Case study
Customer Data Kept Properly Separate for a Telecom Provider
How SquareShift rebuilt data retention into hot-warm-cold tiers, added machine-learning anomaly detection, and isolated tenants with role-based access control for a telecom company's shared Elasticsearch platform.
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Client
A telecom and communications-technology company that runs voice, messaging, analytics, and directory services in real time.
Industry
Telecommunications
Services
Stack
Elasticsearch · Logstash · Terraform · Machine learning anomaly detection
How SquareShift delivered it.
The challenge
The client's Elasticsearch platform served many customer tenants on shared infrastructure at high, real-time volume across voice, messaging, analytics, and directory services. Indexing was inefficient, and tenants were not properly kept apart from each other.
What we delivered
SquareShift rebuilt data retention into hot-warm-cold tiers, added machine-learning anomaly detection for the cluster, and used role-based access control to keep each tenant's data separate. The team put more than 30 best practices in place, including automatic routing rules and infrastructure managed through Terraform.
The outcome
Tenants are now properly separated with role-based access control, and machine-learning anomaly detection watches the cluster for problems on its own. For retention, access control and anomaly detection on Elastic, see our Elastic search and observability services.
Shared infrastructure without real tenant isolation isn't multi-tenant, it's multiple single tenants sharing risk — role-based access control is what actually draws the boundary the architecture only implied.
Security & Compliance Practice Lead, SquareShift
