Platform observabilityAstro Observe

Data quality

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Astro Observe data quality helps you monitor tables to ensure data accuracy, completeness, and integrity across your pipelines. It automatically tracks key metrics such as column null percentages, schema changes, and table row counts to detect anomalies or unexpected shifts in your data.

Configure permissions

Before connecting to Astro Observe, configure the necessary permissions for your data platform.

For Snowflake connections, the Observe role must have access to both the ACCOUNT_USAGE and INFORMATION_SCHEMA system tables. The service user must have a default warehouse configured to support discovery and ongoing data quality monitoring.

1

Log into Snowflake

Log into Snowflake using a high-privilege role such as ACCOUNTADMIN.

2

Create a dedicated role for Observe

Create Observe role
1CREATE ROLE IF NOT EXISTS ASTRO_OBSERVE_ROLE;
3

Create a read-only service user

Create a service user that Observe will use.

Create Observe service user
1CREATE USER IF NOT EXISTS ASTRO_OBSERVE_USER
2  DEFAULT_ROLE = ASTRO_OBSERVE_ROLE
3  TYPE = SERVICE;
4

Assign the role to the user

Grant role to user
1GRANT ROLE ASTRO_OBSERVE_ROLE TO USER ASTRO_OBSERVE_USER;
5

Grant the role the privileges Observe requires.

Replace YOUR_DB and other example names to match your Snowflake environment.

Example privileges for ASTRO_OBSERVE_ROLE
1-- Grant warehouse access (replace COMPUTE_WH with your warehouse)
2GRANT USAGE ON WAREHOUSE "COMPUTE_WH" TO ROLE ASTRO_OBSERVE_ROLE;
3ALTER USER ASTRO_OBSERVE_USER SET DEFAULT_WAREHOUSE = 'COMPUTE_WH';
4
5-- Metadata and object access:
6GRANT USAGE, MONITOR ON DATABASE YOUR_DB TO ROLE ASTRO_OBSERVE_ROLE;
7GRANT USAGE, MONITOR ON ALL SCHEMAS IN DATABASE YOUR_DB TO ROLE ASTRO_OBSERVE_ROLE;
8
9-- Read access:
10GRANT SELECT ON ALL TABLES IN DATABASE YOUR_DB TO ROLE ASTRO_OBSERVE_ROLE;
11GRANT SELECT ON ALL VIEWS IN DATABASE YOUR_DB TO ROLE ASTRO_OBSERVE_ROLE;
12GRANT SELECT ON ALL EXTERNAL TABLES IN DATABASE YOUR_DB TO ROLE ASTRO_OBSERVE_ROLE;
13
14-- Optional: access to Snowflake usage views:
15GRANT SELECT ON SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY TO ROLE ASTRO_OBSERVE_ROLE;
16GRANT SELECT ON SNOWFLAKE.ACCOUNT_USAGE.TABLE_DML_HISTORY TO ROLE ASTRO_OBSERVE_ROLE;
17
18-- Or use:
19-- GRANT IMPORTED PRIVILEGES ON DATABASE SNOWFLAKE TO ROLE ASTRO_OBSERVE_ROLE;
These are example grants. Replace database, schema, and warehouse names with values appropriate for your account and security policies.

Astronomer recommends key-pair authentication for Snowflake service users. Generate an RSA key pair, then assign the public key to the Observe service user to enable secure authentication.

1

Generate a password-protected private key and a public key

Run the following commands on a secure host to create an encrypted private key and a public key:

Generate RSA key pair (example)
$# Create encrypted private key
$openssl genrsa 2048 | openssl pkcs8 -topk8 -v2 des3 -inform PEM -out rsa_key.p8
$
$# Create public key
$openssl rsa -in rsa_key.p8 -pubout -out rsa_key.pub
2

Validate the key

$openssl pkey -in rsa_key.p8 -check -noout
3

Assign the public key to the Snowflake user

Remove the -----BEGIN PUBLIC KEY----- and -----END PUBLIC KEY----- headers and newlines so the key is a compact single string. Assign the cleaned public key to the service user:

Assign public key to Snowflake user
1ALTER USER ASTRO_OBSERVE_USER SET RSA_PUBLIC_KEY = '<your_cleaned_public_key>';

All Snowflake integrations require that the Observe role has access to both ACCOUNT_USAGE and INFORMATION_SCHEMA system tables. The service user must have a default warehouse configured for all discovery and monitoring operations.

Set up a connection

After you configure permissions for your data platform, create an Observe connection.

1

Open Connections

In the Observe UI, navigate to Data Quality → Connections and click + Connection.

2

Fill in connection details

Complete the following fields:

  • Name: A name for the connection.
  • Description: Optional description.
  • Connection Type: Select Snowflake.
  • Polling Schedule: How frequently Observe polls Snowflake for metrics (examples: every 1 hour, 6 hours, 1 day). Polling frequency is the maximum rate at which Observe updates data quality metrics and monitors; more frequent polling may increase Snowflake compute costs.
  • Account Identifier: Your Snowflake account identifier (for example, FY02423-GP2141). Observe maps assets to a connection by account identifier.
  • Username: The Snowflake service user (ASTRO_OBSERVE_USER).
  • Private Key: Paste your private key for key-pair authentication if using key-pair auth.

Only one Observe connection is allowed per Snowflake account identifier. If you have multiple Snowflake accounts, create a separate connection for each account identifier.

3

Save and start discovery

Click Create. Observe begins the metadata extraction process and will discover your data assets and surface discovered tables in the Asset Catalog.

Asset Catalog

Navigate to Asset Catalog, filter by your data platform (for example, Snowflake tables or Databricks tables), and select the desired table.

You can sort tables by popularity to quickly identify frequently used tables. Popularity rankings are based on query frequency and the number of unique users accessing each table.

Schema

The Schema tab shows table structure details:

  • Column names
  • Data types
  • Completeness status
  • Nullability
  • Default values

You can enable monitoring for specific columns to actively track completeness.

Event Timeline

The Event Timeline tab shows data quality events for a selected timeframe. Events are color-coded by severity: Success, Neutral, and Failure. Click an event to view details, historical patterns, and affected metrics.

Data quality

The data quality tab provides visualizations for monitored metrics:

  • Table Volume: track changes in row counts and percent change over time to identify unexpected fluctuations.
  • Completeness: visualize column null percentages against thresholds to surface completeness problems.

Monitors

The Monitors tab lists all configured data quality monitors (Column Null Percentage, Table Schema Change, Row Volume Change). Each monitor’s schedule and modification history are shown for management. To learn how to create data quality monitors, see Data quality monitors.

Manage triggered monitors

To see a high-level overview of your organization’s data quality, click Data Quality in the navigation. Here you can see a summary of triggered data quality monitors from the last week or month, grouped by severity and check type.

Click any triggered monitor to investigate it and see the underlying data that triggered the monitor’s conditions.

Data quality issues overview dashboard