What challenge were we facing in this case?
DPD needed to unify and provide access to operational data across multiple systems in real time. Internal teams were facing limitations caused by fragmented data sources, manual processing, and outdated outputs, which complicated both decision-making and operational management. They lacked a centralized layer that could reliably aggregate data, ensure its validation and transformation, and offer a unified interface for reporting and analytics. The challenge was to design a scalable and robust solution capable of processing millions of records daily, automating workflows, and providing a consistent data view for various teams – from operations to management.
A solution is not enough. It must be smart, scalable, and future-ready.
For DPD, we designed and implemented a centralized and normalized database – the ODS (Operational Data Store) – which ensures powerful, accessible, and reliable real-time data processing. The system supports complex reporting, performance optimization, and integration with other systems. The key to success was a thorough analysis of existing processes, data, and legacy system code, along with a gradual definition of domain terminology and data relationships.
A few technical lines for the enthusiasts.
Our solution is built on modern technologies and integration approaches that ensure scalability, performance, and ease of maintenance:
A DWH and ODS solution built on a centralized relational database.
ETL processes for handling imported data from files.
Normalization mechanisms for a unified data model.
APIs for bidirectional communication with other systems (e.g., FiscalPro, CashApp).
OPS Toolset Portal – a user interface for data management and manual data entry.
From data to delivery: how information is collected, processed, and distributed
To ensure the fast and reliable delivery of parcels, DPD’s system collects a large amount of data at various stages of the process. It all starts with the order – the information that a parcel is to be sent. Additional data is then layered onto this, such as validation and routing data, parcel scans from various scanning points (depots, hubs, couriers), redirect requests, and supplementary information about any inconsistencies, such as duplicates or missing scans.
Data is imported into the system via files in agreed formats – these are then normalized to make them consistent, clear, and useful. Additionally, users have the ability to supplement or edit data directly via the system’s user interface.
Based on these inputs, valuable insights are generated, enabling efficient management and optimization of the delivery process. The Satisfy module processes scan data, including the supplementation of missing records. Another module handles updating and applying parcel redirections and ensures they are passed to downstream systems. Other outputs include approved pickup addresses and customer bank accounts, the availability of express services based on postal codes, internal mail orders, pickup requests, and delivery control results.
These outputs are further distributed to various systems – either through file exports to designated storage, from where tools like Scan Server or DPD Register retrieve them, or through API interfaces actively used by other systems (e.g., CashApp, COD processing tool). The system is also capable of utilizing external APIs from solutions like FiscalPro. Thanks to this integration and data flow, DPD employees always have access to accurate, up-to-date, and verified information essential for the effective operation of the entire logistics chain.
Selected tech stack, tools, and frameworks to ensure scalability, performance, and long-term reliability.
.NET
Azure
Jira
Kubernetes
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