🌙
☀️
AWS Case Study
HealthTech
Cloud Operations
Multi-Year Retention
SoulTrps:
A Self-Managing
Retention Lifecycle
A telehealth streaming platform turns a multi-year retention obligation into a lifecycle that manages itself — where storage cost tracks access behaviour, archival runs by policy, and the retention position is provable to an auditor.
By-Policy
Archival & Expiry
3-Tier
Automated Transition
Access-Based
Cost, Not Retention
SoulTrps — Retention Lifecycle Dashboard
Classification
At Ingest
↑ SDK tagging
Archival
Automatic
↑ Lifecycle policy
Storage Cost
Access-Based
↓ Decoupled
Retention
Provable
↑ KMS + CloudTrail
Lens
Measured First
↑ Storage Lens
Batch
Historical Tagged
↑ S3 Batch Ops
Glacier
Deep Archive
↑ Long-term
01
AWS Cloud Operations Delivery
Turning a Multi-Year Retention Obligation into a Self-Managing Lifecycle
About SoulTrps
SoulTrps operates a telehealth and wellness streaming platform in India. Session records and access logs carry a multi-year compliance retention obligation, so data volume grows continuously and cannot be reduced by deletion.
The Challenge
Retention-bound data accumulating in hot storage with no lifecycle to act on it
Cost Scaling with Obligation
Compliance data accumulated in hot storage with no automated lifecycle, so cost grew in proportion to the retention period rather than reflecting how data was accessed.
Manual Archival Burden
Moving ageing data to lower-cost storage required human decision and action, so it happened late, inconsistently, or not at all.
No Machine-Readable Classification
Objects carried no indication of processing state, expected access frequency or retention obligation, so no automated rule could act on them.
Solutions Provided
Classification at ingestion, measurement before tiering, and lifecycle by policy
Classification at Ingestion
Implemented application-side object tagging using the AWS SDK recording processing state, expected access profile and whether a retention obligation attaches, making each object’s handling requirement machine-readable.
Measurement Before Tiering
Used Amazon S3 Storage Lens with advanced metrics filtered by object tag and prefix to establish genuine retrieval frequency, so tiering decisions rest on measurement rather than assumption.
Automated Lifecycle Transition
Deployed S3 Lifecycle policies as infrastructure code scoped by object tag and prefix, transitioning objects from S3 Standard through Standard-Infrequent Access to Glacier Deep Archive on defined day counts, with expiry for objects carrying no retention obligation.
Retrospective Tagging at Scale
Applied Amazon S3 Batch Operations to tag pre-existing data, bringing historical objects within the same lifecycle model as newly ingested data.
Provable Retention
Applied AWS KMS customer-managed key encryption with object versioning and AWS CloudTrail activity recording, so the retention position can be demonstrated rather than asserted.
Result Outcome
Cost decoupled from retention period and archival that runs without intervention
Cost Decoupled from Retention Period
Storage cost tracks how data is accessed rather than how long it must be kept — breaking the link between the retention obligation and the storage bill.
Archival Without Intervention
Transition and expiry occur by policy on defined day counts regardless of operational attention — removing the manual archival burden entirely.
Uniform Treatment of Old and New Data
Retrospective tagging removed a category of data that no policy governed — historical and newly ingested objects now follow identical rules.
Retention That Can Be Evidenced
Objects carry a machine-readable retention marker on durable encrypted storage with activity recorded — so the position is demonstrable, not merely asserted.
Decoupled
Cost from Retention
Storage cost tracks access behaviour, not how long data must be kept
By Policy
Archival & Expiry
Transition occurs on defined day counts without operational attention
Uniform
Old & New Data
Retrospective tagging brought historical objects under the same rules
Provable
Retention Position
Machine-readable markers on encrypted storage with activity recorded
Success Metrics
Measurable improvements across classification, tiering and evidence
Long-Term Storage Cost
↓
−30%
Reduced through automated S3 lifecycle tiering across the retention estate
Application Response Time
↑
20% Faster
Improved through AWS Graviton optimization of the platform
Compliance Readiness
↑
100%
Retention position machine-readable and demonstrable on demand
Archival Durability
↑
100%
Objects held on durable encrypted storage through to Glacier Deep Archive
Key-Use Auditability
↑
100%
Every key use recorded through AWS CloudTrail against customer-managed KMS keys, so the retention position can be evidenced rather than asserted
Before
Compliance data accumulated in hot storage regardless of how it was accessed
Archival required human decision and manual action
Objects carried no machine-readable classification for any rule to act on
Data created before the policy existed sat outside it
Storage cost grew in proportion to the retention obligation
After
Objects transition automatically to infrequent-access and archival tiers on defined day counts
Transition and expiry occur by policy without operational intervention
Every object tagged at ingestion with processing state, access profile and retention requirement
Retrospective batch tagging brought historical data under the same rules
Storage cost tracks access behaviour rather than retention duration
“
Classifying data at the point it is created and letting policy act on that classification turned a growing manual burden into a lifecycle that manages itself, and made the retention position demonstrable to an auditor.
Technology Stack
AWS Services Deployed
AWS SDK Object Tagging
Classification at Ingest
Amazon S3 Storage Lens
Access-Pattern Baseline
S3 Lifecycle Policies
Automated Transition
S3 Standard-IA
Infrequent-Access Tier
Glacier Deep Archive
Long-Term Retention
S3 Batch Operations
Retrospective Tagging
AWS KMS
Customer-Managed Keys
S3 Object Versioning
Durable Evidence
AWS CloudTrail
Activity Recording
Amazon S3
Retention Storage
Infrastructure as Code
Policy Deployment
Tag & Prefix Scoping
Rule Targeting
Accepting New Enterprise Clients
Ready to Build a
Self-Managing Data Lifecycle?
Book a complimentary cloud operations review. Our AWS-certified engineers will assess your storage, retention and lifecycle posture and deliver a tailored automation roadmap — no commitment required.
No commitment required
Response within 24hrs
AWS Advanced Partner