Case study at a glance
About this case study: this is an Infinite360 Tech Academy project case study delivered on a simulation environment with synthetic data. It shows the scope, architecture and approach our consultants and learners practise. It is not a client engagement and implies no affiliation with, or endorsement by, any company or brand.
| Industry | Cross-industry |
|---|---|
| Platforms | AEP, Query Service, Real-Time CDP, Web SDK |
| Type | Academy project case study (simulated environment, synthetic data) |
| Roles practised | AEP developer, data engineer, solution architect |
| Environment | aepsimualtor.infiante360techacademy.com |
The business challenge
Teams new to Adobe Experience Platform often configure features in isolation and discover late that schemas, identities and datasets do not fit together. This project builds the platform in the right order so each step can be validated before the next.
Target architecture
Implementation approach
- Use cases & backlog — dependencies first
- XDM schemas — profile + events
- Identities — namespaces, primary
- Ingestion — batch + streaming
- Validate — Query Service
- Segment & activate — audiences → destinations
Use cases implemented
- Customer profile and Experience Event schemas with reusable data types
- Identity namespaces and primary identity selection
- Batch ingestion through workflows and streaming through datastreams
- Query Service checks for counts, duplicates and nulls
- Batch, streaming and edge audiences
- Activation to a file-based and an advertising destination
Validation and governance checks
- Reusable data types shared between profile and event field groups
- Profile enablement switched on only after schema review, because it cannot be reversed
- Record counts and distinct IDs reconciled with source files
- Profile fragments and identity graph inspected for test customers
- Audience estimates compared with Query Service results
What Infinite360 brings
Architecture first
A documented target architecture, identity strategy and data model before a single journey is built.
Reusable accelerators
Schema patterns, naming conventions, backlog templates, QA checklists and runbooks reused across projects.
Validation discipline
Every stage is validated - payloads, identity graphs, profile fragments, audience counts and journey test runs.
Governance built in
Consent, data usage labels and least-privilege access are designed in, not bolted on at go-live.
Knowledge transfer
Walkthrough recordings, documentation and pairing so your team can run and extend the solution.
Day-one-ready people
Consultants and contract engineers who have practised the same pattern end to end before joining your team.
From case study to contract delivery
The roles that practise this case study — AEP developer, data engineer, solution architect — are the same profiles we place through contract hiring and staff augmentation. Because they have already built this pattern end to end, onboarding focuses on your systems and standards rather than on learning the platform.
Frequently asked questions
What order should an AEP implementation follow?
Use cases, schema design, identity strategy, datasets, ingestion, profile stitching, consent and governance, segmentation, destinations, activation, QA and go-live.
Can I practise this without a client sandbox?
Yes. The AEP Simulator and training sandboxes are provided to enrolled learners.
Last updated: