---
title: "How Eisan made POS analytics faster, cheaper, and more reliable with ClickHouse Cloud"
description: "Eisan System Development moved a retail analytics workload from a QlikView-based platform to ClickHouse Cloud after rising ID-POS data volumes caused slower processing, failed analyses, and increasing licensing and server costs. Client records from supermarkets and drugstores support basket analysis, cross analysis, and real-time personalization, with some datasets nearing 200 million records annually. The team compared open-source options including PostgreSQL and YugabyteDB, then tested ClickHouse configurations, including a 30-core, 120-GB setup, while accounting for cache memory usage through load testing. In comparison, ClickHouse ran on slightly lower-spec hardware under high-load conditions without observed performance deterioration, and Eisan reports no cases so far where it failed to return a result; the team is now targeting 100 billion records and exploring off-hours processing and scheduled scaling."
---

# How Eisan made POS analytics faster, cheaper, and more reliable with ClickHouse Cloud

[Clickhouse](https://yomu.fyi/company/clickhouse) · ClickHouse · Apr 8, 2026

**Type:** Problem & solution

## Summary

Eisan System Development moved a retail analytics workload from a QlikView-based platform to ClickHouse Cloud after rising ID-POS data volumes caused slower processing, failed analyses, and increasing licensing and server costs. Client records from supermarkets and drugstores support basket analysis, cross analysis, and real-time personalization, with some datasets nearing 200 million records annually. The team compared open-source options including PostgreSQL and YugabyteDB, then tested ClickHouse configurations, including a 30-core, 120-GB setup, while accounting for cache memory usage through load testing. In comparison, ClickHouse ran on slightly lower-spec hardware under high-load conditions without observed performance deterioration, and Eisan reports no cases so far where it failed to return a result; the team is now targeting 100 billion records and exploring off-hours processing and scheduled scaling.

## Context

A client’s QlikView-based analytics system was strained by growing ID-POS data volumes and rising expectations for real-time insight. Some processes took minutes, analysis results sometimes failed to return, and memory consumption, licensing, and server costs increased as more clients and deployments were added.

## Approach / What changed

Eisan evaluated PostgreSQL, YugabyteDB, and other alternatives before adopting ClickHouse Cloud. The team tested CPU and memory configurations, including 30 cores with 120 GB of RAM, investigated cache-related memory usage with the ClickHouse Japan team, and continues load testing while considering off-hours processing, autoscaling, and scheduled scaling.

## Takeaways

- ClickHouse maintained stable performance under high-load settings on a server with slightly lower specifications than typical setups, while QlikView increasingly failed to return some analysis results.
- Eisan’s configuration testing showed that apparently low memory utilization included memory used for caching; accounting for cache usage made the 30-core, 120-GB setup appear more balanced.
- The team aims to support 100 billion records and is exploring off-hours workloads, autoscaling, and possibly shutting down operations during low-use periods such as Sundays.

**Tags:** [Performance](https://yomu.fyi/topic/performance), [Reliability](https://yomu.fyi/topic/reliability), [Scalability](https://yomu.fyi/topic/scalability)

- Source: [Clickhouse](https://clickhouse.com/blog/eisan)
- Source URL: https://clickhouse.com/blog/eisan
- Ingested by Yomu: 2026-08-28T01:23:52.876Z

[Read original post](https://clickhouse.com/blog/eisan)