Modern Data Platforms and AI-Ready Data on AWS by Assembly

Modern data platforms on AWS by Assembly

Assembly is an AWS Advanced Tier Services Partner. Fragmented data means every report and every model starts with the same expensive archaeology. We plan, build and govern one well-modelled platform on AWS, so analytics and AI build on the same foundations.

Relation to AWS services This professional service designs and builds data platforms in your AWS account on Amazon S3 (including Amazon S3 Tables), AWS Glue, Amazon Redshift, AWS Lake Formation and Amazon Kinesis Data Streams. Analytics uses Amazon Quick Sight, Amazon Athena and Amazon SageMaker AI; governance uses Amazon SageMaker, AWS Glue Data Quality, Amazon Macie and Amazon Bedrock AgentCore Gateway for governed access by AI agents.

Four services, one AWS team

• Data strategy and architecture (typically 3-5 weeks): Discover, Assess, Design, Plan. A current-state assessment, a target AWS data architecture covering ingestion, storage, processing and consumption, an operating model and a costed roadmap.
• Modern data platform (typically 6-10 weeks): Design, Build, Model, Operate. Batch and streaming ingestion, storage, transformation and catalogue as code, curated and documented datasets, and monitoring, alerting and cost controls.
• Analytics and decision intelligence (typically 4-8 weeks): Frame, Define, Build, Adopt. Decision mapping, agreed metric definitions and a semantic layer, dashboards in Amazon Quick Sight or your existing BI tool, and forecasting, segmentation or anomaly detection where they change what you do.
• Data governance and AI readiness (typically 4-6 weeks): Assess, Implement, Connect, Govern. Catalogue, lineage, quality tests, classification and fine-grained access, governed datasets exposed to AI agents as MCP tools with each person's own permissions, and a policy for AI use of data.

Why it matters

• Analysts spend most of their time fixing data when the platform is unreliable.
• One well-modelled platform ends the meetings where two teams present different numbers.
• AI amplifies whatever your data already is; quality, ownership and sensitivity need to be known first.

What you receive

• A target AWS data architecture and costed roadmap
• A production data platform with automated ingestion and transformation pipelines
• Curated, documented datasets and a semantic layer
• Production dashboards, with enablement for your teams
• A data catalogue with owners and lineage, quality checks and access controls
• Governed MCP tools for your AI agents

Proof Project OceanOasis turned reef sensor readings into a live conservation dashboard on AWS, with p95 ingestion under a second in load tests.

Important

• Getting your data ready for AI? Start with a use case and readiness assessment, funded by AWS for companies that qualify, subject to AWS approval. We check your fit with you before anything goes to AWS.
• You are responsible for any AWS infrastructure and service charges incurred in your AWS account, billed separately from your AWS Marketplace transaction.