A research pipeline needs more than a start time. If the next task calculates a signal from an old observation, a successful DAG can still produce the wrong research input. The useful dependency is the arrival of a qualifying record, followed by a deliberate handoff to the calculation that consumes it.
This guide builds that handoff around US consumer-price inflation. The same provider accepts other supported currency and indicator identifiers. The workflow is intended for data engineers maintaining research datasets and recurring reports.
- Prepare: record the last successfully processed announcement timestamp and reference period.
- Wait: require a returned observation to pass both cursor checks.
- Process: consume the sensor's XCom result, then advance the saved cursor after downstream work succeeds.
1. Prepare the provider and connection
You need an Airflow environment compatible with the provider, the FXMacroData provider package, and a subscription API key for protected data. The provider's package metadata declares Apache Airflow 2.7 or later; verify compatibility with your deployment's constraints before upgrading an existing environment. Its source and complete example DAG are available in the FXMacroData provider repository.
Create an Airflow connection with ID fxmacrodata_default, connection type FXMacroData, and the API key in its password field. The hook supplies that value in the X-API-Key request header. Keep credentials in your Airflow connection store or configured secrets backend, rather than in DAG source.
| Component | Use in a macro workflow |
|---|---|
FXMacroDataReleaseSensor | Wait for an announcement record that passes a timestamp and optional period check. |
FXMacroDataCalendarOperator | Read confirmed upcoming schedule records for planning. |
FXMacroDataSnapshotOperator | Retrieve the latest indicator snapshot for an economy. |
FXMacroDataHook and FXMacroDataOperator | Make an explicit request from a task when a specialist operator is unnecessary. |
2. Define the release you are waiting for
The sensor accepts two useful constraints. after_announcement_datetime requires a strictly newer stored announcement timestamp. newer_than_period requires a later reference-period date. Together they help distinguish a new-period observation from a revision to an older period.
Initialize the two Airflow variables below from the last record your pipeline successfully processed. They are application cursors, not guessed future publication times. An explicit timestamp is especially useful with rescheduling: each execution can reconstruct the same waiting condition from durable state.
from airflow.models import Variable
from fxmacrodata_provider.sensors.release import FXMacroDataReleaseSensor
wait_for_cpi = FXMacroDataReleaseSensor(
task_id="wait_for_cpi",
currency="USD",
indicator="inflation",
conn_id="fxmacrodata_default",
after_announcement_datetime=int(
Variable.get("fxmd_usd_cpi_last_timestamp")
),
newer_than_period=Variable.get("fxmd_usd_cpi_last_period"),
poke_interval=60,
timeout=6 * 60 * 60,
mode="reschedule",
)Place this operator inside your DAG definition. The interval and timeout are examples to tune to your own workflow and request allowance. Public keyless USD access is delayed by 15 minutes and limited to 100 requests per day; frequent unattended polling should use an appropriately licensed subscription.
3. Consume the record that passed the check
On success the sensor pushes the accepted observation to XCom. Pass that output to the downstream task instead of issuing another latest-value request, which could select a different record if an update arrives between tasks.
from airflow.decorators import task
@task
def build_research_input(release: dict) -> dict:
return {
"reference_period": release["date"],
"value": release["val"],
"announcement_timestamp": release["announcement_datetime"],
}
research_input = build_research_input(wait_for_cpi.output)
wait_for_cpi >> research_inputThe result is a small research input tied to one returned observation. Add your own report, model-feature calculation, or warehouse write after this step. Advance the saved timestamp and period only after that work completes successfully, and make the downstream write idempotent so a retry cannot produce duplicate results.
4. Choose the right waiting policy
| Situation | Appropriate response |
|---|---|
| A newer timestamp belongs to the same reference period. | Keep waiting when the job specifically requires a later period; handle revisions in a separate workflow. |
| No row is returned. | The sensor continues waiting until its configured timeout. |
| The row lacks an integer announcement timestamp. | The current sensor raises an error rather than inventing a comparison value. |
| You need every intermediate revision. | Use a revision-aware extraction workflow; a latest-row polling sensor alone does not establish exhaustive revision capture. |
The provider evaluates stored announcement fields. Its success is not a measurement of when the original publisher first made the figure available. Keep those concepts separate when evaluating a research pipeline.
5. Extend a working pipeline
Use the release calendar to plan work around confirmed events, while retaining a record-based gate for downstream processing. Add the provider's calendar or snapshot operators when the report needs broader economic context. A human analyst can inspect the result through an MCP-connected assistant; Airflow itself owns the scheduled extraction and retry workflow through the provider's REST requests.
Test the old-record, same-period revision, missing-timestamp, successful-new-period, and timeout cases before relying on the DAG. A useful operational result is straightforward: each successful run records which observation it processed, and a failed or incomplete run leaves its last-success cursor intact.
Measure the handoff, not just the DAG status
A release-dependent report has two clocks: when the job runs and which observation the calculation consumes. The sensor connects them by making the accepted record the input to the next task. That is more useful than a green task followed by an independent latest-value request, because the independent request can change the evidence after the waiting decision has already been made.
For a policy or inflation research report, record the accepted series and reference period beside the derived result. Compare successive reports by those identifiers before interpreting a difference as an economic change. If a rerun receives the same qualifying observation, the report should be reproducible; if it receives a revision, the report should make that change intelligible. This turns scheduling from a convenience into an accountable research handoff.
Sources and further reading
- FXMacroData Airflow provider: source, components, and example DAG
- Apache Airflow: managing connections
- Apache Airflow third-party ecosystem directory
- FXMacroData API reference
AI was used for research and editing.
AI was used for research, creating images and editing.
