
AnalyticsCreator is a metadata-driven design application for data warehouse automation and data product engineering across the Microsoft data stack.
Its Governed Control Model connects business meaning, data structures, transformation rules, dependencies, lineage and technical implementation in one controlled project model. Data teams design the required architecture in AnalyticsCreator, then generate native Microsoft assets from that design.
Generated outputs can include SQL Server objects, SSIS packages, Azure Data Factory pipelines, supported Microsoft Fabric components, deployment artefacts and Power BI semantic models. AnalyticsCreator supports dimensional, 3NF and hybrid modelling approaches together with ingestion, transformations, delta loading, historisation, Slowly Changing Dimensions, snapshots and repeatable data-processing patterns.
Because generated outputs are native Microsoft technology, no AnalyticsCreator runtime is required in production. Organisations retain ownership of the resulting implementation and can integrate generated assets into Git, Azure DevOps and CI/CD workflows.
Lineage, documentation and dependency information remain connected to the design, helping teams understand change impact before regenerating affected assets.
Design Intelligence extends this governed project context into AI-assisted data engineering by providing authorised AI tools and agents with structured access to metadata, lineage, dependencies and design rules.
Typical use cases include enterprise data warehouse development, Microsoft Fabric adoption, SQL Server and SSIS modernisation, governed Power BI delivery and repeatable data product engineering.
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Experience Semarchy’s flexible unified data platform to empower better business decisions enterprise-wide.
With xDM, you can discover, govern, enrich, enlighten and manage data. Rapidly deliver data-rich applications with automated master data management and transform data into insights with xDM.
The business-centric interfaces provide for the rapid creation and adoption of data-rich applications. Automation rapidly generates applications to your specific requirements, and the agile platform quickly expands or evolves data applications.
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Google GenAI SDK
The Gemini API libraries offer official, production-ready SDKs from Google for utilizing the Gemini API in various widely-used programming languages. Google advises developers to utilize the Google GenAI SDK for their Gemini projects, as these libraries are crafted and supported by Google, featured in official documentation and examples, and are suitable for production environments. The available SDKs encompass Python, JavaScript/TypeScript, Go, Java, and C#, with convenient installation via standard package managers like pip for Google GenAI, npm for Google GenAI, Maven for Google GenAI, and dotnet for adding the Google GenAI package. These SDKs provide access to the most recent features of the Gemini API and are optimized for superior performance when handling Gemini models. Due to the lack of ongoing support for older libraries, Google strongly encourages transitioning to the new Google GenAI SDK for a more reliable development experience, ensuring that developers can leverage the best tools available for their needs. Moreover, adopting the latest SDK not only enhances performance but also aligns with future updates and improvements from Google.
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Pecan
Founded in 2018, Pecan is a predictive analytics platform that leverages its pioneering Predictive GenAI to remove barriers to AI adoption, making predictive modeling accessible to all data and business teams.
Guided by generative AI, companies can obtain precise predictions across various business domains without the need for specialized personnel.
Predictive GenAI enables rapid model definition and training, while automated processes accelerate AI implementation. With Pecan's fusion of predictive and generative AI, realizing the business impact of AI is now far faster and easier.
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