Perforce Delphix vs. Mage™ Static Data Masking

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Perforce Delphix
Score 9.9 out of 10
N/A
Delphix, from Perforce, provides test data management for DevOps. The Delphix DevOps Data Platform automates data security, while rapidly deploying test data to accelerate application releases.N/A
Mage™ Static Data Masking
Score 0.0 out of 10
N/A
Mage™ Static Data Masking (formerly iScramble, MENTIS' Data Anonymization module) protects critical sensitive data in non-product and pre-production environments. iScramble offers users the flexibility to choose the anonymization method per requirements - including encryption, tokenization, and masking techniques - to protect sensitive data in a delicate balance of performance and security.N/A
Pricing
Perforce DelphixMage™ Static Data Masking
Editions & Modules
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Offerings
Pricing Offerings
Perforce DelphixMage™ Static Data Masking
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeOptional
Additional Details
More Pricing Information
Community Pulse
Perforce DelphixMage™ Static Data Masking
Best Alternatives
Perforce DelphixMage™ Static Data Masking
Small Businesses

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Medium-sized Companies

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Enterprises

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Perforce Delphix
Perforce Delphix
Score 9.9 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Perforce DelphixMage™ Static Data Masking
Likelihood to Recommend
7.5
(0 ratings)
-
(0 ratings)
Support Rating
8.9
(0 ratings)
-
(0 ratings)
User Testimonials
Perforce DelphixMage™ Static Data Masking
Likelihood to Recommend
REST API calls are available for all Delphix activities. Delphix makes it simple to move data between production to other test or QA environments. Through the replacement with a dummy, yet realistic data of the customer or patient data, you reduce the danger of data breaches for any and all copies of data you produce.
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Pros
  • Storage consumption reduction.
  • Data environments self service for consumers.
  • Excellent documentation online.
  • Great community for support you.
  • Great company with great people working on it.
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Cons
  • User/PWD management in the Delphix Self-Service web page does not support LDAP nor AD security groups. This creates a hassle if you have a large number of self-service users to create and maintain passwords for. The fix for this is supposed to be the new Central Management tool. We just ended up creating jobs in Jenkins (which supports groups) using the python-examples scripts from the Delphix Github.
  • Reporting on storage and performing capacity planning is not easy when running many (20+) virtual databases. The latest release and new Central Management tool are both supposed to storage reporting much better.
  • Delphix Data Masking tool could handle masking of complex flat files better. We ended up creating a tagger and un-tagger to add/remove metadata to enable Delphix masking to be able to mask our complex flat file. Would have been nice if that capability was already built into the masking tool.
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Support Rating
Delphix support could be a little better. It seems sometimes we bounce through support engineers until we get one that understands our issues and know how to fix it.
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Alternatives Considered
There are many first generation test data management tools that use SQL scripts behind the scenes to create small subsets of data for testing of functional subjects within the database (i.e. Claims or Membership, etc.). Delphix differs by operating at the data block level on disk, sitting between the db engine (Oracle, SQL, etc.) and the physical storage. This allows Delphix to provide full size virtual databases for the cost of not much more than enough storage for a single prod sized copy. Intelligent block sharing also enables full db refreshes and rewinds in minutes, not hours or days, all without having to maintain any schema relationship information which is required in first-gen TDM tools. Delphix does not perform any SQL functions so its not for extract, transform, load or creating smaller subset databases. We figured why subset the data if we can get a full virtual copy for no additional cost!
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Return on Investment
  • Delphix has a brilliant way of documentation online.
  • Delphix has the constant need to improve its quality and fix bugs quickly as well as transparently.
  • With Delphix, my data remains secured.
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ScreenShots

Mage™ Static Data Masking Screenshots

Screenshot of Choose from 60+ different anonymization methods that protect your sensitive data effectively. Maintain referential integrity between applications through anonymization methods that give you consistent results across applications and datastores. Anonymization methods that offer you the best of both worlds in terms of protection and performance. Choose to encrypt, tokenize, or mask the data as per the use case that suits you.Screenshot of Anonymize your sensitive data using a range of methods that provide adequate security while maintaining data usability. Protect sensitive data across data stores and applications and maintain referential integrity between them. Choose from a variety of NIST-approved encryption and tokenization algorithms in addition to masking to secure your sensitive data. Maintain minimal reversibility risk, thereby complying with rigorous regulations like HIPAA, GDPR, and CCPA.Screenshot of Choose how you want to secure your sensitive data with a data classification centric anonymization technique. Secure your data across the spectrum, whether it is in-transit, at-rest, or in-use. Provide the best in class security for your sensitive data with NIST approved fips140 algorithm for encryption and tokenization.Screenshot of Implement masking that integrates easily with your replication process with a choice of in-app or API based execution of anonymization. Anonymize your data with context preserving techniques that enable you to retain the data’s usability. Retain the characteristics of the original data with anonymization techniques that maintain format, length, and context.Screenshot of Enable adequate anonymization with minimal re-identification risk through the use of MENTIS Identities (patent pending) masking method. Generate a fake dataset similar in characteristics to the original data through fuzzy logic and artificial intelligence, with MENTIS identities. Maintain an anonymized datastore that preserves demographics, gender ratios, age distribution, and the like.