![]() ![]() An efficient solution should know that it needs to mask the integer plate_number key with an integer and the string key with a string. However, the plate_number key in document A is an integer, while the one in document B is a string. Provide complete visibility into your document collection to observe and check each document during the generation process.Īssume a sample analytics collection with multiple documents contains documents A and B with a plate_number key.Mask the data according to type, even though key types may vary across document levels with the same key.Detect and locate PII across each document in the entire collection.Generating MongoDB data is challenging because an effective solution needs to: Finding an Effective Data Masking Solution Your de-identification infrastructure requires generators that can track and mask each version and document format. MongoDB’s various formats and versions significantly increase that time. Challenge 3: Time required to buildĮven for a relational database management system (RDBMS), it takes a significant amount of time and resources to create an infrastructure capable of generating test data that perfectly mimics production data. These high-level nested document fields create complex hierarchies, which complicate the level of granularity needed to generate realistically represented test data. The JSON format houses various forms of data, from names to license plate numbers and other types that are less easily quantifiable. MongoDB’s JSON storage format poses another data masking challenge. Challenge 2: MongoDB’s JSON storage format ![]() This lack of consistency presents an obstacle with masking data and generating production-like data for testing. A field may exist as an integer in one document level and a string in another. Furthermore, this type can change with each document level. Since MongoDB is schemaless, each field in a data collection can represent any one of the various data types. The first challenge is its unstructured nature. However, this document-based storage system presents significant challenges when de-identifying and masking data. ![]() MongoDB’s flexible storage system enables efficiency for scaling apps because it stores large amounts of data within clusters defined in millions of nodes. Its document database system operates without structure or schema, and each copy can contain numerous types of data as the data level progresses. MongoDB is a NoSQL database system that stores data as documents. ![]() Then, we’ll wrap up with a quick demonstration of how to mask MongoDB data using Tonic. Let’s explore how to mask data for testing in MongoDB, plus what makes this such a challenging nut to crack. (Spoiler alert: Tonic.ai isn’t “most teams.”) Personally identifiable information (PII) can be scattered throughout your document-based data in ways that are hard to predict-so hard that it simply isn’t a challenge most teams offering data de-identification solutions are willing to take on. Masking data for safe, compliant use in testing environments is not as straightforward as it seems, especially when using schemaless, unstructured databases like MongoDB. ![]()
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