Compare the Top Data Fabric Software for Windows as of September 2026

What is Data Fabric Software for Windows?

Data fabric software is a unified data management platform that provides seamless integration, access, and governance across an organization’s entire data ecosystem, regardless of where the data is stored—on-premises, in the cloud, or in hybrid environments. Data fabric software aims to simplify and optimize data integration, management, and analytics by using advanced automation, artificial intelligence (AI), and machine learning (ML) technologies. It helps businesses ensure data consistency, accessibility, and security across multiple data sources, enabling faster and more informed decision-making. By creating a connected data environment, data fabric software improves data quality, accelerates time-to-insight, and enhances overall operational efficiency. Compare and read user reviews of the best Data Fabric software for Windows currently available using the table below. This list is updated regularly.

  • 1
    SCIKIQ

    SCIKIQ

    SCIKIQ

    Recognized among the Top 34 AI-Augmented platforms globally by Forrester and India’s Top 10 DeepTech companies in AI & Analytics by NASSCOM, SCIKIQ is an AI-native Enterprise Data Fabric built ground-up for AI. SCIKIQ creates a unified, intelligent data layer across SAP, databases, data warehouses, data lakes, cloud, APIs and enterprise applications—without migration, replatforming or replacing the existing data stack. SCIKIQ Data Fabric combines Data Integration, ETL/ELT, Data Pipelines, Data Quality, Data Governance, Data Catalog, Metadata Management, Data Lineage, Master Data Management, Data Observability and Data Semantics. Its world-leading Data Semantics practice connects technical data with business context, KPIs, relationships and meaning—creating an AI-ready enterprise data foundation. Built for Data Fabric, Data Management, Business Intelligence, Analytics, Generative AI and Agentic AI.
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  • 2
    MANTA

    MANTA

    Manta

    Manta is the world-class automated approach to visualize, optimize, and modernize how data moves through your organization through code-level lineage. By automatically scanning your data environment with the power of 50+ out-of-the-box scanners, Manta builds a powerful map of all data pipelines to drive efficiency and productivity. Visit manta.io to learn more. With Manta platform, you can make your data a truly enterprise-wide asset, bridge the understanding gap, enable self-service, and easily: • Increase productivity • Accelerate development • Shorten time-to-market • Reduce costs and manual effort • Run instant and accurate root cause and impact analyses • Scope and perform effective cloud migrations • Improve data governance and regulatory compliance (GDPR, CCPA, HIPAA, and more) • Increase data quality • Enhance data privacy and data security
  • 3
    Domo

    Domo

    Domo

    Domo’s AI and data platform business is now part of Progress Software. Domo's cloud-native AI data readiness platform complements and significantly broadens Progress' data platform offerings, creating powerful synergies to deliver innovative, secure and scalable AI data readiness solutions worldwide. Together, these capabilities will help customers turn fragmented enterprise data and knowledge into governed, AI-ready intelligence, improving the security, governance and cost of AI-powered initiatives. Domo is the agentic platform for the intelligent enterprise, helping organizations connect, govern, activate, and distribute data and AI. Domo works with cloud data platforms including Snowflake, BigQuery, and Databricks to turn governed data into AI agents, apps, workflows, dashboards, and analytics. Its data foundation, activation, and distribution layers help teams build and deliver intelligence where work happens, with governance, security, and access controls across data and AI.
  • 4
    Dagster

    Dagster

    Dagster Labs

    Dagster is a next-generation orchestration platform for the development, production, and observation of data assets. Unlike other data orchestration solutions, Dagster provides you with an end-to-end development lifecycle. Dagster gives you control over your disparate data tools and empowers you to build, test, deploy, run, and iterate on your data pipelines. It makes you and your data teams more productive, your operations more robust, and puts you in complete control of your data processes as you scale. Dagster brings a declarative approach to the engineering of data pipelines. Your team defines the data assets required, quickly assessing their status and resolving any discrepancies. An assets-based model is clearer than a tasks-based one and becomes a unifying abstraction across the whole workflow.
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  • 5
    Cinchy

    Cinchy

    Cinchy

    Cinchy is a data platform that enables teams, systems, and AI to securely manage and share data across the organization in real time—eliminating the need for complex and costly integrations. With simplified data management and real-time bidirectional updates across the network, Cinchy streamlines ERP and CRM consolidations, accelerates digital transformation, maximizes AI, and provides a single view customer 360, all with embedded data level access controls and governance.
  • 6
    DataBahn

    DataBahn

    DataBahn

    DataBahn.ai is redefining how enterprises manage the explosion of security and operational data in the AI era. Our AI-powered data pipeline and fabric platform helps organizations securely collect, enrich, orchestrate, and optimize enterprise data—including security, application, observability, and IoT/OT telemetry—for analytics, automation, and AI. With native support for over 400 integrations and built-in enrichment capabilities, DataBahn streamlines fragmented data workflows and reduces SIEM and infrastructure costs from day one. The platform requires no specialist training, enabling security and IT teams to extract insights in real time and adapt quickly to new demands. We've helped Fortune 500 and Global 2000 companies reduce data processing costs by over 50% and automate more than 80% of their data engineering workloads.
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