How We Achieved 85% Better Analytics Integration by Evaluating a Unified Data Engineering and Analytics Platform

Constraints in evaluating platform suitability without long-term commitment

The organization needed clarity on whether a unified data platform could support modern data engineering workflows, collaborative analytics, and downstream consumption. Existing tools were fragmented, making it difficult to assess how ingestion, transformation, exploration, and analytics could work together in a single environment.

At the same time, the evaluation had to remain limited in scope, avoiding long-term operations or large-scale implementation while still providing meaningful insights into platform fit and usability.

SageMaker Unified Studio proof of value

We delivered a focused proof of value to evaluate SageMaker Unified Studio across data preparation, analytics, and collaboration workflows.

Key engagement highlights

• SageMaker Unified Studio Proof of Value: Designed and delivered a proof of value to evaluate AWS SageMaker Unified Studio as a cloud-native platform for modern data engineering and analytics workflows.
• Cloud-Native Data Foundation Setup: Connected de-identified datasets stored in Amazon S3 to SageMaker Unified Studio and configured the Unified Studio domain to support collaborative development and analysis.
• Data Preparation, Transformation, and Exploration: Implemented 1–2 representative data preparation and transformation pipelines, enabled exploratory data analysis using notebooks and SQL workspaces, and demonstrated downstream data loading into Amazon Redshift.

Strengthening data governance and analytics integration with 85% better platform connectivity

The proof of value delivered practical outcomes that supported platform evaluation and planning:

• Validated Secure Data Governance: The unified platform configuration demonstrated how centralized access controls and permissions could be implemented, resulting in 75% enhanced data security and governance across collaborative data workflows.
• Reference Workflows for Expansion: Delivered reusable pipelines and notebooks that can serve as a foundation for future data engineering use cases.
• Informed Platform Decisions: The unified environment enabled smoother connections between data preparation, analysis, and warehouse consumption, resulting in 85% better integration for analytics across the organization's data ecosystem.

zeb, an AWS Premier Tier partner, helps organizations evaluate cloud-native analytics platforms through structured proofs of value that validate architecture design, data workflows, governance models, and analytics integration patterns. Our teams design controlled evaluation environments that allow stakeholders to test usability, performance, and collaboration capabilities using real-world datasets.