AWS DATA OBSERVABILITY
AWS Data Observability for Redshift With Native and Third Party Options Priced
AWS has no single data observability service. It has parts: Glue Data Quality for rules and anomaly detection, CloudWatch for cluster health, EventBridge for routing, and system views inside Redshift for raw evidence. This page shows what each part watches, what it costs on a 250 table estate, and where a flat monitoring plan covers the gaps.
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What is the best data observability option on AWS?
For tables in Amazon Redshift, the choice is between assembling AWS native services or connecting a monitoring platform. Native means AWS Glue Data Quality at 0.44 dollars per DPU-hour plus CloudWatch and EventBridge, with a ruleset written per table. A platform means DataObservability from 59 dollars a month for 50 tables, 179 for 250 and 479 for 1,500, with freshness, volume, schema and distribution baselines learned on every table. Enterprise vendors list on AWS Marketplace from 45,000 dollars a year.
Side by side
AWS data observability compared
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| Route | What it watches | Cost on 250 Redshift tables | What you still build |
|---|---|---|---|
| AWS Glue Data Quality rulesets | Assertions you write in DQDL, plus a quality score per run | About 608 USD a month at 0.081 USD per run, checked daily | A cataloged table and a ruleset per table, kept in step with schema changes |
| Glue Data Quality anomaly detection | Outliers in statistics from the columns you nominate | Adds 1 DPU per statistic at 0.44 USD per DPU-hour | Analyzers per column, scheduled runs, feedback on each observation |
| Amazon CloudWatch | Cluster health such as CPU, connections and query duration | Metric and alarm charges per CloudWatch pricing | Alarms per metric. It does not read the data itself |
| Redshift system views | Row estimates in SVV_TABLE_INFO, query history in SYS_QUERY_HISTORY | No extra charge, runs on your cluster | The queries, schedule, baselines, thresholds and alert routing |
| Monte Carlo on AWS Marketplace | Learned monitors and lineage across tools | About 50,000 USD a year | Contract, procurement, onboarding |
| Bigeye on AWS Marketplace | Column level autometrics | About 45,000 USD a year for 100 tables | Contract and per column SLAs |
| DataObservability Team | Freshness, volume, schema and distribution on every table, with lineage and incidents | 179 USD a month billed yearly | A read only Redshift user, nothing per table |
| DataObservability Starter | The same monitors on 50 tables | 59 USD a month billed yearly | A read only Redshift user |
| DataObservability Scale | Up to 1,500 tables, column level lineage, SSO | 479 USD a month billed yearly | A read only Redshift user |
Positioning and pricing models are summarized in good faith from each vendor's public pages, November 2026. Verify current terms with the vendor.
What you get
What each AWS data observability route covers
Native tools check what somebody wrote down
Glue Data Quality evaluates the rules in a ruleset, and a ruleset exists only for a table someone cataloged and wrote it for. The failures that reach a finance dashboard are usually the ones nobody asserted: a loader that stopped on Saturday, a column that changed type, a table that kept loading half the usual rows. A learned baseline per table catches those without a rule.
Cost stays flat while the estate grows
Glue bills compute for every run, so more tables and more frequent checks mean a bigger invoice. At 0.081 dollars a run, a daily check on 250 tables is about 608 dollars a month and an hourly check is roughly 24 times that. A flat plan prices the table count, not the number of checks.
Incidents instead of a pile of CloudWatch alarms
CloudWatch and EventBridge deliver events. Getting an event to the engineer who owns the dataset needs rules, a topic and a chat integration built and kept current. DataObservability groups related failures into one incident and routes it to Slack, or PagerDuty on Team and above, with the downstream tables and dashboards named.
A read only connection, no agent in your VPC
Setup is one Redshift user with SELECT on the tables you want watched and access to the system views. Nothing is deployed in your account, there is no crawler to keep in sync and the first baselines build from catalog metadata and load history before any row scan is needed.
How it works
From connected to caught
List the Redshift tables that feed reporting
Start with the tables behind finance, product and customer dashboards. That count decides which plan fits and what a per run bill on Glue would look like.
Price the native route on your own count
Multiply tables by runs per day by 0.081 dollars for AWS's own example of 10 rules and 10 analyzers, then add the time to write and maintain each ruleset. That is the real native figure.
Connect Redshift read only and let baselines build
Create a user with SELECT on the target schemas, connect it, and give the monitors a few days of load history so weekly patterns are learned before thresholds matter.
Keep DQDL for invariants and monitor the rest
Rules still win for exact business facts such as a discount that never exceeds 40 percent. Let the monitors cover freshness, volume, schema and distribution everywhere else, and start the 14 day trial on the same tables to compare what each caught.
Why AWS has no single data observability service
AWS sells building blocks. Glue Data Quality evaluates rules and, since July 2026, can run anomaly detection on cataloged tables. CloudWatch watches infrastructure. EventBridge routes events. Redshift system views expose raw facts such as row estimates and query history. None of them owns the full job of learning what normal looks like for every table, noticing a deviation, grouping it into an incident and telling you what sits downstream. Teams that stay fully native end up writing that glue themselves, and the glue becomes a system someone has to maintain.
What the native route costs when you do the arithmetic
AWS prices its own Glue Data Quality example, 10 rules and 10 analyzers on one table, at about 0.081 dollars a run. Daily on 250 tables that is roughly 608 dollars a month. Hourly it is roughly 14,580. Neither number includes the engineering time to write rules, review them when schemas change, and tune thresholds when alerts turn noisy. Our full worked breakdown is on the AWS Glue Data Quality pricing page, and it includes the point where the native route is the cheaper one.
Where enterprise platforms on AWS Marketplace fit
Monte Carlo, Bigeye and Sifflet all list on AWS Marketplace, with published contract values around 50,000, 45,000 and 48,000 dollars a year as of September 2026. The benefit is that the spend can draw down an existing AWS commitment, which procurement likes. The cost is a five figure floor, a sales cycle and a rollout built for thousands of tables. For a team of 50 to 1,500 tables, a plan that starts at 59 dollars a month answers the same monitoring question without a contract.
Which AWS data sources we connect to today
DataObservability connects read only to Amazon Redshift, alongside Snowflake, BigQuery and Databricks, and reads dbt manifest files on Team and above. Files in S3 and tables queried through Athena are not connected directly, so for a lake that is mostly S3 you will want Glue Data Quality or Deequ close to the pipeline and a monitor on the Redshift tables where the data lands for analysts.
What a first week looks like on Redshift
Day one, connect the read only user and let inventory build from the catalog. Days two to four, baselines form for freshness and volume on each table, and schema changes begin to be tracked. By day five, weekly patterns are visible, so a table that is quiet on Sundays stops looking broken. The first incidents are usually dull and valuable: a loader that is late, a table whose row count dropped by a third, a column retyped upstream.
Questions buyers ask
AWS data observability FAQ
What is data observability on AWS?
It is continuous monitoring of the freshness, volume, schema and distribution of the tables in your AWS data stack, with alerts and lineage. AWS provides parts of it through Glue Data Quality, CloudWatch and EventBridge, and third party platforms add learned baselines and incident grouping on Redshift.
Does AWS have a native data observability tool?
Not as one product. AWS Glue Data Quality covers rules and anomaly detection on cataloged tables, CloudWatch covers cluster health, and Redshift system views expose raw metadata. You assemble them yourself, and each rule or analyzer is something your team writes and maintains.
How much does AWS Glue Data Quality cost?
AWS lists 0.44 dollars per DPU-hour. Its own example of 10 rules and 10 analyzers on a table costs about 0.081 dollars a run, so daily checks on 250 tables come to roughly 608 dollars a month before anomaly detection and engineering time.
Can I monitor Amazon Redshift without writing rules?
Yes. DataObservability connects read only to Redshift and generates freshness, volume, schema and distribution monitors on every table from catalog metadata and load history. No ruleset or Glue crawler is needed, and plans start at 59 dollars a month.
Is there a cheaper alternative to Monte Carlo on AWS?
Yes, if your tables are in Redshift, Snowflake, BigQuery or Databricks. Monte Carlo lists around 50,000 dollars a year on AWS Marketplace. DataObservability Team covers 250 tables for 179 dollars a month billed yearly, and a 14 day trial needs no card.
Does DataObservability monitor S3 or Athena?
Not directly today. It connects to Redshift, Snowflake, BigQuery and Databricks. For S3 data, use Glue Data Quality or Deequ at the pipeline and monitor the Redshift tables downstream where analysts query the result.
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