Battery Telemetry Analytics Platform
Large telemetry systems need both fast historical queries and a live operational view.
ResultReduced load time on the heaviest analytical views to under 30 seconds while preserving real-time visibility.
- Role
- Architecture and implementation
- Period
- Professional engagement
- Context
- Battery telemetry and analytics
- Confidentiality
- Anonymized professional work
At a glance
Large telemetry systems need both fast historical queries and a live operational view.
Architected the Python and Flask visualization platform, implemented WebSocket updates, and optimized Athena queries to bring the heaviest views below 30 seconds.
Reduced load time on the heaviest analytical views to under 30 seconds while preserving real-time visibility.
The choice behind the implementation.
- Challenge
- Large historical telemetry queries had to coexist with a live operational view without making the interface unusable.
- Decision
- Optimize the analytical query path while keeping current activity on a separate WebSocket-driven flow.
- Trade-off
- The product maintains two data paths, but each path can be tuned for its own latency and workload.
The product problem
Analysts needed to explore datasets large enough to overwhelm a naive query plan. Historical analysis alone was not sufficient; the same interface also needed a live view that updated while operators watched it.
What I delivered
- Architected the Python and Flask visualization platform.
- Implemented real-time streaming through WebSockets.
- Optimized Athena queries across large telemetry datasets.
- Brought load time below 30 seconds on the heaviest views.
- Telemetry sourcesHigh-volume battery records enter the analytical system.
- Query layerAthena-backed access paths are shaped for the requested view.
- Application servicePython and Flask coordinate query and presentation behavior.
- Live interfaceWebSockets update the operational view without discarding historical context.
The public boundary is deliberate.
Product and client identities, customer data, repositories, and confidential implementation details are intentionally omitted. The page describes only confirmed personal contribution and safe technical context.