01 · Operational event stream
The ingestion path prioritized events that affected reporting freshness and helped teams see delays, SLA breaches, route issues, and resource-allocation constraints.
Customer work · Logistics
Delhivery built a petabyte-scale AWS logistics data platform in six months
Delhivery needed current operating views across a rapidly growing parcel network while keeping historical package and scan data available for analysis. BluePi built an AWS data warehouse and near-real-time reporting path using Amazon S3, Kinesis, Storm, Redis, and Redshift.
Near-real-time logistics reporting built for network growth
Delhivery needed fresher operational data and a standardized model for invoicing, business intelligence, and other consumers across a high-volume parcel network. BluePi built a data warehousing and analytics path on AWS using S3, Kinesis, Redshift, Redis, and Storm.
02 · Shared logistics model
A standardized model reduced conflicting interpretations across invoicing, business intelligence, and analytical consumers.
03 · Separated growth layers
Storage, processing, and serving layers were designed for petabyte-scale growth and protected sensitive data with security controls.
From parcel events to shared operational reporting
BluePi collected operational data through AWS ingestion and storage services, processed near-real-time streams, modeled shared warehouse data in Redshift, and served reporting needs across invoicing, business intelligence, and operational views.
Stream the useful events
The ingestion path prioritized operational changes that affected reporting freshness and response.
Standardize shared data
A common model reduced conflicting interpretations across invoicing, reporting, and analytical consumers.
Design for growth
Storage, processing, and serving layers were separated so volume and workload changes could be handled deliberately.
Where this pattern fits
This pattern fits logistics leaders who need fresh operational data, clear service measures, and a platform that can grow with network volume.
Case details
Open a section to review the customer problem, implementation, business change, and architecture.
01The starting point
Operational data remained distributed across systems, the existing platform could not keep pace with growth, and network problems became visible too late. Teams needed a current view of SLA breaches, routes, facilities, package movement, and resource constraints.
- Scalability: The existing system could not keep up with rapid data growth.
- Real-time insights: Without real-time visibility into operations, teams spotted problems late and improvement efforts stalled.
- Data consolidation: Data distributed across separate systems prevented a consistent view of logistics operations.
- Last-mile delivery pressure: Route planning and resource allocation needed better data to improve last-mile delivery.
02System delivered
BluePi connected historical package and scan ingestion with Amazon Kinesis streaming, Storm processing, Amazon S3, Redis, and Amazon Redshift. Derived movement measures and a standardized logistics model served near-real-time SLA reporting, invoicing, visualization, and custom BI.
- Data warehousing: BluePi built the data platform on AWS with S3, Kinesis, Redshift, Redis, and Storm for scalable storage, processing, and retrieval.
- Agile development: Delivered the platform in six months through agile iterations.
- Scalable architecture: The architecture is designed to handle petabyte-scale data growth.
- Real-time reporting: Near real-time reporting tracks service-level agreement (SLA) breaches and surfaces operational issues.
- Standardized data model: A standardized data model serves invoicing, visualization tools, and custom business intelligence (BI).
- Security controls: Security controls protect sensitive data across the platform.
03Outcomes
The platform was delivered in six months and designed for petabyte-scale growth. Operations teams gained earlier visibility into SLA breaches, route constraints, facility delays, and resource-allocation issues.
- Improved decision-making: Real-time data now guides decisions on routes, resource allocation, and last-mile operations.
- Higher operational efficiency: Teams can find and fix bottlenecks and delays, which improved efficiency.
- Reduced costs: Better routes and resource utilization reduced operational costs.
- Better customer satisfaction: Faster deliveries and fewer delays improved customer satisfaction.
- Scalability for future growth: The platform scales with future data growth and changing business needs.
04Architecture boundary
Package, scan, facility, and route data entered streaming and historical ingestion paths. Storm processing derived measures such as time in system, time since last scan, time spent in a facility, and time on road. Amazon S3, Redis, and Redshift supported the standardized model and its reporting consumers.
05Delivery and scale
BluePi delivered the platform in six months. The storage, processing, and serving layers were designed to support petabyte-scale growth as parcel and scan volume increased.
06What changed for logistics operations
Historical and current network data shared one reporting foundation. Teams could identify SLA breaches, route constraints, facility delays, and utilization issues earlier, then use the same model for immediate action and longer-term analysis.