Reduction in Time-to-Insight:
Rapidly launch new analytics and AI workloads on AWS without waiting for lengthy data migration projects.
TCO Savings on Data Operations:
Optimize cloud spend by keeping cold data in open formats on Amazon S3 while leveraging push-down execution for high-performance workloads.
Policy Enforcement for AI:
Eliminate security risks by applying consistent access rules across all prompts and RAG pipelines powered by Amazon Bedrock.
Enterprise data landscapes on AWS are growing exponentially, but value often gets bottlenecked by fragmented governance, complex data movement across legacy stacks, and slow model deployment pipelines.
NuoData enhances your AWS investment by overlaying an open, intelligent metadata and execution mesh. We allow teams to query, model, and deploy AI workloads on AWS resources while keeping non-AWS enterprise sources fully visible, governed, and connected.
Harness Amazon Bedrock and SageMaker through NuoData's Nova and Nora AI agent engines to securely ground LLMs on enterprise enterprise context.
A property and casualty insurer receives 15,000 claims a month as emails, PDFs and photos.
An enterprise data team runs 3,000 daily pipelines, and failures consume 40% of engineering time.
A mid-size bank’s analysts each spend 2–3 hours on every anti-money-laundering alert.
The Challenge:
Moving petabytes of legacy or cross-cloud data into AWS S3 takes months and incurs severe egress costs.
The Solution:
NuoData Quantum connects on-premises and multi-cloud sources to Amazon S3 via no-code pipelines and open formats (Iceberg, Parquet), reducing data movement. Data flows into Amazon Redshift or QuickSight for analytics, with NuoData Astra tracking lineage.
The Challenge:
Enterprise LLM applications require secure, real-time context from enterprise databases without risking data leakage or policy violations.
The Solution:
NuoData Nova builds and deploys RAG and GenAI applications on Amazon Bedrock and SageMaker, with NuoData Nora running governed AI agents. NuoData Astra supplies semantic context and lineage, while NuoData Halo enforces RBAC/ABAC and masks restricted data before prompts reach the model.
The Challenge:
Complex AWS Glue pipelines and Redshift transformations lack end-to-end visibility across legacy upstream dependencies.
The Solution:
NuoData Spectra provides automated data quality scoring and real-time observability across AWS data pipelines, while Astra maps column-level lineage from legacy sources through S3 to AWS consumption layers.
Direct read/write support for open lakehouse formats (Apache Iceberg, Delta) with automated metadata synchronization via NuoData Astra.
Bidirectional synchronization of security policies, table definitions, and access permissions.
Push-down query execution and automated data transformation layer via NuoData Quantum.
Native connectors for NuoData Nova and Nora, enabling Agentic AI workflows with strict enterprise guardrails via NuoData Halo.
Fine-grained access management delegation, mapping enterprise IAM roles directly to NuoData policy engines.
Speak with a NuoData AWS Solution Architect to learn how you can unify your data stack and accelerate AI adoption on AWS.
