How to Build a Schema Drift Alert System in Snowflake

Introduction

As businesses scale, their data systems must evolve, often introducing new fields to support product features or enhanced reporting. Simultaneously, upstream vendors or data providers may modify schemas independently, without coordinating with internal development teams. Without clear communication or version control, environments fall out of sync.

Consider a retail company that tracks sales performance using dashboards powered by an automated data pipeline. One day, an engineer removes the promo_code column from a source table, assuming it’s obsolete. 

The result? Pipelines continue to run without raising errors, but the data they produce becomes unreliable. Dashboards break silently, metrics appear incorrect, and teams waste time chasing false alerts or debugging misaligned reports.

This is schema drift in action: structural changes that silently break downstream processes and erode trust in data without leaving an obvious trace.

Understanding Schema Drift in Practice

Schema drift occurs when the live schema (actual structure) diverges from the expected schema (defined or baseline structure). These changes may include:

Here are common types of schema drift:

• Added Columns: A new column, like session_duration, is introduced to a table without informing downstream users, causing joins or transformations to fail unexpectedly.
• Dropped Columns: Fields such as phone_number are removed, but are still expected by pipelines, leading to null values or failed validations.
• Renamed Columns: Changing user_id to customer_id breaks queries and dashboards that depend on the original column name.
• Data Type Changes: Modifying signup_date from DATE to STRING can disrupt filters, aggregations, and time-based logic in reports.

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Changes can happen unexpectedly, whether from human error or software bugs, or misconfigured deployment scripts. Without a reliable source of truth to compare against, these changes often go unnoticed until something breaks. If schema drift is not detected and handled, data pipelines become vulnerable to upstream changes, leading to data quality issues, broken transformations, and unreliable analytics.

This lag is why schema validation needs to shift left, meaning it should happen earlier in the data lifecycle, not after pipelines are already in production. Catching schema changes at the point of ingestion or during development helps prevent silent failures and ensures data quality before it affects downstream users.   By maintaining a baseline schema, defined and version-controlled, you can catch deviations early, ensure consistency across environments, and reduce the risk of downstream data issues.

Schema Drift vs. Change Data Capture (CDC)

Change Data Capture (CDC) is a powerful method for tracking row-level changes such as inserts, updates, and deletes. It works by reading from a database’s transaction logs, allowing for near real-time replication with minimal overhead. CDC is crucial for keeping systems in sync without needing to scan entire tables.

However, CDC does not monitor structural changes to tables. It won’t detect if a column is added, renamed, or dropped. CDC pipelines may silently skip new or removed fields, leading to incomplete replication or downstream errors. This is why schema drift detection must be paired with CDC: while CDC captures what changed at the data level, schema drift tracking captures how the table structure itself is evolving.

Pairing the two is essential for a reliable data platform. Schema drift detection provides validation checkpoints to ensure that structural changes don’t go unnoticed. It also helps prevent CDC jobs from silently failing or propagating incomplete data. Additionally, integrating schema drift detection with metadata management or data governance platforms ensures schema consistency across the stack.

Components of a Schema Drift Alert System

There are several key components required to build a robust schema drift alert system:

Schema Snapshot Capture

Schema Snapshot Capture is the process of recording the exact structure of a database table, including its column names, data types, and optionally, metadata such as nullability, defaults, or comments,  at a specific time.

This snapshot acts as a baseline or “source of truth” for detecting schema drift, which refers to unintended or untracked changes in data structure over time.

Most modern data platforms offer built-in ways to retrieve schema metadata. For example, Snowflake exposes this information through INFORMATION_SCHEMA views, while dbt allows developers to version and test schema expectations through schema.yml files and data tests. 

Change Categorization (Breaking vs Non-Breaking)

It is important to categorize the changes in the schema, so the alert system can inform the end users or data consumers that some changes directly impact them. 

In an append schema drift, a new schema and/or attribute is added to the table, which generally occurs when a client upgrades a data product to a newer version, and this generally does not cause disruptions to the end users.  Appends to the schema are non-breaking for consumers, but it is also advisable to pre-release such changes to avoid any changes in production without the consumer’s prior knowledge.

On the other hand, if there is a downstream schema change where a vendor alters, renames or deletes an existing field from the table, such as renaming the customer_id column to user_id in a central table without communicating to the analytics team, the BI reports, ML methods, and ETL jobs are simultaneously affected leading to outages for shared consumers. 

Alerting Mechanism

To handle these scenarios effectively, schema drift alert systems should automatically tag schema changes detected via snapshots with a change_impact field (e.g., breaking or non-breaking). Alerts can then be routed using real-time Slack notifications (e.g., webhooks) or simple email notifications to Snowflake when schema drift is detected. 

Alert Lifecycle Management (Open/Closed)

It's also important to track ownership, identifying whether affected columns are used by upstream systems or downstream consumers.  Once an alert is triggered, it should enter a lifecycle that helps your team track its status—whether it’s being investigated, has been resolved, or should no longer generate repeat notifications.

Alerts should follow a lifecycle:

• OPEN: New drift detected
• CLOSED: Drift resolved (e.g., column added back)

Use SQL logic to auto-close alerts when a column is re-added or schema is restored. 

Understand Downstream Impact

To effectively detect schema changes, teams must actively track data lineage to understand how data flows and transforms across systems. In addition, performing regular data profiling helps ensure data quality and highlights unexpected structural changes. Integrating data lineage tools like dbt and and now Matia's Catalog allow for automated impact scanning and metadata tagging, helping teams understand the full ripple effect of schema changes before they cause downstream failures. For example, if a column like customer_id is renamed or removed, a lineage graph can show you all the dashboards, models, or joins that depend on it. This allows teams to proactively update or validate those downstream assets before errors occur.

Matia's Catalog allows for tagging, certifying assets, and drills down into column-level lineage tounderstand the downstream (and upstream) impacts.

Handling Schema Drift

When schema drift is detected, it’s essential to take immediate action to maintain data quality and pipeline stability. Start by adjusting your ETL processes to accommodate newly added, removed, or modified columns, this may involve updating transformation logic, data mappings, or validation rules. If the schema change was unintended or caused errors, consider restoring from a recent backup to revert to a stable state. Additionally, ensure that your analytics tools, BI dashboards, and downstream queries are updated to reflect the new schema, preventing broken reports or misleading insights. It's also important to document schema changes and communicate them with relevant stakeholders to maintain transparency and coordination across teams. Finally, consider implementing stricter version control or data contracts to proactively manage schema evolution and reduce the risk of future disruptions.

Implementing a Schema Drift Alert System in Snowflake

In this step-by-step tutorial, we will build a schema drift alert system using Snowflake by leveraging components we have previously defined, including schema snapshot capture, categorizing schema changes, distinguishing between breaking and non-breaking changes, and implementing automated notifications to keep end users informed of these changes via email and Slack integrations.

Overview: Schema Drift Detection and Alerting Workflow

The flowchart below outlines the end-to-end process, starting from capturing schema snapshots to issuing alerts and resolving changes. This system not only detects and logs schema drift but also automates alerting, enabling teams to respond proactively, maintain data reliability, and adjust ETL processes by updating transformation logic to handle newly detected columns or structural changes.

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We will be loading the following transactions dataset into Snowflake:

Step 1:  Create a schema snapshot table: audit_schema_snapshots
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CREATE OR REPLACE TABLE audit_schema_snapshots (
    snapshot_time TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    table_catalog STRING,
    table_schema STRING,
    table_name STRING,
    column_name STRING,
    data_type STRING
);

This step creates the audit_schema_snapshots table in Snowflake. The table is designed to store schema snapshots—detailed records capturing the exact structure of your tables at specific points in time. Each snapshot includes:

• snapshot_time: Automatically records when the snapshot was taken
• table_catalog: The database name of the captured table
• table_schema: The schema name within the database
• table_name: The name of the table being captured
• column_name: The names of the columns within the table.
• data_type: Data types associated with each column.

This snapshot table serves as the baseline against which future schema changes (schema drift) can be detected and analyzed.

Step 2:  Create a stored procedure that reads INFORMATION_SCHEMA.COLUMNS

The procedure reads the current schema structure from Snowflake's built-in metadata view INFORMATION_SCHEMA.COLUMNS for a given table, and then inserts this schema snapshot into the audit_schema_snapshots table created earlier.

CREATE OR REPLACE PROCEDURE take_schema_snapshot(schema_name STRING, table_name STRING)
RETURNS STRING
LANGUAGE JAVASCRIPT
EXECUTE AS CALLER
AS
$$
var schemaName = arguments[0];
var tableName = arguments[1];
var insert_sql = `
  INSERT INTO audit_schema_snapshots (
    table_catalog, table_schema, table_name, column_name, data_type
  )
  SELECT 
    UPPER(table_catalog), 
    UPPER(table_schema), 
    UPPER(table_name), 
    UPPER(column_name), 
    data_type
  FROM INFORMATION_SCHEMA.COLUMNS
  WHERE UPPER(table_schema) = UPPER('` + schemaName + `') 
    AND UPPER(table_name) = UPPER('` + tableName + `')
`;

var stmt = snowflake.createStatement({ sqlText: insert_sql });
stmt.execute();
return 'Snapshot taken for ' + tableName + ' in schema ' + schemaName;
$$;

Output of current AUDIT_SCHEMA_SNAPSHOT:

This captures and saves the current schema structure of the table "TRANSACTIONS_DB"."PUBLIC"."TRANSACTIONS" into the audit_schema_snapshots table, creating a reliable baseline for future drift detection.

We can automate this as a task to run at your preferred frequency from every 30 minutes, hour, day, etc. 

CREATE OR REPLACE TASK schema_snapshot_task  
WAREHOUSE = COMPUTE_WH  
SCHEDULE = '30 MINUTE'
AS
CALL take_schema_snapshot('your_schema_name', 'your_table_name');

#Start the Task
ALTER TASK schema_snapshot_task RESUME;

Step 3: Create a Schema Drift Alerts Table

Next, we created a table named schema_drift_alerts that will be used to log and manage any detected schema changes (also known as schema drift) in your Snowflake database. This table acts as the central alerting mechanism of your schema drift detection system.

CREATE OR REPLACE TABLE schema_drift_alerts (
    alert_id STRING DEFAULT UUID_STRING(),
    table_name STRING,
    column_name STRING,
    change_type STRING,
    impact_category STRING,
    status STRING DEFAULT 'OPEN',  -- OPEN or CLOSED
    first_detected_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    last_detected_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    alert_sent BOOLEAN DEFAULT FALSE
);

This table will allow you to:

• Track and log all schema changes over time
• Avoid sending duplicate alerts by checking the status and alert_sent flags
• Monitor recurring issues with the last_detected_at timestamp
• Integrate with notification systems (like email or Slack) to send alerts only when needed

Step 4: Create the Schema Drift Detection Procedure

This stored procedure, log_schema_drift, compares the current schema of a table with its most recent snapshot (stored in audit_schema_snapshots) to detect any schema drift, changes in column structure that have occurred since the last snapshot. It logs the results into the schema_drift_alerts table.

CREATE OR REPLACE PROCEDURE log_schema_drift(table_name_input STRING)
RETURNS STRING
LANGUAGE JAVASCRIPT
EXECUTE AS CALLER
AS
$$
var tableName = arguments[0].toUpperCase();
var drift_count = 0;

// Get latest snapshot time
var snapshot_stmt = snowflake.createStatement({
  sqlText: `SELECT MAX(snapshot_time) FROM audit_schema_snapshots WHERE UPPER(table_name) = ?`,
  binds: [tableName]
});
var snapshot_rs = snapshot_stmt.execute();
if (!snapshot_rs.next()) return "No snapshot found for " + tableName;
var snapshot_time = snapshot_rs.getColumnValue(1);

// Compare snapshot to live schema (REMOVED columns)
var removed_stmt = snowflake.createStatement({
  sqlText: `
    SELECT column_name
    FROM audit_schema_snapshots
    WHERE snapshot_time = ? AND UPPER(table_name) = ?
    MINUS
    SELECT column_name
    FROM INFORMATION_SCHEMA.COLUMNS
    WHERE UPPER(table_name) = ?
  `,
  binds: [snapshot_time, tableName, tableName]
});
var removed_rs = removed_stmt.execute();
while (removed_rs.next()) {
  var col = removed_rs.getColumnValue(1);
  snowflake.createStatement({
    sqlText: `
      INSERT INTO schema_drift_alerts (table_name, column_name, change_type, impact_category, status)
      VALUES (?, ?, 'REMOVED', 'Breaking', 'OPEN')
    `,
    binds: [tableName, col]
  }).execute();
  drift_count++;
}

// Compare live schema to snapshot (ADDED columns)
var added_stmt = snowflake.createStatement({
  sqlText: `
    SELECT column_name
    FROM INFORMATION_SCHEMA.COLUMNS
    WHERE UPPER(table_name) = ?
    MINUS
    SELECT column_name
    FROM audit_schema_snapshots
    WHERE snapshot_time = ? AND UPPER(table_name) = ?
  `,
  binds: [tableName, snapshot_time, tableName]
});
var added_rs = added_stmt.execute();
while (added_rs.next()) {
  var col = added_rs.getColumnValue(1);
  snowflake.createStatement({
    sqlText: `
      INSERT INTO schema_drift_alerts (table_name, column_name, change_type, impact_category, status)
      VALUES (?, ?, 'ADDED', 'Non-breaking', 'OPEN')
    `,
    binds: [tableName, col]
  }).execute();
  drift_count++;
}

return (drift_count === 0)
  ? "No schema drift detected for " + tableName
  : `${drift_count} changes detected for ${tableName}`;
$$;

CALL log_schema_drift('TRANSACTIONS');

Here’s what the output looks like

log_schema_drift:

Here's the schema drift alert table:

This would log any added or removed columns in the TRANSACTIONS table and store the results in schema_drift_alerts.

In summary, this procedure:

• Compares the live schema to the most recent snapshot
• Logs added and removed columns
• Labels changes as Breaking (Removing Columns) or Non-breaking (Adding Columns)
• Marks all new issues with status = 'OPEN'

Step 5: Add or Remove Columns for Schema Drift Alerts

INSERT INTO TRANSACTIONS (INVOICENO, STOCKCODE, PROMO_CODE) 
VALUES ('536370', '85123C', 'SALE50');

ALTER TABLE "TRANSACTIONS_DB"."PUBLIC"."TRANSACTIONS" ADD COLUMN DISCOUNT_CODE STRING;

INSERT INTO TRANSACTIONS (INVOICENO, STOCKCODE, DISCOUNT_CODE)
VALUES ('536370', '85123C', 'DISC50');

ALTER TABLE "TRANSACTIONS_DB"."PUBLIC"."TRANSACTIONS"
DROP COLUMN PROMO_CODE;

A PROMO_CODE column is dropped from the TRANSACTIONS table.

The next schema check logs it as a breaking change. A Slack alert is sent and the alert is marked as OPEN. Once the column is re-added, the alert status is automatically CLOSED.

Step 6: Create the Email Notification Procedure

Next, we defined another stored procedure, send_drift_alert_email, which sends out email alerts for new, unresolved schema drifts detected in the system. It uses Snowflake’s native SYSTEM$SEND_EMAIL function and works alongside the schema_drift_alerts table.

CREATE OR REPLACE PROCEDURE send_drift_alert_email()
RETURNS STRING
LANGUAGE JAVASCRIPT
EXECUTE AS CALLER
AS
$$
var summary = "";
var rs = snowflake.createStatement({
  sqlText: `
    SELECT table_name, column_name, change_type, impact_category
    FROM schema_drift_alerts
    WHERE status = 'OPEN' AND alert_sent = FALSE
  `
}).execute();

var rows = [];
while (rs.next()) {
  var table = rs.getColumnValue(1);
  var column = rs.getColumnValue(2);
  var type = rs.getColumnValue(3);
  var impact = rs.getColumnValue(4);
  summary += `• ${impact} ${type} -- Column '${column}' in table '${table}'\n`;
  rows.push([table, column, type]);
}

if (rows.length === 0) {
  return "No new alerts to send.";
}

// SAFELY use bind parameter for email body
var email_stmt = snowflake.createStatement({
  sqlText: `
    CALL SYSTEM$SEND_EMAIL(
      'drift_email_integration',
      'your_email@gmail.com',
      'Schema Drift Alert',
      ?
    )
  `,
  binds: [summary]
});
email_stmt.execute();

// Update alerts to mark them as sent
for (let i = 0; i < rows.length; i++) {
  snowflake.createStatement({
    sqlText: `
      UPDATE schema_drift_alerts
      SET alert_sent = TRUE
      WHERE table_name = ? AND column_name = ? AND change_type = ? AND status = 'OPEN'
    `,
    binds: [rows[i][0], rows[i][1], rows[i][2]]
  }).execute();
}

return "Email sent for " + rows.length + " new schema drift alerts.";
$$;

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Here’s the output:

Email Notifications:

An email is sent for every new schema drift upon calling the store procedure. 

STEP 7: Send Schema Drift Alerts via Slack

This step expands your alerting capabilities by sending real-time schema drift notifications directly to Slack, using Snowflake’s webhook-based notification system.

#Stores your Slack Incoming Webhook URL securely in Snowflake as a secret.

CREATE OR REPLACE SECRET my_slack_webhook_secret
  TYPE = GENERIC_STRING
  SECRET_STRING = 'https://hooks.slack.com/services/...';

CREATE OR REPLACE NOTIFICATION INTEGRATION my_slack_webhook_integration
  TYPE=WEBHOOK
  ENABLED=TRUE  WEBHOOK_URL='https://hooks.slack.com/services/XXXXXXX/XXXXXXX/XXXXX'
  WEBHOOK_SECRET=my_slack_webhook_secret
  WEBHOOK_BODY_TEMPLATE='{"text": "SNOWFLAKE_WEBHOOK_MESSAGE"}'
  WEBHOOK_HEADERS=('Content-Type'='application/json');

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This sets up a Webhook integration that tells Snowflake how to send messages to Slack.

• WEBHOOK_URL: Points to your Slack Incoming Webhook endpoint.
• WEBHOOK_SECRET: References the secure token you created earlier.
• WEBHOOK_BODY_TEMPLATE: Defines the format Slack expects. The placeholder SNOWFLAKE_WEBHOOK_MESSAGE will be replaced with your actual message.

Send a Message via Stored Procedure

You should place this block inside your send_drift_alert_email() procedure, right after inserting or detecting schema changes. This ensures Slack gets notified only once per alert, in sync with your email system.

CALL SYSTEM$SEND_SNOWFLAKE_NOTIFICATION(
  SNOWFLAKE.NOTIFICATION.TEXT_PLAIN(
    SNOWFLAKE.NOTIFICATION.SANITIZE_WEBHOOK_CONTENT('Non-breaking ADDED -- Column DISCOUNT_CODE in table TRANSACTIONS')
  ),
  SNOWFLAKE.NOTIFICATION.INTEGRATION('my_slack_webhook_int')
);

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Here’s what the slack notification looks like:

Upon detecting schema drift, and calling the store procedure, an alert is sent to your Slack account. 

Step 8: Automate Drift Checks (Task)

Instead of calling the store procedures for snapshot and detection manually, we can set up a Snowflake task named drift_monitoring_task that automatically runs your schema drift detection and email alerting logic on a recurring schedule, every 30 minutes in this case.

CREATE OR REPLACE TASK drift_monitoring_task
  WAREHOUSE = COMPUTE_WH
  SCHEDULE = 'USING CRON */30 * * * * UTC'
AS
BEGIN
  CALL log_schema_drift('TRANSACTIONS');
  CALL send_drift_alert_email();
END;

ALTER TASK drift_monitoring_task RESUME;

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This sets up a Snowflake Task to:

1. Run log_schema_drift() — calls the log_schema_drift procedure, which compares the current schema of the TRANSACTIONS table to the latest snapshot and logs any added or removed columns into the schema_drift_alerts table.
2. Run send_drift_alert_email() — After detecting and logging any changes, this line sends email notifications for any new, unresolved schema drifts using SYSTEM$SEND_EMAIL.

This task ensures that schema drift detection and alerting are automated, runs every 30 minutes, and is in real-time. Alerts are sent immediately after drift is detected, which reliably compares against the latest available snapshot

Step 9: Manage Alert Status (Open/Closed)

This step ensures that once a drift issue is resolved (e.g., a column is added back), you can mark that alert as CLOSED so it doesn’t trigger notifications again.

UPDATE schema_drift_alerts
SET status = 'CLOSED'
WHERE status = 'OPEN'
  AND change_type IN ('ADDED', 'REMOVED')
  AND table_name = 'TRANSACTIONS'
  AND column_name IN (
    SELECT column_name
    FROM INFORMATION_SCHEMA.COLUMNS
    WHERE UPPER(table_name) = 'TRANSACTIONS'
  );

We can update the status of schema drift alerts for the TRANSACTIONS table — specifically closing alerts for columns that now exist in the table (i.e., the column was previously flagged as ADDED or REMOVED, but now is present in the actual schema).

To automate closing schema drift alerts, you can implement a recurring validation workflow that:

• Checks the actual schema vs. recorded drift alerts
• Automatically closes resolved alerts
• Logs or notifications on changes

In conclusion, we built an automated system to detect and alert on schema drift in  Snowflake. The system captured snapshots of table structures at regular intervals and compared them over time to identify changes such as added or removed columns. Detected changes were logged in a dedicated alert table, where each alert was categorized by impact (e.g., breaking or non-breaking) and tracked by status (open or closed). We implemented automated email and Slack notifications to inform stakeholders of new schema changes in real time. The entire workflow was scheduled to run automatically using Snowflake Tasks, eliminating the need for manual monitoring. Additionally, we built logic to automatically close alerts when previously flagged changes were resolved, such as when a removed column was re-added, ensuring the alert system remained current and accurate. This system helped improve visibility, maintain pipeline stability, and reduce the risks associated with untracked schema changes.

Shifting Left: Preventing Breakage Before it Happens

Observability plays a critical role as a proactive mechanism, enabling teams to monitor schema integrity precisely at the point of data ingestion. Matia’s unified DataOps platform empowers teams to detect and swiftly resolve data anomalies early within the pipeline. With built-in observability, Matia allows data teams to maintain a robust data ecosystem without relying on external tools. In essence, data teams can spend time actually managing data instead of vendors. This ensures high confidence in both data integrity and the performance of downstream AI models.

Matia distinguishes itself through an innovative observability approach, providing continuous visibility into data flows and proactively catching and correcting anomalies as they emerge. Unlike traditional methods, which often address data issues reactively, Matia's proactive "shift-left" strategy identifies and mitigates problems before they can affect end-users. Where other platforms might surface that an integration didn’t run or a schema shifted, Matia will tell you actually what happened, and how to fix it.This ensures data remains consistently reliable, trustworthy, and actionable throughout the entire pipeline.

Matia handles schema drift automatically as part of shift-left observability

As data flows into Matia’s ingestion layer, it is continuously monitored for consistency, freshness, and other quality metrics. Embedding observability directly within the ETL process provides an additional layer of assurance, helping teams proactively address ingestion issues before data reaches the transformation stage. Monitoring data quality during ingestion also reduces reliance on downstream data warehouse queries, ultimately saving infrastructure costs.

Matia’s observability scrutinizes every transformation output, ensuring data is accurately transformed and maintains the necessary quality standards for downstream use. If an error is found, Matia can prevent reverse ETL and other post warehouse actions from triggering as well. 

With deep integrations with dbt, Matia users also get detailed metadata on model run results, comprehensive lineage ERDs, and a historical view of past dbt test results. This integration enables users to closely monitor dbt runs and access detailed error reports directly within the platform, eliminating the need for separate monitoring tools.

Alert Fatigue can be a problem, so you need a Strategy: Surfacing Real-Time Alerts Matters

Effective alerting for data teams means integrating notifications directly into the tools they already use, such as Slack. Matia’s Slack integration alerts enables users to update issue statuses directly from Slack without logging into the platform. Users receive instant notifications about schema changes, integration failures, and observability anomalies, reducing unnecessary noise with context-aware alerts. Unlike other observability solutions, Matia’s detailed Slack notifications explicitly identify issues and provide actionable suggestions for quick remediation, significantly enhancing responsiveness and operational effectiveness.

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Visualizing schema changes with lineage

Because Matia is a unified platform, users can also see lineage, and be alerted to schema changes, natively in the product, and understand column level lineage. The capabilities will help companies with thousands of models look at their downstream impacts when it comes to schema drift.

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Data Contracts: Making Expectations Explicit

There is a need for data contracts among data producers and consumers to shift data quality into being a shared responsibility. Making data expectations explicit in these contracts, such as specifying schema clearly and adding quality guarantees, it brings structure and accountability to data ecosystems by requiring that changes be negotiated and documented. When paired with observability, they become even more powerful: data contracts define what should happen, and observability verifies that it is happening. This combination ensures that expectations are not only agreed upon but also continuously enforced.

Tools like dbt contracts, Monte Carlo, and Matia make it easier to operationalize data contracts. Dbt contracts allow teams to define expected schemas directly within their transformation code, ensuring that changes to upstream models are validated against defined rules before deployment. Matia makes it easier to operationalize data contracts by acting as the observability layer that monitors schema integrity in real time. When a contract is breached, Matia can trigger contextual alerts via Slack or email and integrate it with dbt to surface test results and lineage, helping teams respond quickly and effectively to data quality issues.

Conclusion

The schema drift alert system tutorial demonstrates how organizations can shift left by proactively detecting changes in data structures and enforcing expectations through automation. By combining schema snapshots, drift detection procedures, and alerting mechanisms via Slack and email, teams can maintain visibility and control over evolving datasets.

Matia enhances this workflow by embedding observability directly at both the ingestion and transformation stages, offering real-time visibility into schema changes, data flow disruptions, and quality anomalies. This proactive monitoring reduces reliance on standalone observability platforms by providing built-in context around where issues originate, how they propagate, and which downstream assets are affected. Its integration with tools like Slack and dbt allows teams to respond in real time, surface model dependencies and test outcomes, and consolidate lineage, quality, and contract enforcement into a single observability layer—enabling resilient and trustworthy data operations from source to insight.

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