ProNatura Waste Management Data System

ProNatura is a waste management company based in Bydgoszcz, focused on environmentally responsible processing of municipal waste. The organization operates large-scale systems for waste collection and disposal, where accurate scheduling and reliable communication with residents are essential.

The waste collection schedule needed to be distributed through both digital and printed channels, making accuracy critical.

However, the source data was inconsistent and partially edited manually, leading to frequent errors in schedules. These inconsistencies created confusion for residents and increased operational complexity.

At the same time, the previous system relied on direct database integration, which lacked validation mechanisms and made it difficult to correct errors automatically.

To improve accuracy and reduce operational issues, the system needed to:

• automatically validate and correct inconsistent input data
• generate reliable schedules for both digital and print channels
• provide flexibility to adjust specific dates when needed
• reduce dependency on manual data correction
• ensure consistency across all communication channels

The goal was to create a system that could transform imperfect input data into reliable and usable output.

A serverless data processing platform was introduced to manage and correct waste collection schedules. It applies automated correction algorithms to incoming data, identifying and fixing inconsistencies before schedules are generated. It also supports both web-based access and integration with external printing systems, making sure that residents receive accurate information regardless of the communication channel.

By introducing structured validation and correction logic, the solution replaces unreliable direct database integration with a controlled and scalable data processing pipeline. This approach reflects the data quality and integration principles behind industrial data platforms, ensuring that inconsistent source information is corrected before it reaches downstream applications and reporting workflows.

Modernizing established operational systems often requires addressing unreliable data flows without disrupting existing processes. Although this project focused on waste collection scheduling rather than equipment retrofitting, similar integration challenges arise in brownfield IoT modernization, where legacy infrastructure must work alongside modern data systems.

It was built using cloud-native and web technologies:

• Python for backend processing and data correction logic
• React (SPA) for the user interface
• AWS Cloud for serverless infrastructure
• DynamoDB for data storage

Incoming data is processed through a set of correction algorithms that detect inconsistencies and adjust values automatically. Where necessary, users can manually update specific dates through the system interface, ensuring flexibility without compromising overall data integrity.

Once validated, the system generates schedules for individual addresses and distributes them across both digital platforms and print workflows. All components are hosted in the cloud, enabling scalable and reliable processing.

The introduction of automated data correction significantly improved the reliability of waste collection schedules. Teams no longer need to manually fix inconsistencies at scale, reducing workload and minimizing the risk of errors. At the same time, consistent data across channels improved communication with residents.

The system delivered measurable improvements across key areas:

• Reduced scheduling errors, through automated data correction
• Improved data consistency, across digital and print channels
• Lower printing costs, through optimized schedule generation
• Reduced manual workload, by eliminating repetitive corrections
• More reliable communication, improving service quality for residents

Can inconsistent waste collection schedule data be corrected automatically before publishing?

Yes, correction algorithms can detect inconsistencies in incoming schedule data and adjust values automatically before schedules are generated. Users can still update specific dates manually through the system interface without compromising overall data integrity. In this project, InTechHouse wrote the correction logic in Python for ProNatura, replacing unreliable direct database integration with a controlled data processing pipeline.

How can one schedule system serve both a website and printed waste collection calendars?

By validating the data first and then generating schedules for both channels from the validated output. The system produces schedules for individual addresses and supports web-based access plus integration with external printing systems, so residents receive accurate information in either channel. In this project, InTechHouse hosted all components for ProNatura in AWS Cloud, and the results include improved data consistency across digital and print channels.

Why use a serverless architecture for a municipal waste collection schedule platform?

A serverless architecture provides scalable and reliable processing for schedule generation. ProNatura's platform runs on AWS Cloud serverless infrastructure with DynamoDB for data storage and a React single-page application for the user interface. In this project, InTechHouse used this setup to replace a direct database integration that lacked validation mechanisms, which reduced scheduling errors and repetitive manual corrections.