Best Data Quality Software for Blotout

Find and compare the best Data Quality software for Blotout in 2025

Use the comparison tool below to compare the top Data Quality software for Blotout on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.

  • 1
    Immuta Reviews
    Immuta's Data Access Platform is built to give data teams secure yet streamlined access to data. Every organization is grappling with complex data policies as rules and regulations around that data are ever-changing and increasing in number. Immuta empowers data teams by automating the discovery and classification of new and existing data to speed time to value; orchestrating the enforcement of data policies through Policy-as-code (PaC), data masking, and Privacy Enhancing Technologies (PETs) so that any technical or business owner can manage and keep it secure; and monitoring/auditing user and policy activity/history and how data is accessed through automation to ensure provable compliance. Immuta integrates with all of the leading cloud data platforms, including Snowflake, Databricks, Starburst, Trino, Amazon Redshift, Google BigQuery, and Azure Synapse. Our platform is able to transparently secure data access without impacting performance. With Immuta, data teams are able to speed up data access by 100x, decrease the number of policies required by 75x, and achieve provable compliance goals.
  • 2
    Typo Reviews
    TYPO is a data-quality solution that corrects errors at the point of entry to information systems. Typo uses AI to detect errors at the point of entry, rather than reactive tools that try to fix data errors after they have been saved. This allows for immediate corrections before they are stored and propagated into downstream systems and reports. Typo can be used in web apps, mobile apps, devices, and data integration tools. Typo can inspect data in motion as it enters an enterprise or at rest after storage. Typo provides complete oversight of data origins, points of entry and exit into information systems, including devices and APIs. The user is notified when an error is detected and given the chance to correct it. Typo uses machine learning algorithms for detecting errors. It is not necessary to implement and maintain data rules.
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