Process Data at the Edge, Before It Hits the Cloud
Deploy lightweight data processing pipelines at remote sites, factories, and IoT gateways. Parse, filter, and aggregate streaming data locally—reducing bandwidth by 90%+ while enabling instant edge decisions.
Unlike traditional cloud ETL that waits for data upload, layline.io processes at the source—cutting bandwidth costs by 90% and enabling real-time local actions with <5ms latency.
Flexible Edge Architecture
Deploy anywhere from lightweight single nodes to resilient edge clusters
Edge Devices
Sensors, PLCs, Cameras,
Industrial Controllers
Single Reactive Node
Lightweight node as part of distributed cluster
Full Edge Cluster
Multi-node resilient cluster at edge location
Cloud / HQ Cluster
Central layline.io cluster for
analytics & distribution
Bidirectional Flow
Data & commands both ways
Cluster Mesh
Distributed across locations
Tiny Footprint
50MB RAM per node
The Edge Data Explosion Is
Crippling Infrastructure
Sending raw edge data to the cloud creates a cascade of problems
Traditional Approach
All data to cloud
Crushing Bandwidth Costs
$50K+/month for 10TB/day transmission
Unacceptable Latency
200ms+ round-trip kills real-time decisions
Privacy & Compliance Risks
Raw data leaving premises violates regulations
Cloud Processing Costs
Compute charges on massive raw datasets
Network Saturation
Limited edge connectivity overwhelmed
layline.io Approach
Process at edge
99% Bandwidth Reduction
Process at edge, send only insights
Sub-5ms Response
Local processing enables instant decisions
Data Stays Local
Compliance-ready by design, no data export
Minimal Cloud Costs
Only processed data reaches cloud compute
Network Efficiency
Bandwidth freed for critical traffic
Cost Impact: Real Numbers
Monthly costs for 10TB/day edge data processing
Real-World Example
A manufacturing plant with 500 sensors generating 10TB/day would spend $900K annually just moving data to the cloud. With layline.io edge processing, that drops to $60K — freeing up $840K for innovation.
Lightweight Processing,
Enterprise Capabilities
Deploy the full layline.io Reactive Engine at the edge with minimal footprint
Three-Stage Processing Pipeline
Ingest
Real-time data capture from any source
Process
Transform, enrich, filter, aggregate in real-time
Distribute
Send insights to cloud, local systems, or both
Enterprise Features, Edge Footprint
Full-featured data processing in a lightweight package
Universal Connectivity
Industry-standard protocols including HTTP/REST, UDP, WebSocket, and more for seamless integration
Real-Time Processing
Stream processing with <5ms latency for instant decision-making at the edge
Smart Filtering
Process 10TB, send 100GB with configurable filtering rules and thresholds
Data Transformation
Parse, map, enrich, and aggregate data on-the-fly with visual workflows
Local Storage
Optional edge buffering for intermittent connectivity and offline operation
Cluster-Ready
Deploy single node or multi-node HA clusters with automatic failover
Minimal Resource Requirements
Memory (scales with throughput)
CPU (sufficient for most workloads)
Storage for binaries + optional buffer
Network (only processed data transmitted)
Deploy Anywhere
From tiny edge gateways to resilient Kubernetes clusters
Raspberry Pi / Edge Gateway
Lightweight single node for remote locations
- •50MB RAM footprint
- •ARM or x86 compatible
- •Perfect for IoT deployments
Industrial PC / Server
Dedicated edge processing with more resources
- •Higher throughput capacity
- •Local data buffering
- •Factory floor ready
Kubernetes Cluster
Distributed, resilient edge deployment
- •High availability
- •Automatic failover
- •Enterprise-grade resilience
Real-World Use Cases
See how organizations across industries leverage edge processing to transform their operations
Smart Manufacturing Quality Control
Manufacturing facilities can process high-frequency sensor data from hundreds of assembly line sensors in real-time. Edge processing enables instant defect identification and immediate corrective actions. By sending only quality metrics and alerts to the cloud, facilities can potentially reduce bandwidth costs by up to 95% while achieving millisecond-level quality control decisions.
Industry studies show that edge processing can reduce data transmission volumes by 90%+ while improving response times by orders of magnitude.
Energy Grid Monitoring & Optimization
Energy providers can deploy edge processing at thousands of substations to monitor grid health in real-time. Local anomaly detection and predictive maintenance algorithms process massive telemetry streams, sending only actionable alerts and aggregated metrics to central operations—enabling proactive grid management while potentially reducing network traffic by up to 98%.
Research shows that edge computing in utility networks can enable predictive failure detection while keeping sensitive infrastructure data on-premises for compliance.
Fleet Telematics & Route Optimization
Logistics companies can equip large vehicle fleets with edge-enabled gateways that process telematics data locally. Real-time route optimization, driver behavior analysis, and predictive maintenance can run on-device, transmitting only aggregated trip summaries and critical alerts—potentially cutting cellular data costs by up to 92% while improving fuel efficiency by 10-15%.
Transportation studies indicate that edge-based route optimization can deliver real-time adjustments without overwhelming cellular networks, leading to significant operational savings.
Edge Processing vs. Cloud-Only
See how edge processing with layline.io compares to traditional cloud-only approaches
How Edge Processing Compares to Cloud-Only
| Feature | layline.io Edge Processing | Cloud-Only Processing |
|---|---|---|
| Response Latency | <5ms Local processing | 200ms+ Network round-trip |
| Bandwidth Usage | 100GB/day 99% reduction | 10TB/day All raw data |
| Monthly Cost | ~$5K 93% savings | ~$75K Bandwidth + compute |
| Data Privacy | Data leaves premises | |
| Offline Operation | ||
| Scalability |
Ready to reduce costs and improve performance?
Get Started with Edge ProcessingFrequently Asked Questions
Everything you need to know about deploying edge processing pipelines with layline.io's lightweight reactive engine.
Edge processing means running data pipelines at remote locations—factories, branch offices, IoT gateways—before sending data to the cloud. This reduces bandwidth by 90%+, eliminates cloud latency, enables offline operation, and cuts infrastructure costs. You process, filter, and aggregate data locally, sending only what matters to central systems.
Yes. layline.io's edge deployment runs on ARM devices like Raspberry Pi 4, industrial PCs, and even smaller IoT gateways. The footprint is under 50MB with minimal CPU/memory requirements. You get the same visual workflow designer and enterprise features in a lightweight package optimized for resource-constrained environments.
layline.io includes connectors for HTTP/REST, UDP, WebSocket, and Kafka. For data formats, built-in support covers JSON, XML, and CSV, while the visual format editor lets you configure structured ASCII, ASN.1-based, and custom binary formats without coding. Parse, transform, and route even complex data formats visually.
layline.io provides centralized management for distributed edge fleets. Deploy configurations from a central console, monitor all edge nodes in real-time, collect logs and metrics, and push updates remotely. Built-in health checks and auto-recovery ensure reliability across your entire edge infrastructure.
Edge processing runs at data sources for low latency, offline capability, and bandwidth reduction. Cloud processing handles historical analysis, and centralized dashboards. layline.io lets you run the same workflows at edge or cloud, or split processing between both—preprocessing at the edge, deep analysis in the cloud.
Absolutely. Start with our free developer edition on your laptop or a local VM. Design your workflows visually, test with sample data, then deploy to edge hardware when ready. We offer proof-of-concept programs with loaner hardware and technical support to validate your use case risk-free.
Ready to Process Data Where It Matters Most?
Deploy edge processing workflows in hours, not weeks. Reduce bandwidth costs by 90%+ while maintaining real-time insights.
Trusted by teams processing billions of events daily