Keep your pipelines healthy — catch data failures before they cascade
ETL failures mean stale dashboards, wrong reports, and decisions based on yesterday's data. Whether you use Python, dbt, Airflow, or raw SQL jobs, Cronping gives you instant visibility into every pipeline stage — and alerts you before stakeholders notice.
import requests
def run_etl():
extract_from_source()
transform()
load_to_warehouse()
# Ping Cronping on successful completion
requests.get(
"https://ping.cronping.com/YOUR_TOKEN",
timeout=5
)
run_etl()One line added to your script. Cronping handles the rest.
The cost of silent failures
Stale business dashboards
An ETL job failed at 3am. By 9am, executives are looking at yesterday's numbers — and they don't know it. Decisions are made on bad data.
Cascading pipeline failures
Upstream jobs fail silently and downstream jobs produce garbage results without raising any error. The problem compounds for hours before discovery.
Long-running jobs with no timeout
A slow query or network issue causes a job to run for 6 hours instead of 30 minutes. No alert, no timeout, no fallback. The pipeline is blocked.
Partial loads nobody notices
The job completed but only loaded half the data due to a memory limit or connection reset. Exit code 0. Everything looks fine. Nothing is fine.
Set up in under 2 minutes
- 1
Create a heartbeat per stage
Set up a heartbeat for each critical pipeline step with its expected schedule. Name them clearly: "nightly_orders", "hourly_inventory", "daily_reports".
- 2
Add the ping at completion
Call the Cronping URL at the end of each stage to confirm it completed. Use /start at the beginning to track run duration on long pipelines.
- 3
Get alerted on delays or failures
Cronping alerts your data engineering team via Slack, PagerDuty, or email when any stage misses its window — before the business impact cascades.
Everything you need
No SDK, no dashboard agent, no infrastructure to manage.
Per-stage monitoring
Monitor individual pipeline stages independently. Know precisely which step failed instead of guessing across a 20-step workflow.
Run duration tracking
Use /start and /finish to track how long each stage takes. Detect slowdowns before they cause missed windows.
90-day run history
Visual timeline of every pipeline run. Instantly see patterns, failures, and slowdowns across 3 months of execution history.
Instant team alerts
Route alerts to your data engineering Slack channel before analytics or business teams notice stale dashboards.
Unlimited heartbeats
Monitor every stage of every pipeline. Whether you have 5 jobs or 500, each gets its own heartbeat with independent alerting.
Zero infrastructure
No agents, no sidecars, no log aggregation pipelines to set up. One HTTP call is all it takes to monitor any ETL job.
Frequently asked questions
Wrap your dbt commands in a shell script and add the Cronping ping at the end: `dbt run --select my_model && curl -fsS https://ping.cronping.com/YOUR_TOKEN`. You can create a separate heartbeat for each critical model.
Yes. Add a final task to your DAG that calls the Cronping ping URL on success. For failure detection, add an on_failure_callback to your DAG that calls /fail.
Use /start when the stage begins and the base URL when it finishes. Set a generous grace period based on your expected duration. Cronping will alert you if the job doesn't finish within that window.
Yes — add validation logic after the load step. If the row count or checksum doesn't match expectations, call /fail explicitly instead of the success ping.
Any language that can make an HTTP GET request. Python (requests), Node.js (fetch/axios), Java, Go, Ruby, shell scripts — if it can call a URL, it works with Cronping.
Stop discovering failures when it's too late.
Free to start. No credit card required. Add your first heartbeat in under 5 minutes.