Energy trading with Apache Kafka: Becoming faster than the competition

Quick facts

• Industry: Energy Trading
• Date: 2024
• Our Involvement: Managed Kafka platform deployment for real-time energy trading operations across multiple Dutch energy trading firms

About Dutch energy trading operations

Multiple Dutch energy trading companies specializing in intraday and day-ahead markets operate in one of Europe's most competitive and complex energy environments. With renewable energy integration increasing grid volatility and trading windows measured in minutes, these firms compete on speed, data accuracy, and operational reliability.

Goals & context

Energy trading companies face a unique architectural challenge: they must process massive volumes of real-time data from disparate sources (market price feeds, weather monitoring systems, grid sensors, trading applications) and make split-second trading decisions where delays of even seconds can mean the difference between profit and loss. These firms operated with fragmented data infrastructure spanning multiple database technologies (Oracle, MySQL, MariaDB, PostgreSQL, and Cassandra), creating critical latency as data synchronized across systems. By the time market data propagated through various databases to trading algorithms, market conditions had already shifted. The diverse technology stack also required specialized expertise for each system, making performance bottlenecks difficult to diagnose and scale.

The companies recognized that event streaming could solve these problems, but building and operating Apache Kafka at the scale and reliability required for financial trading presented its own risks. None of the firms had deep Kafka expertise in-house, and a misconfigured cluster or failed broker during critical trading hours could result in substantial financial losses. Standard tactics failed: adding database capacity increased costs without solving latency, hiring Kafka specialists would take months, and cloud-managed Kafka services introduced concerns about data residency and vendor control for mission-critical trading infrastructure.

Strategic approach

Hypothesis: Event streaming with proper governance can consolidate fragmented data infrastructure while providing the sub-second latency required for competitive energy trading, but only if the platform operates with financial-grade reliability and the operational burden remains manageable.

Principles:

• Real-time data processing must handle market data, weather feeds, grid sensors, and trading signals with consistent low latency
• The streaming platform must achieve 99.9% availability because downtime during trading hours directly impacts revenue
• Data consistency across all systems is non-negotiable in financial operations where incorrect data leads to trading losses
• The solution must reduce rather than increase operational complexity
• Access controls must meet financial regulatory requirements while enabling rapid integration of new data sources

Operating Model: Deploy a managed Kafka platform that handles operational complexity while providing the firms with architectural control over their trading data flows. The platform would replace point-to-point database integrations with a unified event streaming backbone, enabling real-time data availability across all trading systems while maintaining the governance and reliability standards required for financial operations.

Implementation: Real-time infrastructure with financial-grade reliability

The architecture created a fundamental problem: market price updates, weather conditions, and grid status followed different paths through different databases before reaching trading algorithms. Trading decisions were based on data that could be several seconds old. The problem wasn't the databases themselves but the lack of a real-time data backbone. Each database served legitimate analytical needs, but using them as the integration layer created unavoidable latency.

The solution implemented Kafka as the central nervous system for trading data. All data sources (Java trading applications, market price scrapers, weather monitors, grid sensors) publish events to Kafka topics, with databases consuming from these topics rather than polling each other. This event-driven architecture inverts the traditional model: the event stream becomes the source of truth, and databases become materialized views optimized for specific queries.

The deployment followed a phased approach to minimize risk. Axual Connect used change data capture to publish database modifications as Kafka events without modifying existing applications. Trading applications then migrated to consume directly from Kafka topics, using event replay during testing to verify identical behavior. Predictive analytics and automated trading systems deployed as new Kafka consumers, receiving updates within milliseconds. Axual's role-based access control isolated different trading teams and systems, ensuring only authorized systems could access sensitive trading information.

Energy trading operates with effectively zero tolerance for unplanned downtime. The firms recognized that operating Kafka at financial-grade reliability requires deep expertise that takes years to develop. A single misconfigured parameter during trading hours could result in losses that dwarf the cost of any managed platform.

Axual deployed two production Kafka clusters in separate data centers with the Distributor maintaining real-time synchronization between them. Both clusters remain current at all times through continuous replication of events, consumer offsets, and schema definitions with sub-second latency. Applications distributed load between clusters in normal operations. If either cluster experienced issues, applications automatically connected to the healthy cluster within seconds. During deployment, planned broker maintenance and an unplanned network issue validated this resilience with no trading interruptions or data loss.

The architecture processed market data in real time with consistent sub-100ms latency from event creation to availability across all consuming applications. The fragmented five-database landscape remained for analytical roles, but database synchronization overhead disappeared. The firms achieved financial-grade reliability without building internal Kafka operations teams, and the multi-cluster architecture removed reliability as a constraint on growth.

Operational efficiency and expertise transfer

Axual's managed platform approach addressed the operational reality that the firms needed streaming capabilities without becoming Kafka experts. The self-service interface enabled different trading teams to create topics, configure schemas, and manage access policies without involving central IT or understanding Kafka internals. Data engineering teams focused on trading algorithms and analytics rather than cluster operations and capacity planning. Axual's monitoring provided insight into streaming performance through accessible dashboards rather than requiring Prometheus queries or JMX tool expertise.

When issues arose, Axual's support team investigated and resolved problems that would have required specialized knowledge in a DIY deployment, including performance tuning, capacity planning, and troubleshooting complex failure scenarios. The firms gained enterprise streaming capabilities while their teams remained focused on energy trading rather than Kafka operations.

Results

• System Availability: 99.9% uptime maintained across multiple clusters and trading scenarios
• Data Processing Latency: Sub-second event propagation from market feeds to trading algorithms
• Infrastructure Consolidation: Eliminated point-to-point integrations across five database systems while maintaining each system for its analytical role
• Operational Complexity: Reduced from five database replication paths requiring specialized expertise to a unified streaming platform with self-service management
• Scalability: Platform handles increasing data volumes from renewable energy integration and granular market data without architectural changes

Testimonial

"Operating in the highly competitive energy trading market, we've learned that every second counts. Axual's Kafka solution has been pivotal in enabling us to make swift, informed decisions and gain a competitive edge."

Closing thoughts

Energy trading demonstrates a pattern visible across regulated, mission-critical industries: the theoretical benefits of event streaming are clear, but the operational reality of running production Kafka at financial-grade reliability remains a barrier to adoption.

These Dutch energy trading firms solved this by separating concerns. They retained architectural control over their data flows and trading systems while delegating operational complexity to a managed platform. This approach delivered both the real-time capabilities required for competitive trading and the reliability standards required for financial operations.

The enduring capability is not just the streaming platform itself but the operational model it enables. Trading teams can now iterate on algorithms, integrate new data sources, and respond to market changes without architectural constraints or operational risk. The platform scales with business needs rather than creating bottlenecks that constrain growth.

For industries where system reliability directly impacts revenue and competitive position, this model resolves the tension between the need for modern streaming architectures and the risk of operational complexity.

Further information / resources

• Axual Platform for Energy Sector
• Apache Kafka for Financial Services

Call to action

If your organization faces similar challenges with fragmented data infrastructure or requires financial-grade reliability for mission-critical streaming, discuss your specific architecture requirements with our technical team. We can evaluate whether a managed streaming platform fits your operational model and regulatory environment.

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