Best Data Catalog Software for Google Cloud Pub/Sub

Find and compare the best Data Catalog software for Google Cloud Pub/Sub in 2024

Use the comparison tool below to compare the top Data Catalog software for Google Cloud Pub/Sub on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.

  • 1
    Google Cloud Data Catalog Reviews

    Google Cloud Data Catalog

    Google

    $100 per GiB per month
    Fully managed and highly scalable metadata and data discovery service. New customers receive $300 in Google Cloud credits for free during the Free Trial. All customers receive up to 1 MiB business or ingested meta data storage and 1,000,000 API calls free of charge. A simple, but powerful faceted search interface allows you to pinpoint your data. Automatically sync technical metadata and create schematized tags to support business metadata. Cloud Data Loss Prevention integration allows you to automatically tag sensitive data. Access your data immediately and scale without the need to manage or set up infrastructure. With a powerful UI built with the same search technology that Gmail or API access, empower any member of the team to find and tag data. Data Catalog is fully managed so that you can easily start and scale. Cloud IAM integrations and Cloud DLP integrations allow you to enforce data security policies and ensure compliance.
  • 2
    Validio Reviews
    Get a clear view of your data assets: popularity, usage, and schema coverage. Get important insights into your data assets, such as popularity and utilization. Find and filter data based on tags and descriptions in metadata. Get valuable insights about your data assets, such as popularity, usage, quality, and schema cover. Drive data governance and ownership throughout your organization. Stream-lake-warehouse lineage to facilitate data ownership and collaboration. Lineage maps are automatically generated at the field level to help understand the entire data ecosystem. Anomaly detection is based on your data and seasonality patterns. It uses automatic backfilling from historical data. Machine learning thresholds are trained for each data segment and not just metadata.
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