Challenges
The customer operated a complex data platform combining Apache Hudi and Apache Iceberg table formats, thousands of dbt models, configuration-driven processing, workflow orchestration, and extensive data quality monitoring.
The customer needed an evidence-based understanding of how these components supported modernization and where technical validation was required.
NuVista AI was engaged to assess cost efficiency, workload placement, Iceberg readiness, governance, and data lifecycle management.
The assessment also needed to distinguish conclusions supported by architecture and code from cost and performance assumptions that could not be validated without runtime telemetry.
NuVista AI's Solution
NuVista AI completed a three-week modernization assessment, using AIHouse to analyze architecture and code artifacts across 22+ repositories. The team inventoried the environment, examined implementation patterns, and connected findings to supporting technical evidence.
Architecture & Modernization Assessment
The review covered Amazon S3, Amazon Redshift Serverless, AWS Glue, and the AWS Glue Data Catalog, alongside dbt, PySpark, Hudi, and Iceberg processing.
NuVista AI also examined Amazon Managed Workflows for Apache Airflow (Amazon MWAA) orchestration, Amazon DynamoDB control tables, AWS Lake Formation governance, observability, continuous integration and deployment, and downstream data delivery.
Evidence-Based Modernization Decisions
The assessment documented configuration-driven Hudi-to-Iceberg conversion patterns and orchestration that operated across table formats. It identified Iceberg maintenance and snapshot lifecycle management as important operational considerations, including their relevance to GDPR-related data handling.
NuVista AI evaluated workload placement and documented the rationale for retaining dbt workloads in Amazon Redshift at the time of the assessment.
The team also distinguished architectural findings from right-sizing and workload-offload assumptions that required query-level telemetry.
The completed engagement produced six evidence-backed reports documenting the environment baseline, modernization decisions, cost and performance evidence, feasibility, artifact conversion requirements, and execution readiness.
Findings were confidence-rated and linked to the relevant architecture and code artifacts.
The Outcome
The customer received a documented assessment of its AWS data platform, replacing assumptions with traceable technical findings.
The engagement clarified existing capabilities, workload placement considerations, Iceberg operational requirements, and the evidence needed to support cost and performance decisions.
AIHouse assessed 13 modernization options across an environment containing 500+ feeds, 50+ external sources, 4,614 dbt models, 22 DynamoDB control tables, and 3,508 quality monitors.
The assessment covered seven AWS accounts, three Amazon MWAA environments, and 22+ repositories.
The six completed reports gave the customer a consolidated understanding of its architecture, modernization feasibility, and governance considerations.
By separating validated findings from unsupported assumptions, NuVista AI provided a defensible basis for technical and operational decisions without attributing unverified savings or performance improvements to the engagement.
Company:
Global Sports and Entertainment Organization
Overview:
A major sports and entertainment organization serving a global ecosystem of fans, member clubs, media organizations, and commercial partners.
Value Provided:
The customer received a documented assessment of its AWS data platform, replacing assumptions with traceable technical findings.
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