Teradata VantageCloud: Open, Scalable Cloud Analytics for AI
VantageCloud is Teradata’s cloud-native analytics and data platform designed for performance and flexibility. It unifies data from multiple sources, supports complex analytics at scale, and makes it easier to deploy AI and machine learning models in production. With built-in support for multi-cloud and hybrid deployments, VantageCloud lets organizations manage data across AWS, Azure, Google Cloud, and on-prem environments without vendor lock-in. Its open architecture integrates with modern data tools and standard formats, giving developers and data teams freedom to innovate while keeping costs predictable.
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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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Cribl Edge
Introducing your discreet partner for a vendor-neutral unified data collection solution: Cribl Edge is a smart and highly scalable system designed for edge-based data collection, encompassing logs, metrics, and application information. With features such as automatic log discovery and metrics generation, Cribl Edge caters to the needs of modern distributed microservice architectures. You can easily deploy, configure, and manage your agents from a central location, allowing for seamless scalability and reduced resource overhead while maintaining the flexibility to avoid vendor lock-in through agnostic integrations. Transitioning away from numerous outdated agents, you can eliminate overlapping proprietary systems to simplify your operations, minimize attack surfaces, and lower expenses. Bid farewell to tedious manual upgrades by empowering your team with a centralized platform for agent management. Moreover, effectively handle your widespread infrastructure, enabling the swift deployment and monitoring of thousands of nodes within days rather than weeks, ultimately streamlining your entire data management process.
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Data Lakes on AWS
Numerous customers of Amazon Web Services (AWS) seek a data storage and analytics solution that surpasses the agility and flexibility of conventional data management systems. A data lake has emerged as an innovative and increasingly favored method for storing and analyzing data, as it enables organizations to handle various data types from diverse sources, all within a unified repository that accommodates both structured and unstructured data. The AWS Cloud supplies essential components necessary for customers to create a secure, adaptable, and economical data lake. These components comprise AWS managed services designed to assist in the ingestion, storage, discovery, processing, and analysis of both structured and unstructured data. To aid our customers in constructing their data lakes, AWS provides a comprehensive data lake solution, which serves as an automated reference implementation that establishes a highly available and cost-efficient data lake architecture on the AWS Cloud, complete with an intuitive console for searching and requesting datasets. Furthermore, this solution not only enhances data accessibility but also streamlines the overall data management process for organizations.
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