Replication Flows
I supported the UX design for Replication Flows by crafting user journeys, building prototypes in UI5 using Sketch and Figma, conducting usability tests, and collaborating with PM, Engineering, and QA before handing off to a successor designer.
Jan 25, 2021
CLIENT
SAP
CLIENT
SAP
CLIENT
SAP
Role
Designer
Role
Designer
Role
Designer
Service
UX Design
Service
UX Design
Service
UX Design
Introduction: Setting the Scene
Introduction: Setting the Scene
Introduction: Setting the Scene
Product Context
Part of the Data Builder in SAP Datasphere, Replication Flows provide high-performance data copying from source to target systems. They complement Data Flows and Transformation Flows by focusing on reliable, governed replication.
Problem Statement
Users lacked a straightforward way to replicate large datasets from ABAP and other systems into SAP Datasphere. Existing flows either required transformation steps (Data/Transformation Flows) or external ETL tools, leading to confusion, inefficiency, and governance gaps.
My Role & Responsibilities
Initiated the UX design for Replication Flows.
Defined user journeys for replication-specific use cases.
Created prototypes in Figma/UI5 for flow canvas and replication settings.
Collaborated with PM + Engineering to prioritize and refine requirements.
Conducted usability testing with Data Engineers.
Handed over finalized design specs to another designer upon leaving the team.
Tools & Methods
Figma for design explorations and UI prototypes.
UI5 prototyping for interaction testing.
Moderated usability testing to validate mental models and settings clarity.
Product Context
Part of the Data Builder in SAP Datasphere, Replication Flows provide high-performance data copying from source to target systems. They complement Data Flows and Transformation Flows by focusing on reliable, governed replication.
Problem Statement
Users lacked a straightforward way to replicate large datasets from ABAP and other systems into SAP Datasphere. Existing flows either required transformation steps (Data/Transformation Flows) or external ETL tools, leading to confusion, inefficiency, and governance gaps.
My Role & Responsibilities
Initiated the UX design for Replication Flows.
Defined user journeys for replication-specific use cases.
Created prototypes in Figma/UI5 for flow canvas and replication settings.
Collaborated with PM + Engineering to prioritize and refine requirements.
Conducted usability testing with Data Engineers.
Handed over finalized design specs to another designer upon leaving the team.
Tools & Methods
Figma for design explorations and UI prototypes.
UI5 prototyping for interaction testing.
Moderated usability testing to validate mental models and settings clarity.
Product Context
Part of the Data Builder in SAP Datasphere, Replication Flows provide high-performance data copying from source to target systems. They complement Data Flows and Transformation Flows by focusing on reliable, governed replication.
Problem Statement
Users lacked a straightforward way to replicate large datasets from ABAP and other systems into SAP Datasphere. Existing flows either required transformation steps (Data/Transformation Flows) or external ETL tools, leading to confusion, inefficiency, and governance gaps.
My Role & Responsibilities
Initiated the UX design for Replication Flows.
Defined user journeys for replication-specific use cases.
Created prototypes in Figma/UI5 for flow canvas and replication settings.
Collaborated with PM + Engineering to prioritize and refine requirements.
Conducted usability testing with Data Engineers.
Handed over finalized design specs to another designer upon leaving the team.
Tools & Methods
Figma for design explorations and UI prototypes.
UI5 prototyping for interaction testing.
Moderated usability testing to validate mental models and settings clarity.
The Design Journey
The Design Journey
The Design Journey
Design Process
Key Findings:
Users conflated replication with transformation, leading to confusion in earlier flows.
They wanted replication to be “set it and trust it”—with reliability and monitoring.
Iterations:
Attempted embedding replication into existing flows → rejected for being too complex.
Pivoted to a new flow type dedicated to replication, with simplified UI.
Final Design:
A replication-focused flow with:
Source and Target nodes on the familiar canvas.
Replication-specific configuration panel (initial load, delta load).
Monitoring hooks for reliability.
Key Deliverables
Figma prototypes and UI5 demos.
Usability testing reports.
Annotated design specifications for Engineering.
Challenges & Resolutions
Challenge: Distinguishing Replication Flows from Transformation Flows.
Resolution: Scoped replication strictly to copying data—without transformations—while still providing flexibility in scheduling and monitoring.
Impact & Results
Metrics: (Not yet available).
Qualitative Feedback: Users in the SAP community highlight Replication Flows as “indispensable for ABAP to Datasphere replication.”
Strategic Value: Clearer flow taxonomy (Replication vs. Transformation vs. Data Flow) reduced user confusion and streamlined adoption.
Design Process
Key Findings:
Users conflated replication with transformation, leading to confusion in earlier flows.
They wanted replication to be “set it and trust it”—with reliability and monitoring.
Iterations:
Attempted embedding replication into existing flows → rejected for being too complex.
Pivoted to a new flow type dedicated to replication, with simplified UI.
Final Design:
A replication-focused flow with:
Source and Target nodes on the familiar canvas.
Replication-specific configuration panel (initial load, delta load).
Monitoring hooks for reliability.
Key Deliverables
Figma prototypes and UI5 demos.
Usability testing reports.
Annotated design specifications for Engineering.
Challenges & Resolutions
Challenge: Distinguishing Replication Flows from Transformation Flows.
Resolution: Scoped replication strictly to copying data—without transformations—while still providing flexibility in scheduling and monitoring.
Impact & Results
Metrics: (Not yet available).
Qualitative Feedback: Users in the SAP community highlight Replication Flows as “indispensable for ABAP to Datasphere replication.”
Strategic Value: Clearer flow taxonomy (Replication vs. Transformation vs. Data Flow) reduced user confusion and streamlined adoption.
Design Process
Key Findings:
Users conflated replication with transformation, leading to confusion in earlier flows.
They wanted replication to be “set it and trust it”—with reliability and monitoring.
Iterations:
Attempted embedding replication into existing flows → rejected for being too complex.
Pivoted to a new flow type dedicated to replication, with simplified UI.
Final Design:
A replication-focused flow with:
Source and Target nodes on the familiar canvas.
Replication-specific configuration panel (initial load, delta load).
Monitoring hooks for reliability.
Key Deliverables
Figma prototypes and UI5 demos.
Usability testing reports.
Annotated design specifications for Engineering.
Challenges & Resolutions
Challenge: Distinguishing Replication Flows from Transformation Flows.
Resolution: Scoped replication strictly to copying data—without transformations—while still providing flexibility in scheduling and monitoring.
Impact & Results
Metrics: (Not yet available).
Qualitative Feedback: Users in the SAP community highlight Replication Flows as “indispensable for ABAP to Datasphere replication.”
Strategic Value: Clearer flow taxonomy (Replication vs. Transformation vs. Data Flow) reduced user confusion and streamlined adoption.