Best Data Quality Software for Fivetran

Find and compare the best Data Quality software for Fivetran in 2026

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

  • 1
    Segment Reviews

    Segment

    Twilio

    $120 per month
    2 Ratings
    Twilio Segment’s Customer Data Platform (CDP) provides companies with the data foundation that they need to put their customers at the heart of every decision. Using Twilio Segment, companies can collect, unify and route their customer data into any system. Over 25,000 companies use Twilio Segment to make real-time decisions, accelerate growth and deliver world-class customer experiences.
  • 2
    Sifflet Reviews
    Effortlessly monitor thousands of tables through machine learning-driven anomaly detection alongside a suite of over 50 tailored metrics. Ensure comprehensive oversight of both data and metadata while meticulously mapping all asset dependencies from ingestion to business intelligence. This solution enhances productivity and fosters collaboration between data engineers and consumers. Sifflet integrates smoothly with your existing data sources and tools, functioning on platforms like AWS, Google Cloud Platform, and Microsoft Azure. Maintain vigilance over your data's health and promptly notify your team when quality standards are not satisfied. With just a few clicks, you can establish essential coverage for all your tables. Additionally, you can customize the frequency of checks, their importance, and specific notifications simultaneously. Utilize machine learning-driven protocols to identify any data anomalies with no initial setup required. Every rule is supported by a unique model that adapts based on historical data and user input. You can also enhance automated processes by utilizing a library of over 50 templates applicable to any asset, thereby streamlining your monitoring efforts even further. This approach not only simplifies data management but also empowers teams to respond proactively to potential issues.
  • 3
    Mozart Data Reviews
    Mozart Data is the all-in-one modern data platform for consolidating, organizing, and analyzing your data. Set up a modern data stack in an hour, without any engineering. Start getting more out of your data and making data-driven decisions today.
  • 4
    DataOps.live Reviews
    Create a scalable architecture that treats data products as first-class citizens. Automate and repurpose data products. Enable compliance and robust data governance. Control the costs of your data products and pipelines for Snowflake. This global pharmaceutical giant's data product teams can benefit from next-generation analytics using self-service data and analytics infrastructure that includes Snowflake and other tools that use a data mesh approach. The DataOps.live platform allows them to organize and benefit from next generation analytics. DataOps is a unique way for development teams to work together around data in order to achieve rapid results and improve customer service. Data warehousing has never been paired with agility. DataOps is able to change all of this. Governance of data assets is crucial, but it can be a barrier to agility. Dataops enables agility and increases governance. DataOps does not refer to technology; it is a way of thinking.
  • 5
    Foundational Reviews
    Detect and address code and optimization challenges in real-time, mitigate data incidents before deployment, and oversee data-affecting code modifications comprehensively—from the operational database to the user interface dashboard. With automated, column-level data lineage tracing the journey from the operational database to the reporting layer, every dependency is meticulously examined. Foundational automates the enforcement of data contracts by scrutinizing each repository in both upstream and downstream directions, directly from the source code. Leverage Foundational to proactively uncover code and data-related issues, prevent potential problems, and establish necessary controls and guardrails. Moreover, implementing Foundational can be achieved in mere minutes without necessitating any alterations to the existing codebase, making it an efficient solution for organizations. This streamlined setup promotes quicker response times to data governance challenges.
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