Kedro vs Splunk Observability
Side-by-side comparison to help you choose the best tool.
Kedro
freeKedro is an open-source Python system for creating reproducible, maintainable, and modular data science code with pipeline orchestration. Developed by McKinsey QuantumBlack and donated to the Linux Foundation, it brings software engineering best practices like modularity and testing to data science projects. Kedro provides a standardised project structure, a data catalogue, and pipeline visualisation.
Splunk Observability
paidSplunk Observability Cloud is an enterprise AIOps and full-stack observability platform providing unified metrics, traces, logs, and real user monitoring. Its AI anomaly detection, assisted triage, and root-cause analysis help teams detect and resolve incidents before customers are impacted. Part of the broader Splunk platform (now Cisco), it is a leading choice for large-scale cloud-native observability.
| Feature | Kedro | Splunk Observability |
|---|---|---|
| Pricing | free | paid |
| Category | - | - |
| Rating | 4.2 | 4.4 |
| Best For | Data science teams who want to apply software engineering best practices to their projects | Enterprise engineering and SRE teams needing full-stack AI observability and AIOps at scale |
| Views | 35 | 38 |
Pros
- Excellent code organisation and modularity
- Strong software engineering principles
- Good documentation
Cons
- Learning curve for data scientists unfamiliar with software engineering
- Less real-time monitoring than alternatives
Pros
- Enterprise-grade scalability for large environments
- Strong AI anomaly detection
- OpenTelemetry-native for modern cloud stacks
Cons
- Expensive at scale
- Complex to configure for newcomers to observability
- Modular pipeline nodes
- Data catalogue abstraction
- Project templating
- Pipeline visualisation
- Plugin ecosystem
- AI anomaly detection & alerting
- Full-stack metrics, traces & logs
- Real user monitoring (RUM)
- AI-assisted root cause analysis
- OpenTelemetry-native