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Real-Time Data Pipeline Development for Robotics Data Processing

geniusee.com

Robotics

Logistics

Manufacturing

Challenges in Real-Time Data Processing for Robotics

The client faces critical challenges in maintaining data quality, eliminating processing delays, and ensuring system stability for a high-volume data pipeline (10GB/s) from IoT devices and third-party systems. Existing infrastructure lacks scalability and cost-efficiency for real-time analytics.

About the Client

A robotics company specializing in warehouse automation, data pipelines, and real-time data streaming for enterprise process optimization.

Key Goals for Pipeline Development

Design a real-time data pipeline with zero processing delays
Ensure 100% data completeness and quality validation
Implement a cloud-native, cost-effective architecture for 10GB/s throughput
Build auto-scaling infrastructure independent of data volume
Enhance system stability with automated error recovery

Core System Requirements

Real-time data ingestion from IoT sensors and CRM systems
Configurable batch processing (mini/full batching)
Automated data quality validation with alerting
Cloud-native microservices architecture
Multi-source integration with third-party services

Technology Stack

Confluent

Elastic Kubernetes Service

Python

Scala

Terraform

External System Integrations

IoT devices
Salesforce CRM
Barcode scanners
Third-party data lakes

Performance and Scalability Requirements

Support 10GB/s throughput with <50ms latency
99.99% system availability
Auto-scaling infrastructure
End-to-end data encryption
Real-time monitoring dashboard

Expected Business Outcomes

Enables real-time decision-making for warehouse robotics, reduces processing delays by 90%, cuts infrastructure costs by 40% through optimized cloud usage, and supports seamless scaling for future IoT device integration.