List of the Top 3 Data Warehouse Software for Qlik Cloud Analytics in 2025

Reviews and comparisons of the top Data Warehouse software with a Qlik Cloud Analytics integration


Below is a list of Data Warehouse software that integrates with Qlik Cloud Analytics. Use the filters above to refine your search for Data Warehouse software that is compatible with Qlik Cloud Analytics. The list below displays Data Warehouse software products that have a native integration with Qlik Cloud Analytics.
  • 1
    COZYROC SSIS+ Suite Reviews & Ratings

    COZYROC SSIS+ Suite

    COZYROC

    Unlock seamless data integration with powerful ETL solutions!
    The SSIS+ suite from COZYROC features over 270 data integration connectors, ETL components, and tasks designed to facilitate the creation of ETL solutions using Microsoft SQL Server Integration Services. This extensive collection empowers developers to streamline their data processing workflows efficiently.
  • 2
    OpenText Analytics Database (Vertica) Reviews & Ratings

    OpenText Analytics Database (Vertica)

    OpenText

    Unlock powerful analytics and machine learning for transformation.
    OpenText Analytics Database, formerly known as Vertica Data Platform, is a powerful analytics database designed to provide ultra-fast, scalable analysis of massive data volumes with minimal compute and storage requirements. It enables organizations to unlock real-time insights and operational efficiencies by combining high-speed analytics with integrated machine learning capabilities. The platform’s massively parallel processing (MPP) architecture ensures that complex, resource-intensive queries run efficiently regardless of dataset size. Its columnar storage format optimizes both query speed and storage utilization, significantly reducing disk I/O. OpenText Analytics Database seamlessly integrates with data lakehouse environments, supporting popular formats like Parquet, ORC, AVRO, and native ROS, providing versatile data accessibility. Users can query and analyze data using multiple languages, including SQL, R, Python, Java, and C/C++, catering to a wide range of skill sets from data scientists to business analysts. Built-in machine learning functions enable users to build, test, and deploy predictive models directly within the database, eliminating the need for data movement and accelerating time to insight. Additional in-database analytics functions cover time series analysis, geospatial queries, and event-pattern matching, providing rich data exploration capabilities. Flexible deployment options allow organizations to run the platform on-premises, in the cloud, or in hybrid setups to optimize infrastructure alignment and cost. Supported by OpenText’s professional services, training, and premium support, the Analytics Database empowers businesses to drive revenue growth, enhance customer experiences, and reduce time to market through data-driven strategies.
  • 3
    Data Virtuality Reviews & Ratings

    Data Virtuality

    Data Virtuality

    Transform your data landscape into a powerful, agile force.
    Unify and streamline your data operations. Transform your data ecosystem into a dynamic force. Data Virtuality serves as an integration platform that ensures immediate access to data, centralizes information, and enforces data governance. The Logical Data Warehouse merges both materialization and virtualization techniques to deliver optimal performance. To achieve high-quality data, effective governance, and swift market readiness, establish a single source of truth by layering virtual components over your current data setup, whether it's hosted on-premises or in the cloud. Data Virtuality provides three distinct modules: Pipes Professional, Pipes Professional, and Logical Data Warehouse, which collectively can reduce development time by as much as 80%. With the ability to access any data in mere seconds and automate workflows through SQL, the platform enhances efficiency. Additionally, Rapid BI Prototyping accelerates your time to market significantly. Consistent, accurate, and complete data relies heavily on maintaining high data quality, while utilizing metadata repositories can enhance your master data management practices. This comprehensive approach ensures your organization remains agile and responsive in a fast-paced data environment.
  • Previous
  • You're on page 1
  • Next