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Mastering Data Masking: Key Strategies for Handling Large-Scale Data Volumes

Mastering Data Masking Key Strategies for Handling Large-Scale Data Volumes
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Masking large volumes of data isn’t just a bigger version of small-scale masking—it’s exponentially more complex. High-volume data masking introduces unique engineering challenges that demand careful balancing of performance, integration, accuracy, and infrastructure costs.

In this blog, we’ll dive into the critical factors you must consider when choosing the right tool for large-scale data masking, helping you confidently navigate these complexities. 

Defining Large Volume 

When we refer to large-volume data, we’re typically talking about datasets that are 10 GB/day or 1M+ documents/day. Such data volumes can be common in enterprises handling massive logs, customer records, conversations, or transaction histories. Processing large volumes requires a data masking tool that can: 

  1. Handle high throughput without degrading performance. 
  2. Process asynchronous jobs to avoid bottlenecks. 
  3. Integrate seamlessly with existing data pipelines for minimal disruption. 

High Volume vs. Low Volume: Unique Challenges 

Processing large volumes of data presents a fundamentally different set of challenges than handling small or moderate data sizes. Let’s break down the key issues: 

  1. Processing Latency: For high-volume datasets, the masking tool must minimize the time spent processing each chunk of data. Latency in data retrieval or masking could cause unacceptable delays. 
  2. Asynchronous Processing and Queuing: Asynchronous processing is critical for ensuring that large data sets do not block smaller jobs. Such processing requires efficient queuing systems where incoming data is processed in parallel and continuously based on system resources. 
  3. Scalability: High-volume data processing requires the ability to auto-scale as the data grows. The masking tool must efficiently scale its resources up when large datasets are being ingested and scale back down once the data is processed. Auto-scaling helps control costs and ensures the system runs optimally. 
  4. Error Handling: With larger volumes, the likelihood of encountering corrupt or malformed data or system failure increases. A robust error-handling mechanism must be in place to deal with failed masking attempts, such as reprocessing and continuing without stopping the entire pipeline. 
  5. Infrastructure Costs: The sheer size of large datasets can have huge implications for infrastructure, especially in cloud environments. Beyond storage, there’s also the computational cost of scanning and masking PII, which can be expensive when using AI models that require GPU processing. 

Asynchronous Processing and Queuing 

In large-volume scenarios, data masking must be processed asynchronously to ensure the system can handle peak loads without crashing. An effective data masking tool for large volumes should: 

  • Provide an internal queuing system to hold data jobs until system resources are available to process them. 
  • Include a tracking mechanism to monitor the status of jobs in the queue, providing feedback on which jobs are in progress, completed, or failed. 

Integration with Existing ETL Tools 

A successful data masking tool should integrate seamlessly with existing ETL frameworks such as Spark and Kafka. Large enterprises commonly use these tools to extract, transform, and load (ETL) data. The masking tool must plug into Spark jobs and process data streams, or the masking tool should be able to read Kafka streams, apply masking, and ensure that the masked data is written back into the data pipeline. 

Auto-Scaling and Infrastructure Efficiency 

Auto-scaling is a core requirement for handling large data sets efficiently. The masking tool should be able to: 

  • Scale up processing capacity as the volume of incoming data increases, leveraging cloud infrastructure to add resources as needed. 
  • Shrink back resources when the data volume decreases, reducing unnecessary infrastructure costs. 

This dynamic scaling approach ensures the system is highly efficient and only consumes resources when needed. 

Caching for Repeated PII and Texts 

When dealing with repetitive data such as logs or a known set of encountered PII, caching becomes an essential feature for optimization. By caching previously masked data, the system can: 

  • Reduce the number of times the AI model needs to be invoked, increasing overall system efficiency and reducing the cost of GPU usage, 
  • Improve processing speed by quickly referencing the cache rather than repeatedly applying the AI model to identical data. 

Balancing GPU vs. CPU Costs in PII Scanning 

When dealing with large-scale PII data, scanning and masking using AI often require GPUs. GPU processing is typically faster but also significantly more expensive than CPU processing. Here’s how to balance the costs and make the most of your infrastructure: 

  1. Evaluate Processing Needs: If the AI model for detecting PII is computationally intensive and time-sensitive, it might be necessary to use large GPUs. However, for less complex masking tasks or scenarios where processing speed is not a critical factor, CPU-based processing might suffice.
  2. Optimize Token Count: Large datasets with massive payloads can be broken down into appropriate chunk sizes for GPU processing, optimizing the GPU needs to process and reducing the overall infrastructure cost. 

Read more: How to Compare the Effectiveness of PII Scanning and Masking Models

Protecto addresses all these challenges by offering a comprehensive enterprise solution with features to handle large volume data. With Protecto, you can ensure that your data masking is highly effective and optimized for cost-efficiency while seamlessly scaling to meet the demands of your organization’s large data volumes. 

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Amar Kanagaraj

Founder and CEO of Protecto

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