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Mage™ Static Data Masking vs. PrestoDB (or Presto)

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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    Mage™ Static Data Masking

    N/AN/AMage™ 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

    PrestoDB (or Presto)

    Score10 out of 10
    N/APresto is an open source SQL query engine designed to run queries on data stored in Hadoop or in traditional databases. Teradata supported development of Presto followed the acquisition of Hadapt and Revelytix.N/A
    Pricing
    Mage™ Static Data MaskingPrestoDB (or Presto)
    Editions & Modules
    No answers on this topic
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    Offerings
    Pricing Offerings
    Mage™ Static Data MaskingPrestoDB (or Presto)
    Free Trial
    NoNo
    Free/Freemium Version
    NoNo
    Premium Consulting/Integration Services
    NoNo
    Entry-level Setup FeeOptionalNo setup fee
    Additional Details——
    More Pricing Information
    Best Alternatives
    Mage™ Static Data MaskingPrestoDB (or Presto)
    Small Businesses
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    Amazon RDS
    Score8.1 out of 10
    Medium-sized Companies
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    SingleStore
    Score8.2 out of 10
    Enterprises
    Perforce Delphix
    Score9.9 out of 10
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    Score5.4 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    Mage™ Static Data MaskingPrestoDB (or Presto)
    Likelihood to Recommend
    -
    (0 ratings)
    7.8
    (2 ratings)
    User Testimonials
    Mage™ Static Data MaskingPrestoDB (or Presto)
    Likelihood to Recommend
    Mage Data
    No answers on this topic
    Open Source
    Presto is for interactive simple queries, where Hive is for reliable processing. If you have a fact-dim join, presto is great..however for fact-fact joins presto is not the solution.. Presto is a great replacement for proprietary technology like Vertica
    Incentivized
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    Pros
    Mage Data
    No answers on this topic
    Open Source
    • Linking, embedding links and adding images is easy enough.
    • Once you have become familiar with the interface, Presto becomes very quick & easy to use (but, you have to practice & repeat to know what you are doing - it is not as intuitive as one would hope).
    • Organizing & design is fairly simple with click & drag parameters.
    Incentivized
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    Cons
    Mage Data
    No answers on this topic
    Open Source
    • Presto was not designed for large fact fact joins. This is by design as presto does not leverage disk and used memory for processing which in turn makes it fast.. However, this is a tradeoff..in an ideal world, people would like to use one system for all their use cases, and presto should get exhaustive by solving this problem.
    • Resource allocation is not similar to YARN and presto has a priority queue based query resource allocation..so a query that takes long takes longer...this might be alleviated by giving some more control back to the user to define priority/override.
    • UDF Support is not available in presto. You will have to write your own functions..while this is good for performance, it comes at a huge overhead of building exclusively for presto and not being interoperable with other systems like Hive, SparkSQL etc.
    Incentivized
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    Alternatives Considered
    Mage Data
    No answers on this topic
    Open Source
    Presto is good for a templated design appeal. You cannot be too creative via this interface - but, the layout and options make the finalized visual product appealing to customers. The other design products I use are for different purposes and not really comparable to Presto.
    Incentivized
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    Return on Investment
    Mage Data
    No answers on this topic
    Open Source
    • Presto has helped scale Uber's interactive data needs. We have migrated a lot out of proprietary tech like Vertica.
    • Presto has helped build data driven applications on its stack than maintain a separate online/offline stack.
    • Presto has helped us build data exploration tools by leveraging it's power of interactive and is immensely valuable for data scientists.
    Incentivized
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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.