Innovecs team enabled real-time data availability in a supply chain web portal by integrating Azure-based architecture. This transformation empowered the client’s customers with up-to-date insights and improved analytics-driven decision-making.

Supply Chain

Location: USA
Team extension: Backend Engineer, Data Engineer, Solution Architect
Focus Areas: Data management, Integration
Duration: 5 months

The client is based in the US and serves major players in the food and beverage sector with specialized logistic solutions. Their mission is to optimize every link in the supply chain, from farm to shelf, with a commitment to reliability and precision.

The client needed real-time data access for their customer-facing web portal, but their existing system only offered batch Datalake access. Our team designed and implemented a hybrid Azure-based architecture that continuously captures, validates, and versions data changes. This enabled real-time insights, ensured data accuracy through automated business rule checks, and triggered downstream updates via API — empowering both operational visibility and advanced analytics.

One of the client’s IT systems provides only Datalake access, but this data is needed for customer-facing web portal in real time as well as for analytics workload.

As for upstream/source system we have Datalake with files and a metadata database on top of it. The implemented solution has both serverless and server-based architecture and includes continuous delta data capture from the source system with preliminary data validation and deduplication. The data is loaded into a separate database which acts as a staging area as well. Each business entity change on the source is captured and stored as a new version in the staging area. So, there is the possibility to track versions of each entity over time with the information about who made each change and when.

Next step is to process the new version of the entity from the staging layer. During this process, the data is joined with the master data, and at this step we have initial data validation including business rules. Also, early coming facts are handled here. If all checks are passed, the data is pushed/merged into a separate application schema, and after this data is available for querying and reporting. There are also some data-driven conditions that trigger data to be pushed to the downstream system using an API call.

Real-Time Data Availability:

Enabled live data synchronization between upstream systems and the client’s web portal, eliminating batch delays and improving operational responsiveness.

Version Control and Auditability:

Each change to a business entity is versioned with timestamps and user attribution, ensuring full traceability and compliance.

Improved Customer Experience:

End users gained access to fresh data through the client-facing application, enhancing visibility, reducing support queries, and boosting satisfaction.

Actionable Data Triggers:

Data-driven conditions automatically push updates to downstream systems via API, streamlining workflows and reducing manual interventions.