Optimizing Elasticsearch for Multi-Tenancy in a Telecom Tech Company
Client
The client is a market technology and communications firm powering platforms in voice, messaging, analytics, and directory services. They handle real-time, high-volume data streams and needed scalable observability.
Project Context
Their Elasticsearch 7.17.12 setup required deep tuning for high ingestion rates, performance improvements, and secure multi-tenancy for analytics and platform teams.
Challenges
- Inefficient indexing and high shard counts
- Overlapping data access between tenants
- Manual retention and inconsistent ILM phases
Solution
- ILM policies with hot-warm-cold setup
- Monitoring clusters and ML anomaly detection
- Shard sizing, aliases, routing, and security via RBAC
Project Objectives
- Improve ingestion throughput and multi-tenant isolation
- Optimize ILM strategies and reduce query latency
- Implement shard, replica, and resource scaling policies
Solution Delivery
SquareShift implemented 30+ best practices including alias-based routing, dynamic scaling, Logstash multi-threading, field-level mapping control, and Terraform for infra definition.
Testimonial
Their deep expertise let us squeeze the most performance from every cluster
