Data Quality Studio
Atlan's Data Quality Studio is a data quality platform designed for teams operating in Snowflake and Databricks environments.
Publisher review
Atlan's Data Quality Studio is a data quality platform designed for teams operating in Snowflake and Databricks environments. It positions itself as a unified trust engine for AI, aiming to accelerate analytics and AI readiness by managing data freshness, completeness, validity, and more. The tool is built for data engineers, analysts, and governance professionals who need to set up and monitor data quality checks without extensive manual configuration. It is particularly suited for organizations already invested in the Snowflake or Databricks ecosystems, offering deep integration with those platforms.
The platform runs natively on Snowflake Data Metric Functions (DMFs), executing checks directly within the customer's Snowflake account. This approach keeps data in place and scales to petabytes without requiring new infrastructure. Users can define quality expectations using no-code templates, custom SQL, or AI-suggested rules. The system provides a unified data quality view across both Snowflake and Databricks, surfacing trust signals everywhere. Collaboration is supported through Slack-based workflows, enabling teams to review and approve data quality rules and alerts in their existing communication channels.
In the data quality market, Data Quality Studio competes with established players like Monte Carlo, Anomalo, and Soda. Monte Carlo is known for its automated monitoring and complete package approach, while Anomalo focuses on machine learning-driven anomaly detection. Soda offers open-source and cloud-based solutions for data testing. Data Quality Studio differentiates itself by its tight coupling with Snowflake's native capabilities and its emphasis on business-first quality checks, allowing non-technical users to define expectations in natural language. However, it is a newer entrant compared to these competitors, which have broader platform support and longer track records.
The primary trade-offs are its limited ecosystem focus and lack of transparent pricing. Data Quality Studio is optimized for Snowflake and Databricks, meaning organizations using other data warehouses like BigQuery or Redshift may not benefit from its core features. The absence of public pricing information makes it difficult for teams to evaluate cost without a sales conversation. Additionally, as a relatively new product, it may lack the extensive community support and third-party integrations that more mature tools like Monte Carlo offer. Teams seeking a multi-cloud, vendor-agnostic solution should consider alternatives with broader compatibility.
How it works
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Native Snowflake DMF integration
Runs checks directly on Snowflake Data Metric Functions, processing data in-place within the customer's account to scale to petabytes.
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No-code and SQL checks
Users define quality expectations using natural language templates or custom SQL, accommodating both business and technical users.
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AI-suggested rules
Automatically generates data quality rules based on historical patterns, reducing manual effort in rule creation.
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Unified cross-platform view
Provides a single dashboard for data quality signals across Snowflake and Databricks, enabling consistent monitoring.
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Slack-based collaboration
Supports workflow approvals and alerts directly in Slack, allowing teams to review and act on quality issues without switching tools.
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Zero-infrastructure deployment
Rules execute inside the customer's existing Snowflake account, requiring no additional servers or data movement.
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Business-first quality templates
Enables non-technical stakeholders to define quality expectations in natural language, bridging the gap between business and engineering.
Strengths and trade-offs
Strengths
- Runs natively on Snowflake Data Metric Functions, processing data in-place and scaling to petabytes without additional infrastructure.
- Offers both no-code templates and custom SQL for defining quality checks, accommodating users with varying technical skills.
- Provides a unified data quality view across Snowflake and Databricks, reducing the need for separate monitoring tools for each platform.
- Integrates with Slack for workflow approvals and alerts, enabling teams to manage data quality within their existing communication channels.
Trade-offs
- Limited to the Snowflake and Databricks ecosystems, making it unsuitable for organizations using other data warehouses like BigQuery or Redshift.
- Pricing is not publicly disclosed, requiring potential users to engage with sales to understand costs.
- As a relatively new product, it lacks the extensive community support and third-party integrations of more mature competitors like Monte Carlo.
- The focus on Snowflake-native execution may create vendor lock-in for teams that later want to adopt a multi-cloud strategy.
Pricing context
Not publicly disclosed; requires contacting Atlan sales for a quote.
Getting started with Data Quality Studio
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Sign up for Data Quality Studio
Visit Atlan's website and request a demo or trial of Data Quality Studio. Complete the registration form with your work email and organization details. A sales representative will contact you to set up your account and provide access credentials.
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Connect your Snowflake or Databricks
Log into Data Quality Studio and navigate to the data source configuration page. Provide your Snowflake or Databricks connection details, including account URL, warehouse, and credentials. The platform will test the connection and establish secure access to your data.
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Define your first quality check
Select a table or dataset from the connected source. Use the no-code template to set a quality rule, such as ensuring a column has no null values. Alternatively, write a custom SQL query to define more complex checks. Save the rule to activate monitoring.
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Review quality results in dashboard
Open the unified data quality dashboard to view the results of your checks. Examine pass/fail statuses, freshness metrics, and completeness scores for your datasets. Filter by platform or dataset to focus on specific areas needing attention.
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Set up Slack alerts and approvals
Integrate your Slack workspace with Data Quality Studio by providing the Slack API token or webhook URL. Configure alert rules to notify your team when a quality check fails. Set up approval workflows for rule changes, allowing team members to review and approve directly in Slack.
Frequently Asked Questions
What is Data Quality Studio by Atlan?
Data Quality Studio is a data quality platform from Atlan designed for Snowflake and Databricks environments. It helps teams manage data freshness, completeness, and validity to accelerate analytics and AI readiness without extensive manual configuration.
How does Data Quality Studio integrate with Snowflake?
It runs natively on Snowflake Data Metric Functions, executing checks directly within your Snowflake account. This keeps data in place and scales to petabytes without requiring new infrastructure, making it efficient for large datasets.
What types of data quality checks can I create in Data Quality Studio?
You can define quality expectations using no-code templates, custom SQL, or AI-suggested rules based on historical patterns. This accommodates both business users who prefer natural language and technical users who need precise SQL controls.
Does Data Quality Studio support both Snowflake and Databricks?
Yes, it provides a unified data quality view across both Snowflake and Databricks. This allows teams to monitor trust signals consistently from a single dashboard, reducing the need for separate monitoring tools for each platform.
How does Data Quality Studio handle collaboration on data quality?
It integrates with Slack for workflow approvals and alerts. Teams can review and approve data quality rules and alerts directly in Slack, enabling them to manage quality issues without switching tools.
What are the main limitations of Data Quality Studio?
It is limited to Snowflake and Databricks ecosystems, so it does not support BigQuery or Redshift. Pricing is not publicly disclosed, requiring a sales conversation. As a newer product, it lacks the community support of mature competitors like Monte Carlo.
Alternatives
How Data Quality Studio compares
Direct head-to-head against 3 competitors. Picked by 7wData.
Data Quality Studio
- Pricing
- Not publicly disclosed; requires contacting Atlan sales for a quote.
- Target
- Atlan's Data Quality Studio is a data quality platform designed for teams operating in Snowflake and Databricks environments.
- Strength
- Runs natively on Snowflake Data Metric Functions, processing data in-place and scaling to petabytes without additional infrastructure.
- Watch for
- Limited to the Snowflake and Databricks ecosystems, making it unsuitable for organizations using other data warehouses like BigQuery or Redshift.
Monte Carlo
- Pricing
- Custom/Contact sales
- Target
- Data teams needing end-to-end observability for large, complex pipelines
- Deployment
- SaaS
- Strength
- Pioneer of data downtime concept; broad ML-driven anomaly detection across warehouses
- Watch for
- Alerts may fire after bad data reaches tables, complicating upstream debugging
Great Expectations
- Pricing
- Open source (free); Great Expectations Cloud from $15,000/year
- Target
- Data engineers wanting an open-source, code-first testing framework
- Deployment
- Self-hosted or cloud
- Strength
- Open-source standard for data quality tests with strong community and integrations
- Watch for
- Requires significant setup and maintenance; no built-in anomaly detection
Ataccama ONE
- Pricing
- Custom/Contact sales
- Target
- Enterprises needing a unified data quality, governance, and catalog platform
- Deployment
- SaaS or on-premises
- Strength
- Combines data quality, catalog, and governance in a single platform
- Watch for
- Complex implementation; pricing can escalate with data volume
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Sources
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