Databricks helps companies process large-scale data, and Databricks data masking ensures high-quality, compliant data. If your organization relies on data for AI/ML or analytics, it’s essential to ensure you are implementing a robust, compliant data masking strategy.
Let’s explore how data masking works natively in Databricks. Then, we’ll go over challenges teams might run into in using native masking capabilities. Finally, we will discuss how to determine the best Databricks data masking approach for your team.
How Does Data Masking Work in Databricks?
Databricks offers several native data masking techniques.
Two key features — column masking and dynamic views — are both part of the broader concept of dynamic data masking. Dynamic data masking helps organizations enforce privacy and security policies without altering the underlying data.
Column Masking
Column masking allows you to apply masking function to specific columns in a dataset. This ensures that sensitive information, such as personally identifiable information (PII) or financial data, is obfuscated based on the user's access privileges.
With column masking, the data in the specified columns is replaced with predefined characters or values, making it impossible for unauthorized users to view the actual data. For example, a masked credit card number might display as "****-1234" for users who lack the necessary permissions.
Dynamic Views
Dynamic views in Databricks allow you to create virtualized views of your data. Sensitive fields are automatically masked or transformed depending on the user’s role and permissions by using more complex SQL expressions and regular expressions. This provides a flexible and scalable approach to data access, ensuring that users can only see the data relevant to them while keeping sensitive information protected.
Dynamic views offer more granular control by enforcing security policies at the view level, making it easier to manage access to sensitive data across different teams or environments.
Column masking and dynamic views are both essential tools for dynamic data masking in Databricks. They allow you to protect sensitive information while enabling data-driven insights. They ensure that privacy and security requirements are met without compromising the usability of the data for analytics and AI workloads.
Other Common Databricks Data Masking Workflows
Other than column masking and dynamic views, common data masking workflows that can be manually built in Databricks include:
- Shuffling: Randomly rearranging data values within a column to prevent sensitive information from being identified while maintaining the overall data structure.
- Encryption: Converting sensitive data into an unreadable format. This ensures only authorized users can decrypt and access it, and it protects data both at rest and in transit.
- Nulling: Replacing sensitive values with null or placeholder values. This effectively removes access to the original data but preserves the dataset's structure.
📘Related reading: Data Masking vs. Data Encryption: How to Make the Right Choice
Challenges of Data Masking in Databricks
Databricks’ native data masking techniques offer clear benefits, but they also have limitations.
Challenges with Upstream and Downstream Applications
In a complex data pipeline, masking data in one stage may introduce inconsistencies or errors in downstream applications. For instance, when Databricks applies native dynamic data masking (such as through column masking or dynamic views), sensitive data is obfuscated based on the user’s access privileges.
Yet, inconsistencies can arise if other parts of the data pipeline (especially non-Databricks systems) apply different masking techniques or tools. These tools may use different masking strategies, patterns, or keys. And this can lead to discrepancies in how data is obfuscated across systems.
Inconsistencies can complicate data reconciliation, integration, and reporting because different teams or systems may interpret the data differently.
Additionally, this mismatch might impact the accuracy of analytics, AI models, and decision-making processes that rely on consistent datasets.
Masking Across Large Distributed Datasets
Databricks operate in large-scale, distributed environments where data is spread across multiple nodes and clusters. Applying data masking at scale can be a significant challenge.
Masking operations, especially dynamic views or column-based masking, could introduce overhead in terms of performance. In large datasets, applying masking functions dynamically during query execution can slow down processing times, as every query would need to check access policies and apply the relevant masking logic.
Moreover, when data is distributed across multiple locations or stored in different formats (like Delta tables or Parquet files), it is not always easy to ensure consistent masking functions across all data sources. It’s essential to standardize and enforce masking functions across the entire distributed system to avoid gaps in protection.
Operational Nature of Dynamic Data Masking
A key challenge when using dynamic data masking (such as the column masking and dynamic views in Databricks) is its operational nature compared to static data masking.
Dynamic data masking modifies the visibility of sensitive data at query time based on user access privileges. It allows different users to see different versions of the same data. While this offers flexibility and real-time protection, it can introduce performance overhead as it requires ongoing policy evaluation and transformation during each data query.
This makes it more difficult to ensure that all systems work with the same underlying data semantics. It’s also hard to ensure that analytics results or AI models are not affected by the varying levels of data obfuscation applied in different contexts.
Scalability and Flexibility Challenges
While Databricks' native data masking solutions are effective for many use cases, they may face scalability challenges in high-volume environments with vast amounts of data. It takes careful planning and regular updates to implement and maintain dynamic functions across distributed systems. Doing so helps teams avoid potential performance degradation as data grows.
It can also be hard to change how data is masked when business needs or rules change—especially if you need custom masking that is not already built in.
What Makes Data Masking Critical in AI Environments?
The vast majority of organizations we surveyed for our recent data compliance and security report — 99% — say they use sensitive data in analytics and AI environments. Most (82%) are also concerned about risks like the theft of model training data, personal data re-identification, and non-compliance.
Yet, many leaders don’t know where to begin in addressing these risks and safely implementing AI into analytics workflows. Some falsely assume that their existing compliance methods, built for structured production databases, are just as effective in AI for next-gen platforms like Databricks. But AI poses unique risks to sensitive data that traditional compliance strategies aren’t equipped to handle.
Data masking safeguards not only your customers’ information but also your organization’s reputation. It lets your teams work confidently with accurate and compliant datasets.
But not all data masking strategies or solutions are equal. Homegrown or native sensitive data discovery and masking tools can be insufficient and cause bottlenecks in enterprise pipelines. And it is critical to make sure data is masked irreversibly and that masking policies are consistent across your organization.
Learn How to Balance Innovation, Speed, and Data Privacy in AI & Analytics
Get a primer on the challenges that data engineering leaders face in adopting AI. Then, discover best practices for implementing data masking for enterprise AI and analytics initiatives. This and more in “AI Without Compromise,” an expert guide from Steve Karam, Principal Product Manager for AI, SaaS, and Growth at Perforce Delphix.
Choosing the Best Approach to Databricks Data Masking for Your Team
The three main approaches are:
- Databricks’ native data masking functionality
- Building your own custom scripts
- Integrating a third-party tool
Which one is ideal for your Databricks environment comes down to your organization’s size, how stringent the regulations are in your industry, the amount of data you need to mask, and what you’re using masked data for.
Native Functionality
There are pros and cons to using Databricks’ native data masking features, especially in complex, large-scale environments.
Pros:
- Seamless Integration: Built-in masking features are deeply integrated into Databricks, allowing for easy configuration and deployment.
- Granular Control: Dynamic data masking enables role-based access control, allowing you to mask sensitive data based on the user’s privileges.
- Real-Time Protection: Data is masked in real time during queries, which means sensitive information is always protected without needing to alter the underlying data.
- Simplified Compliance: Helps meet regulatory requirements like GDPR and CCPA by ensuring sensitive data is consistently obfuscated across workloads.
Cons:
- Performance Overhead: Applying dynamic masking during queries can introduce performance bottlenecks, particularly in large-scale distributed datasets or high-throughput environments.
- Complexity in Distributed Systems: Databricks is built on top of Apache Spark. For Spark-based workflows, maintaining consistent masking across distributed data can be challenging, especially when data is partitioned or spread across multiple nodes.
- Limited Flexibility: Databricks' built-in masking capabilities may not offer the same level of customization as third-party solutions, especially for highly specific masking requirements.
- Inconsistencies Across Systems: If other systems in the data pipeline use different masking tools or techniques, maintaining consistency across platforms can be difficult, leading to potential data misalignment.
Custom Scripts
Using existing Spark capabilities to write custom data masking scripts in Databricks allows for a high degree of flexibility and control over how sensitive data is masked. This approach enables you to create tailored masking logic that can be adjusted to meet specific business or compliance requirements and can be integrated directly into your Spark-based data pipelines.
But this approach is time intensive. Writing and maintaining custom masking scripts requires a significant investment of development time and expertise. You need to account for the complexity of applying the correct masking logic across large, distributed datasets, ensuring that all nodes in the Spark cluster correctly process and obfuscate sensitive data without introducing inconsistencies or errors.
Additionally, the custom scripts may need to be updated regularly to account for evolving security and compliance requirements, which can add further maintenance overhead. Testing, debugging, and optimizing these scripts for performance — especially in high-volume, real-time environments — can also be resource-heavy and require careful tuning to avoid performance bottlenecks.
Third-Party Integrated Solutions
As organizations scale their data infrastructure and increase the complexity of their environments, native or custom-built tools often reach their limits, especially when it comes to advanced use-cases like data masking, test data management, or compliance drive workflows in AI/ML environments. While native features within platforms like Databricks can address basic needs, they may fall short in areas like cross-platform consistency or enterprise grade compliance and automation.
At such times, it is very typical for organizations to evaluate third party solutions to address problems like increased regulatory or compliance pressure, losing agility as data grows, complex data pipelines, bottlenecks in MLOps, and security concerns. Third party solutions are designed to specialize where native capabilities plateau by offering greater flexibility, deeper automation, and proven scalability.
Perforce Delphix is one such solution that provides static data masking capabilities for Databricks, offering a robust and secure approach to protecting sensitive data in complex environments. By integrating with Databricks, Delphix allows organizations to mask sensitive information while ensuring data integrity and usability for various applications. This integration provides several key benefits, particularly for DevOps, analytics, and AI environments.
How to Automate Data Masking in Databricks with Perforce Delphix
Automating data masking in Databricks with Perforce Delphix offers a seamless way to protect sensitive data while accelerating AI and analytics initiatives. The integration streamlines the process by automatically discovering, masking, and delivering data for model training and business intelligence applications — all within a single, automated pipeline.
Automate Data Discovery and Masking at Scale
In a MLOps pipeline, sensitive data is often used to train machine learning models, but it must be handled carefully to ensure compliance with data privacy regulations. With Perforce Delphix, sensitive data is automatically discovered across Databricks, and is then masked using customizable, static masking policies.
This process ensures that only obfuscated data is used for model training, preserving the privacy of sensitive information while still allowing AI algorithms to function with realistic data.
Seamlessly Integrate into Analytics Toolchains
Similarly, the same masked data in Databricks can be routed into reporting tools like Power BI or Tableau for business intelligence and analytics purposes. This ensures that stakeholders can access realistic data for reporting and decision-making without compromising data privacy.
Gain One Pipeline for Data Discovery, Masking, and Delivery
One significant benefit of automating the process of masking in Databricks through Delphix is that it integrates all these steps — data discovery, masking, and delivery — into a single pipeline.
This significantly reduces the time, effort, and complexity involved in ensuring compliance and data security. It also minimizes the risk of human error, provides consistent data masking across the pipeline, and supports scalability as your data grows and evolves.
Databricks’ integration with Perforce Delphix makes it easier for organizations to leverage data for analytics and AI while maintaining privacy and security across the entire workflow.
Speed Up Compliance for AI & Analytics with Perforce Delphix
Discover how Delphix helps you quickly mask data, achieve compliance, and remove bottlenecks in AI and analytics workflows. Contact us to learn more about our solutions, and get expert advice on sensitive data discovery and masking.