What Tools Can Automate Data Quality Checks in ETL?

You're in a board meeting when the CFO pulls up the quarterly revenue dashboard. The numbers look wrong, really wrong. Customer acquisition costs have supposedly tripled overnight, and monthly recurring revenue shows a 40% drop that didn't happen. The engineering team gets an emergency page while executives question every data-driven decision made in the past month.

The culprit? A schema change in your CRM system broke the ETL pipeline three weeks ago, but nobody caught it until the corrupted data reached executive dashboards. Manual data quality checks simply can't scale when you're processing terabytes daily across dozens of sources.

Modern data teams need automated quality validation that catches issues before they reach business users. Schema drift, transformation errors, and source system changes create downstream quality issues that surface when business stakeholders notice incorrect reports. When your pipeline processes millions of records daily, automated validation becomes essential infrastructure protection rather than optional enhancement.

This guide examines the 4 leading tools that can automate data quality checks throughout your ETL processes, from extraction through transformation to loading.

Overview: Data Quality Tools Comparison

Key Evaluation Criteria:

• Integration Complexity: How easily the tool integrates with your existing data stack, orchestration platforms, and data warehouses. Tools that work seamlessly with current infrastructure reduce implementation friction and accelerate time-to-value.
• Technical Requirements: Whether the tool requires extensive coding knowledge or offers no-code solutions for different team skill levels. Engineering-heavy teams prefer code-based flexibility while business users need intuitive interfaces.
• Detection Capabilities: Range from basic schema validation to advanced ML-powered anomaly detection and real-time monitoring. More sophisticated detection catches subtle quality issues that rule-based systems miss.
• Scale and Performance: Ability to handle enterprise-scale data volumes without impacting pipeline performance. Quality checks must operate efficiently on terabyte datasets without creating bottlenecks.‍
• Cost Structure: From open-source options to enterprise platforms with custom pricing based on data volume and features. Total cost includes licensing, infrastructure, and operational overhead.

Each tool serves different organizational needs and technical requirements. Great Expectations excels for engineering-heavy teams wanting code-based validation, while Monte Carlo provides enterprise-grade observability with minimal configuration. dbt Tests integrate seamlessly for teams already using dbt, and Soda bridges technical and business users with SQL-based checks.

The key is matching tool capabilities to your team's technical skills, existing infrastructure, and data quality requirements rather than choosing based on features alone.

1. Great Expectations

Great Expectations transforms data validation into version-controlled code through Python-based "expectation suites" that define what good data should look like. The framework automatically validates datasets against these expectations and generates comprehensive data documentation.

Founded as an open-source project, Great Expectations has become the standard for code-driven data validation. The platform enables data teams to create systematic quality checks that evolve with their data infrastructure while maintaining transparency about data quality across the organization.

Key Features:

• Declarative data validation with Python API and JSON configuration
• Auto-generated data documentation and statistical profiling
• Extensive library of 50+ built-in expectations for common validation patterns
• Integration with Jupyter notebooks for interactive data exploration
• Automated expectation generation from sample datasets
• Support for batch and streaming data validation
• Custom expectation development for specific business rules

Integration Capabilities:

• Works seamlessly with modern data orchestration tools including Airflow, Prefect, and Dagster
• Integrates with all major data warehouses (Snowflake, BigQuery, Databricks) and can validate data at multiple pipeline stages
• Supports integration with data integration platforms, ensuring quality checks work across the entire data movement process from source systems to final destinations
• Handles schema changes that can break data quality through automated detection and validation

Use Case Example: Consider a financial services organization that needs to validate transaction data quality during ETL processing. Great Expectations could create custom expectations to check for valid account numbers, transaction amount ranges, and timestamp consistency, preventing corrupted financial data from reaching regulatory reports.

2. dbt Tests

dbt Tests provide native data validation directly within transformation workflows, enabling SQL-based quality checks that run automatically as part of the modeling process. Tests fail fast to prevent bad data from propagating through downstream models.

Built into the dbt ecosystem, dbt Tests leverage the same SQL skills data teams already use for transformations. This integration means quality testing becomes part of the development workflow rather than an additional process, creating natural checkpoints throughout data transformation pipelines.

Key Features:

• Built-in tests for uniqueness, null values, referential integrity, and accepted values
• Custom test development using SQL assertions and macros
• Test results integrated with dbt documentation and lineage graphs
• Incremental testing capabilities for large datasets
• Source data validation before transformation begins
• Model-specific and cross-model relationship testing
• Integration with dbt Cloud for automated test execution

Integration Capabilities:

• Seamlessly works with any data warehouse supporting dbt including Snowflake, BigQuery, Redshift, and Postgres
• Integrates with orchestration tools through dbt's CLI and API
• Test results connect with dbt's documentation and lineage features
• Provides comprehensive data quality visibility within transformation workflows

Use Case Example: An e-commerce organization could use dbt tests to validate customer data quality during transformation. Tests would ensure customer IDs are unique, email addresses follow valid formats, and order amounts fall within expected ranges before building customer lifetime value models.

3. Monte Carlo

Monte Carlo provides ML-powered data observability that automatically detects anomalies in data freshness, volume, and schema without requiring manual rule configuration. The platform monitors entire data ecosystems and provides intelligent alerting with root cause analysis.

The platform uses machine learning to understand normal data patterns, automatically flagging deviations that indicate quality issues. This approach reduces the configuration overhead of rule-based systems while catching subtle anomalies that fixed thresholds might miss.

Key Features:

• Machine learning-based anomaly detection for volume, freshness, and schema changes
• Automated data lineage mapping and impact analysis across systems
• Real-time monitoring with intelligent alert noise reduction
• Incident management and collaboration tools for data teams
• Business impact scoring for quality issues
• Integration with popular BI tools for downstream impact assessment
• Custom metrics and monitors for business-specific quality rules

Integration Capabilities:

• Connects to major data warehouses, lakes, and BI tools including Snowflake, BigQuery, Looker, and Tableau
• Monitors data integration pipelines and tracks quality across the entire data journey
• Provides comprehensive observability for modern data stacks
• Tracks data flow from source systems through transformation to final business applications

Use Case Example: A SaaS organization could use Monte Carlo to monitor customer usage data across their analytics pipeline. The platform would automatically detect when daily active user metrics deviate from normal patterns and trace issues back to specific data sources or transformation steps.

4. Soda

Soda focuses on making data quality accessible through SQL-based checks configured in YAML files. The platform bridges technical and business teams by providing intuitive interfaces for quality monitoring while maintaining the flexibility of code-based validation.

Designed to democratize data quality monitoring, Soda enables both technical and business users to define and monitor quality metrics. The platform uses familiar SQL syntax while providing business-friendly reporting and collaboration features.

Key Features:

• SQL-based quality checks with simple YAML configuration
• Data profiling and automatic anomaly detection
• Integration with popular data warehouses and orchestration platforms
• Collaborative incident management and business-friendly reporting
• Custom metrics development for specific business requirements
• Real-time and scheduled monitoring capabilities
• Data quality scorecards and trend analysis

Integration Capabilities:

• Native integrations with Snowflake, BigQuery, Databricks, PostgreSQL, and other major data platforms
• Works with Airflow, Prefect, and other orchestration tools for automated quality monitoring
• Provides APIs for custom integration and monitoring workflows
• Supports both real-time and scheduled monitoring capabilities

Use Case Example: A retail organization could use Soda to monitor product catalog data quality across multiple systems. SQL-based checks would validate product pricing consistency, inventory level accuracy, and category assignments while providing business teams with quality scorecards and trend reports.

How Do You Choose the Right Data Quality Tool?

Selecting the optimal data quality automation tool requires evaluating your team's technical capabilities, existing infrastructure, and organizational requirements. The decision framework involves four key areas that determine which approach fits your specific context.

1. Team Technical Capabilities 

Engineering-heavy teams with strong Python skills benefit from Great Expectations' extensive customization options and code-based approach. Teams already using dbt for transformations gain immediate value from dbt Tests' native integration. Organizations seeking minimal technical overhead prefer Monte Carlo's ML-powered automation, while teams needing SQL-based solutions that bridge technical and business users choose Soda.

2. Existing Infrastructure Integration

Your current data stack significantly influences tool selection. Teams using dbt should prioritize dbt Tests for seamless workflow integration. Organizations with complex data ecosystems spanning multiple warehouses and BI tools benefit from Monte Carlo's comprehensive monitoring. Those preferring warehouse-native solutions find Soda's strong platform integrations advantageous.

3. Scale and Performance Requirements

High-volume environments processing terabytes daily need tools that operate efficiently without creating pipeline bottlenecks. Great Expectations offers fine-tuned performance control through custom expectations, while Monte Carlo provides enterprise-scale monitoring with intelligent sampling. Consider whether you need real-time validation or can operate with batch-based quality checks.

4. Organizational Collaboration Needs

Business stakeholder involvement in quality monitoring influences tool selection. Monte Carlo and Soda provide business-friendly interfaces and collaboration features, while Great Expectations and dbt Tests serve technically-focused teams. Consider whether quality monitoring remains centralized within data teams or requires broader organizational participation.

What Are the Implementation Best Practices?

Successful data quality automation requires strategic implementation that balances comprehensive coverage with operational efficiency. Follow these practices to maximize effectiveness while minimizing disruption to existing workflows.

Start with Critical Data Pathways

Begin quality automation on business-critical data flows rather than attempting comprehensive coverage immediately. Focus on data feeding executive dashboards, regulatory reports, or customer-facing applications where quality issues create immediate business impact. This approach demonstrates value quickly while building team confidence in automated quality processes.

Layer Multiple Validation Approaches

Combine tools rather than relying on single solutions. Use dbt Tests for transformation-level validation, Great Expectations for detailed data profiling, and Monte Carlo for ecosystem-wide monitoring. This layered approach catches different types of quality issues while providing redundancy for critical data flows.

Integrate with Existing Workflows

Embed quality checks into current development and deployment processes rather than creating parallel workflows. Configure tests to run automatically with transformation deployments, integrate alerts with existing incident management systems, and connect quality metrics to data team dashboards.

Balance Automation with Human Oversight

Automated detection requires human judgment for resolution. Establish clear escalation procedures for quality issues, define ownership for different data domains, and create playbooks for common quality problems. Automation should augment human decision-making rather than replace it entirely.

Conclusion

Choosing the right data quality automation tool depends on your team's technical capabilities, existing infrastructure, and organizational requirements. Great Expectations excels for engineering-heavy teams wanting maximum customization, while dbt Tests provide seamless integration for teams already using dbt transformations.

Monte Carlo offers enterprise-grade observability with minimal configuration overhead, making it ideal for organizations needing comprehensive monitoring across complex data ecosystems. Soda strikes a balance between technical flexibility and business accessibility through SQL-based checks and collaborative features.

Selection Framework:

• For Python-savvy engineering teams: Great Expectations provides maximum flexibility and customization
• For dbt users: Native dbt Tests offer seamless integration with existing transformation workflows
• For enterprise observability needs: Monte Carlo delivers ML-powered monitoring with minimal setup
• For balanced technical/business requirements: Soda bridges SQL familiarity with business-friendly interfaces

Most successful implementations combine multiple approaches: transformation-level testing with dbt, comprehensive monitoring with observability platforms, and custom validation for specific business rules.

The key to successful data quality automation lies in treating it as essential infrastructure protection rather than optional enhancement. Quality issues compound quickly at enterprise scale, making early detection and automated response critical for maintaining stakeholder trust in data-driven decisions.

Ready to implement automated quality checks in your ETL pipelines? Reliable data integration with schema validation forms the foundation of effective quality automation. Explore how Airbyte's schema change detection and extensive connector ecosystem can support your data quality strategy from source to destination.

Frequently Asked Questions 

Why is automated data quality validation important?

Manual checks can’t keep up with enterprise-scale pipelines. Automated validation ensures schema changes, transformation errors, or source issues are caught before they reach dashboards or reports, preventing business decisions from being made on bad data.

Can I use multiple data quality tools together?

Yes. Many teams layer tools for full coverage—for example, using dbt Tests for transformation-level checks, Great Expectations for profiling, and Monte Carlo for end-to-end observability. Combining tools helps catch different classes of issues and builds redundancy.

How do I decide which data quality tool to start with?

It depends on your stack and team skills. If you already use dbt, start with dbt Tests. Python-heavy teams may prefer Great Expectations. Enterprises with complex environments often choose Monte Carlo, while Soda provides a balance between SQL-based flexibility and business-friendly reporting.

Do automated tools completely replace human oversight?

No. Automated systems flag anomalies, but humans decide how to act. Teams should define escalation paths, ownership, and playbooks for common issues. Automation is most effective when paired with clear processes for resolution.

How much do data quality platforms cost?

Open-source options like Great Expectations and dbt Tests are free to start, though they require engineering effort. Platforms like Monte Carlo and Soda follow enterprise pricing models based on usage and features. Costs include not just licenses but also infrastructure and operations.