Tom Sawyer Data Streams
Real-time ETL knowledge graph platform
OVERVIEW
Tom Sawyer Data Streams is a schema-driven platform that unifies real-time and legacy data sources—such as Apache Kafka streams and relational databases—into a governed, query-ready knowledge graph.
My role as the UI/UX designer was to make this technical, real-time integration platform accessible and intuitive, especially for data architects and engineers working at scale. I collaborated closely with developers, product managers, and data architects to deliver a clean, visually guided experience.
THE CHALLENGE
Complexity of Integration. Streaming architectures (e.g., Kafka) and legacy systems often live in silos. Mapping these systems into a unified graph involves complex transformations, schema definitions, and real-time logic.
Steep Technical Barrier. Defining data pipelines, transformations, and schema relationships often requires deep expertise in code, ETL tools, or query languages. This slows adoption.
Lack of Visibility & Governance. Without a visual, schema-driven approach, it’s difficult to enforce data quality, lineage, and business rules in real time. Architects risk creating inconsistent or ungoverned graph models.
User Experience Friction. Data architects and engineers need to design, validate, and maintain data flows. A purely code-based interface can make it hard to reason visually, test flows, and iterate quickly.
MY PROCESS
User Research
Synthesized insights from data architect interviews, product documentation audits, and competitor benchmarking to map complex integration patterns—such as Kafka and CDC—into optimized requirements for visual flow design and schema-driven transformations.
Problem Statement
Translated user needs for data visibility and schema integrity into a comprehensive requirement set, featuring a visual flow editor, real-time previews, SpEL transformation UI, and integrated deployment controls to ensure validated graph commits.
Test & Validate
Refined the user experience based on targeted feedback, implementing inline validation, schema graph views, and transformation previews to enhance the clarity of error messaging and complex expression editing.
Collaborate & Deliver
Embedded within the agile lifecycle—from sprint planning and backlog grooming to QA and documentation—to prioritize high-impact features and ensure technical resilience against complex edge cases like malformed data.
OUTCOME
Validated Visual Logic. User testing confirmed that the visual-first approach significantly reduced mental load and the learning curve for complex real-time data integration.
Accelerated Development. Design prototypes streamlined technical alignment, reducing architectural ambiguity and establishing UI patterns now adopted across the entire Modern Web Framework.
Enhanced Market Positioning. Pre-launch feedback established a forward-looking product direction, signaling a commitment to usability that strengthened the company’s competitive edge.
Strategic Growth Potential. Early adoption patterns suggest the tool will become a core platform component, specifically addressing enterprise needs for schema governance and ETL simplification.