ELO Slashes Costs 87% Migrating Payments Data to ClickHouse

Cost & Performance Revolution

This case study powerfully illustrates the transformative potential of choosing the right database for specific workloads. Nava's successful migration of ELO's payment monitoring platform from Elasticsearch to ClickHouse highlights significant cost savings and performance gains. The 87% infrastructure cost reduction, from R$900,000 to R$120,000 annually, is a staggering figure, directly attributable to ClickHouse's superior storage efficiency (12TB down to 2TB) and query optimization capabilities. The assertion of 5x faster aggregations and sub-2-second end-to-end latency for real-time dashboards refreshing every five seconds underscores ClickHouse's suitability for high-throughput, low-latency analytical workloads, especially in the financial sector where prompt visibility into transaction flows can prevent significant financial losses.

The technical details provided, such as the use of ZSTD and LZ4 compression codecs, LowCardinality data types, materialized views, and projections, demonstrate a well-architected implementation. The shift from Elasticsearch's verbose EQL to standard ANSI SQL is a critical usability improvement, democratizing data access for ELO's operations teams and reducing development bottlenecks. This aspect is particularly noteworthy, as it addresses not just technical performance but also the operational agility of the business. The article effectively positions ClickHouse as a compelling alternative for organizations struggling with the cost and performance limitations of Elasticsearch for analytical purposes, especially within high-volume transaction processing environments.

However, a potential limitation not fully explored is the long-term operational overhead and complexity of managing a ClickHouse cluster, even with Kubernetes operators like Altinity. While the article emphasizes the cost savings, it would be beneficial to hear more about the team's experience with ongoing maintenance, scaling challenges, and the expertise required to fully leverage ClickHouse's advanced features for continuous optimization. Furthermore, while ClickHouse Cloud is mentioned as an option, a deeper dive into its managed service benefits and potential trade-offs compared to self-hosting would add further value for readers considering different deployment models. Nevertheless, the success story presented is highly persuasive and offers a clear blueprint for other organizations facing similar data analytics challenges.

Key Points

• Nava migrated ELO's payment monitoring platform from Elasticsearch to ClickHouse.
• This resulted in an 87% reduction in annual infrastructure costs (R$900,000 to R$120,000).
• Storage requirements decreased from 12 TB to 2 TB.
• ClickHouse delivered 5x faster aggregations and sub-2-second end-to-end latency for real-time dashboards.
• The migration simplified querying with standard SQL, improving developer productivity and operational agility.
• Technical optimizations included ZSTD/LZ4 compression, LowCardinality types, materialized views, and projections.
• ClickHouse is positioned as a cost-effective and high-performance alternative to Elasticsearch for analytical workloads.

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📖 Source: How Nava helped ELO cut infrastructure costs by 87% by migrating from Elasticsearch to ClickHouse