
AnalyticsCreator helps Microsoft data teams turn governed design into deployable data solutions without introducing a proprietary runtime layer.
Teams use AnalyticsCreator to define warehouse structures, transformation logic, historisation rules, relationships and dependencies in a central model. From that model, the application can generate native implementation assets for technologies such as SQL Server, SSIS, Azure Data Factory, Microsoft Fabric and Power BI.
The approach is designed for organisations that want to standardise how data warehouses and data products are engineered while keeping full control of the resulting code and project artefacts. Generated outputs can be integrated into existing Git, Azure DevOps and CI/CD workflows for versioning, review and controlled deployment across environments.
AnalyticsCreator supports dimensional, 3NF and hybrid modelling as well as common engineering patterns including delta loading, Slowly Changing Dimensions, snapshots and historisation. Documentation, lineage and dependency information are maintained alongside the project design, making it easier to assess the impact of proposed changes and keep implementation aligned with the underlying model.
The AnalyticsCreator Governed Control Model provides the foundation for this process by keeping business meaning, technical structures and implementation logic connected. Design Intelligence builds on that context by making governed project metadata, lineage, dependencies and design rules available to authorised AI tools and agents.
Typical use cases include modernising SQL Server and SSIS estates, building Microsoft Fabric solutions, standardising Power BI delivery and creating repeatable data warehouse and data product engineering processes.
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IONOS provides GPU Servers that create a powerful computing environment tailored for handling tasks requiring much greater power than conventional CPU systems can offer. This setup includes high-quality NVIDIA GPUs, such as the H100, H200, and L40s, alongside dedicated AI accelerators like Intel Gaudi, which support extensive parallel processing for resource-intensive applications. With GPU-accelerated instances, the cloud infrastructure is further improved by integrating dedicated graphical processors, allowing virtual machines to perform complex calculations and manage data-heavy operations considerably more swiftly than standard servers. This solution is particularly advantageous in sectors like artificial intelligence, deep learning, and data science, where it is crucial to train models on large datasets or conduct fast inference processes. Additionally, it supports big data analytics, scientific simulations, and visualization tasks requiring significant computational strength, such as 3D rendering and modeling. Consequently, organizations aiming to enhance their processing power for intricate workloads can reap substantial benefits from this sophisticated infrastructure, making it an ideal choice for modern computational demands. Moreover, the flexibility of this service allows businesses to scale their resources according to project requirements, ensuring efficient performance across various applications.
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Cloudera Data Visualization
Craft dynamic and engaging dashboards to boost your analytical capabilities across your organization.
Cloudera Data Visualization empowers data engineers, scientists, and business analysts to investigate data, collaborate, and disseminate insights throughout every phase of the data lifecycle, from ingestion to actionable insights. As a core component of Cloudera, this tool offers a seamless and user-friendly experience for data visualization through intuitive drag-and-drop functionality for dashboards and custom applications.
With SDX, Data Visualization is fortified with robust security, facilitating improved data workflows in all analytics processes. In addition, Cloudera Machine Learning can be utilized to develop predictive applications, or alternatively, you can tap into your data warehouse for rapid and intelligent reporting solutions. Overall, this suite of tools enhances decision-making capabilities within the enterprise.
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GoodData.AI
GoodData is an enterprise analytics platform designed to help organizations build AI-ready business intelligence, embedded analytics, and intelligent decision-making solutions on top of trusted business data. The platform combines governed semantics, scalable analytics, and open architecture to create a consistent foundation for reporting, dashboards, AI assistants, and automated workflows. Its semantic layer defines business metrics once and reuses them across applications, reducing inconsistencies while improving confidence in analytical results. Businesses can embed analytics directly into their own software products, customer portals, and operational systems to deliver contextual insights where users work. GoodData also supports conversational analytics, AI orchestration, and agentic workflows that help users analyze trends, explain changes, and recommend next actions. The platform integrates with modern cloud data warehouses and enterprise technology stacks while providing flexible deployment in cloud, hybrid, or on-premises environments. Developers can extend the platform using APIs, SDKs, analytics-as-code tools, and automation capabilities to build highly customized analytics experiences. Enterprise security features and compliance certifications, including HIPAA, GDPR, ISO 27001, SOC 2 Type II, and FedRAMP support organizations operating in regulated industries. Performance optimization features such as query acceleration and scalable architecture allow businesses to analyze growing volumes of data efficiently. Professional services and forward-deployed engineering teams help organizations design, implement, and optimize analytics solutions tailored to their business requirements. GoodData enables enterprises to modernize legacy BI environments while creating a trusted foundation for AI-powered analytics and embedded decision intelligence.
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