Storage that
retires itself.
Lifecycle, retention, and residency enforced where data is created. Free 30-day assessment ends with a storage cost-reduction roadmap.
Before
After
Baseline
Measure current volume and cost
Config
Declare selected outputs
Test
Validate retention and recovery
Measure
Calculate observed outcomes
/ Challenges
Challenges
Retention Limits Driven by Budget
Deleting data after 7-30 days not because you want to, but because cloud storage costs would bankrupt the department.
Can't Search Historical Data
Engineers need data from last month to diagnose issues. It's already gone. No way to search - you have to know exactly when and where to look.
Storage Costs Spiraling
Raw data volume and retention can grow faster than budget without an evidence-based storage plan.
Data Gravity Problem
Data stuck in expensive platforms. Moving it costs a fortune. Can't optimize without massive migration.
/ What We Do
Source and Output Inventory
Map source volume, required records, configured outputs, and current storage destinations.
Measured baseline
Retention Requirements
Document retention, retrieval, outage, replay, and cleanup requirements before changing data movement.
Explicit acceptance
Configured Output Evaluation
Test whether selected records can satisfy downstream needs without universal reduction assumptions.
Observed results
Cost Evaluation
Compare observed source, destination, storage, and operational costs for the bounded workload.
Customer-specific ROI
/ What You Get
Data Flow Mapping
Complete data flow diagram with cost breakdownWe map where your data is created, where it moves, where it's stored, and what it costs
Storage Cost Analysis
Measured baseline and test methodCalculate current storage costs vs. projected costs with local storage and indexing
Retention Optimization
Retention policy recommendationsDesign retention strategy that meets compliance needs without breaking budget
Migration Roadmap
Scoped validation planStep-by-step plan to implement source-level indexing and local storage
/ Evaluation Scenarios
Retention Baseline
Challenge
A team needs to understand which data is retained locally and which selected results are sent downstream.
Solution
Inventory sources, configured outputs, retention requirements, storage systems, and operational constraints.
Evidence
Evidence to collect: current volume, retention, retrieval behavior, storage cost, and recovery requirements.
Configured Output Evaluation
Challenge
A team wants to evaluate sending selected records rather than every source record to a destination.
Solution
Build a bounded job, define retained information explicitly, and compare source and destination output.
Evidence
Evidence to collect: record completeness, downstream volume, storage cost, replay, and exception handling.
MQTT Aggregation Evaluation
Challenge
A team wants to aggregate telemetry close to its source before writing a configured output.
Solution
Use the documented MQTT input and IoT aggregation pattern with representative hardware and traffic.
Evidence
Evidence to collect: aggregation correctness, resource use, destination volume, outage recovery, and retention behavior.
/ Book your free consultation
Storage getting away from you?
Pick a time. 30 minutes. No slides — we go straight to your lifecycle and retention gaps and the tiers that are costing the most.
- 30-day assessment with a prioritized cost-reduction roadmap
- Free of charge — no commitment beyond the call
- We'll ask hard questions, not try to sell
