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Real-time analytics with Lambda and Kappa architectures
Two proven patterns for combining historical and real-time data, and how we choose between them.
The challenge
The business needs live dashboards and alerts, but also complete, accurate history. Running separate systems for each often produces two versions of the truth.
Our approach
Lambda runs a batch layer for complete, accurate views and a speed layer for low-latency views, merged at serving time. Kappa treats everything as a stream and rebuilds history by replaying a durable event log, keeping one code path. We recommend Lambda when heavy historical recomputation is needed and Kappa when a single streaming code base is simpler to run.
Architecture
How the pieces fit together. Scroll sideways on small screens.
Lambda architecture
Kappa architecture
Key components
- Kafka, Kinesis or Event Hubs as the event log
- Flink or Spark Structured Streaming for stream processing
- Spark batch jobs on Delta Lake or Iceberg for the batch layer
- Serving tables and low-latency stores for queries
- Monitoring of lag, throughput and data freshness
What you can expect
- Dashboards and alerts updated in seconds
- Consistent numbers between real-time and historical views
- Ability to reprocess history after logic changes
- Architecture sized to your data volumes and team skills
How we deliver it
Assess
Review current systems, data and goals, and agree success measures.
Design
Target architecture, security model, plan and fixed quote.
Build and test
Agile sprints with demos, automated tests and documentation.
Launch and support
Production rollout, knowledge transfer and optional managed support.
Have a data, AI or finance challenge?
Tell us what you need. Our team will set up a no-cost discovery call.