Case study
Enterprise Search Modernized for a Global Professional Services Firm
How SquareShift retired an old Solr search platform and stood up a 9-node Elasticsearch cluster on Google Kubernetes Engine, with role-based security and monitoring built in from launch.
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Client
A global professional services firm modernizing its enterprise search platform.
Industry
Professional Services
Services
Stack
Solr · Elasticsearch · Elastic Cloud on Kubernetes (ECK) · Google Kubernetes Engine (GKE) · Python
How did SquareShift move enterprise search from Solr to Elasticsearch?
The challenge
The client is a global professional services firm that needed to retire an old Solr search platform. It wanted to move to Elasticsearch on Google Kubernetes Engine without losing security, performance, or reliability along the way, and without a production-grade security model already in place.
What we delivered
SquareShift stood up a 9-node Elasticsearch cluster (3 master, 3 hot, 3 cold) on GKE and wrote custom Python scripts to carry out the Solr-to-Elasticsearch data transfer, moving high-volume data across with no manual re-entry.
The team designed a production security model around role-based and field- and document-level security, added snapshot and restoration workflows to protect the migrated data, and layered in Stack Monitoring, slow-query logging, and ILM tuning.
The outcome
The firm now runs its search platform on a cloud-native, 9-node Elasticsearch cluster on GKE, with security and monitoring built in from launch instead of added on afterward. Solr migrations are part of our Elasticsearch migration services.
Retiring a search platform is only half the job. The other half is proving, before cutover, that the new cluster's security model can do everything the old one did — and more.
Platform & Software Engineering Practice Lead, SquareShift
