For teams
Data Observability for Data Engineers and Data Engineering Observability Tools That Catch Breaks First
Built for the engineers on the hook for data reliability: auto-monitors, lineage, and grouped alerts that find the break before the Slack message does.
14-day trial, no credit card, read-only connection
Alerted #data-eng 0.8s ago.
Downstream impact · consumers at risk
In one paragraph
Data observability for data engineers is monitoring that watches every production table for late loads, row count drops, schema changes and value anomalies without a hand-written test per table. DataObservability connects read-only, generates monitors from your warehouse and dbt project, puts the upstream tables on each incident and routes it to Slack or PagerDuty. Prices are published: 59 dollars a month for 50 tables, 179 for 250 and 479 for 1,500, billed yearly, with a 14-day trial and no card.
Side by side
Data engineering observability tools compared by upkeep and price
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| Option | What the on-call engineer gets | What you maintain | Published price |
|---|---|---|---|
| dbt tests plus orchestrator alerts | Failures on the invariants you wrote and on jobs that crash | Every test, by hand. A job that succeeds with zero rows stays silent | Included with dbt and Airflow |
| Soda or Great Expectations Core | Checks as code, versioned in Git and reviewed in pull requests | Scheduling, result storage and alert wiring | Soda Team lists at 750 dollars a month. GX Core is free under Apache 2.0 |
| Datafold | Data diffs on pull requests, before a change merges | Nothing in production, because it does not watch tables after deploy | No public price. Its AWS listing works out to 3,000 dollars per developer seat a year |
| Datadog Quality Monitoring | Table monitors inside the Datadog account you already page from | Per-table enablement and a second meter on the Datadog bill | 16 dollars per monitored table a month billed annually, 48,000 dollars a year for 250 tables |
| Monte Carlo or Bigeye | Broad enterprise coverage and deep lineage | A sales cycle, a security review and an annual contract | AWS listings of 50,000 and from 45,000 dollars a year |
| DataObservability | Monitors generated for every table, upstream tables on the incident, Slack and PagerDuty routing | A read-only connection. No agent and no test files | 59, 179 or 479 dollars a month billed yearly |
Summarized in good faith from public documentation, August 2026. Verify current behavior and terms with the vendor.
Why it fits
Data and platform engineers responsible for keeping production data trustworthy.
Less manual test-writing
Auto-monitors cover every table, including the breaks you would not have tested for.
Faster root cause
Lineage and recent-change context cut investigation from hours to minutes.
On-call you can live with
Grouped, routed alerts mean fewer pages and clearer ownership.
The breaks a data engineer gets paged for are the ones nobody wrote a test for
Look at the last few incidents that reached a stakeholder before they reached you. They are rarely the null check you already had. A source system changed an enum and a filter quietly dropped a third of the rows. A vendor file arrived empty and the load succeeded. A column switched from cents to dollars. A job ran on time against a table that had not been refreshed upstream. Each of these passes dbt tests and leaves the orchestrator green, because nothing failed in the sense a scheduler understands. Monitoring that learns each table's normal load time, row count, schema and value distribution from its own history catches this class of break without anyone predicting it first. That is the gap this product fills, and it is why it sits next to your tests and does not try to replace them.
What you maintain after setup
The cost of a monitoring tool that engineers feel is upkeep. A checks-as-code framework is free to license and expensive to own: someone schedules the runs, stores the results, retunes thresholds after every seasonal swing and adds a file for each new model. On a 250 table warehouse that is a standing part-time job. DataObservability takes the other route. It reads the catalog through a read-only connection, picks up new tables on its own and builds a baseline per table, so a table that loads hourly on weekdays and skips Sunday does not fire on Sunday. Checks run every hour on all five pillars. When a check passes again the incident closes itself, which keeps the queue honest without a cleanup ritual. Your tests stay where they are, covering the business rules only you know.
Root cause starts with the upstream tables on the alert
An alert that says orders_daily is stale sends you into query history. An alert that names the tables feeding it sends you toward the cause. Every incident here carries its upstream tables in the Slack or email message, with end-to-end lineage in the workspace. On the Scale plan lineage goes down to the column, read from your warehouse catalog, which matters when one column out of sixty changed type and you need the list of tables that select it. Team and Scale add dbt-native monitors generated from your project, so coverage follows the models you ship. Related failures on the same table collapse into one incident, so a broken load pages once and the responder is not acknowledging six alerts for one cause.
Which plan fits a data engineering team
Starter at 59 dollars a month billed yearly (119 month to month) watches 50 tables on one warehouse with 7 days of history, which suits a single critical schema. Team at 179 (359 month to month) covers 250 tables, adds dbt-native monitors and PagerDuty, and keeps 90 days of history, enough to see weekly and monthly seasonality. Scale at 479 (959 month to month) covers 1,500 tables across up to 5 warehouses and adds column-level lineage, a workspace audit log and 365 days of history. For comparison, 250 tables on a per-table meter at 16 dollars come to 48,000 dollars a year, and the enterprise contracts listed on AWS Marketplace start around 45,000 to 50,000. Every plan starts with a 14-day trial that needs no card.
Frequently asked questions
What is the best data observability tool for data engineers?
The best fit depends on what breaks. For silent production failures (late loads, row count drops, schema drift) on a cloud warehouse, a monitoring platform that generates checks per table is the right class of tool, and DataObservability does that from 59 dollars a month with a read-only connection. For regressions introduced by code changes, Datafold diffs data at pull request time. For rules you want reviewed as code, Soda and Great Expectations are the usual picks.
Do data engineers still need data observability if they already run dbt tests?
Yes, for the failures tests do not model. dbt tests check the invariants someone wrote and run when the job runs. They do not notice a table that stopped loading, a row count that halved inside accepted bounds, or a column nobody tested. Observability adds learned baselines and history per table, and runs on its own schedule. Most teams keep both: tests for business rules, monitoring for everything else.
How much does data observability cost for a data engineering team?
DataObservability costs 59 dollars a month for 50 tables, 179 for 250 tables and 479 for 1,500 tables when billed yearly, or 119, 359 and 959 month to month. Datadog lists 16 dollars per monitored table a month on an annual commitment, which is 48,000 dollars a year for 250 tables. Monte Carlo and Bigeye list 50,000 and from 45,000 dollars a year on AWS Marketplace.
How long does it take a data engineer to set up data observability?
Setup is one read-only warehouse user and a connection form. There is no agent to install and no per-table configuration to write, because tables are picked up from the catalog automatically and checked every hour. Freshness, volume and schema checks are useful from the first runs. Anomaly baselines sharpen as history builds up over the first days and weeks.
Can data observability page the on-call data engineer through PagerDuty?
Yes. Slack and email alerts are on every plan, and PagerDuty routing is included on Team, Scale and Enterprise. Each incident opens once, carries the upstream tables of the affected table, and closes on its own when the check passes again, so the pager reflects what is broken right now.
Does the tool need write access to my warehouse?
No. The connection is read-only and works from metadata: catalog tables, row counts and schema. The two exceptions are a single maximum-timestamp query per table to measure freshness and one sample used for the null profile. Credentials are stored encrypted, and nothing is written to your warehouse.
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