USER}
          password: ${env:API_PASS}
      records_path: $.events[*]
      pagination:
        type: PageNumber
        param_name: page
        start_page: 1
        page_size: 500
        page_size_param: per_page
      max_pages: 200
      timeout: 45
      max_retries: 5
      retry_backoff: 2
      tolerated_http_errors: [404]
      replication_method:
        type: FullTable
      primary_keys: [event_id]
      schema_sample_size: 100

  sink:
    type: bigquery
    config:
      project_id: my-gcp-project
      dataset_id: analytics
      table_id: events
      auth:
        type: service_account_key_path
        config:
          path: service-account.json
      batch_size: 1000

Secrets come from the environment via ${env:VAR} — keep credentials out of the config file. Put them in a sibling .env or export them before running.

export API_USER=… API_PASS=…
faucet run rest_to_bigquery.yaml

The records_path is a JSONPath that selects the array of records inside each response body; pagination walks pages until an empty page or max_pages. See the pagination cookbook for the other styles.

Make it incremental

Switch replication_method from FullTable to a key-based incremental method and attach a state store so progress survives between runs:

pipeline:
  source:
    type: rest
    config:
      # … as above …
      replication_method:
        type: Incremental
        cursor_field: updated_at
      primary_keys: [event_id]
  sink:
    # … as above …
  state:
    type: file
    config:
      path: ./state

Now each run records the maximum updated_at it saw; the next run resumes from that bookmark. Swap the file state store for redis or postgres for shared, durable state across machines — see state.

Tip: run faucet schema source rest and faucet schema sink bigquery to see every available config field with its type and default.