Case Study: Building the Data Vault Framework – A Fully Automated No‑Code Data Vault 2.0 Solution
Background Organizations implementing Data Vault 2.0 often face challenges involving complex SQL development, metadata management, and the orchestration of Hubs, Links, and Satellites. The client requested a modular, visual, and no‑codesolution that allows data analysts to build and manage a full Data Vault 2.0 environment—without writing code. Solution Our team designed and engineered the Data Vault Framework—a secure, scalable, automated platform for building Data Vault 2.0 environments through a fully visual interface. Architecture Overview Execution Outcome High‑level architecture (JWT + LDAP auth; REST between React/Express and Application Server, and between Workers and Application Server).
Case Study: Real-Time Automated Invoice Approval
1. Business Context A global organization specializing in OCR, business automation, and data engineering needed a system to automatically approve or reject supplier invoices by matching them against existing purchase orders. 2. Problem Statement Invoices are sent by suppliers using inconsistent product names. Each invoice needs to be validated against one or more purchase orders to ensure the correct items, quantities, and organizational units were billed. Partial deliveries and reuses of PO numbers further complicate validation. Manual checks were slow and error-prone. 3. Solution Architecture We designed a fully asynchronous, microservice-based architecture. The system listens in real-time to Kafka topics where OCR-parsed invoice XMLs are streamed. The central Orchestrator service coordinates the workflow, delegating tasks to stateless services: Each step is managed by the Orchestrator, which uses an internal repository and state machine to handle recovery and process continuation. The final decision is sent to a Kafka topic (Approve/Reject), used by downstream systems. 4. Key Characteristics 5. Outcome The system automated the invoice approval process, reducing processing time from hours to seconds. The fuzzy matching ensured high accuracy despite naming inconsistencies. Human involvement was eliminated, resulting in fewer errors and a consistent audit trail. The client continued to expand the platform after delivery. High‑level architecture (JWT + LDAP auth; REST between React/Express and Application Server, and between Workers and Application Server).
Case Study: Research Data Management System
1. Business Need A healthcare organization needed to improve its process for accessing and retrieving data for epidemiological research. The existing process was manual – field selection, population filtering, and structuring the dataset – all performed via Word documents and email threads, resulting in inconsistencies, errors, and inefficiencies across research teams. 2. The Challenge 3. The Solution Phase 1: ETL to Data Lake Automated scheduled ETL using Talend from various data sources into Cloudera Hadoop. Data is standardized and stored in Parquet format for structured access. Phase 2: Web-Based Research Management A full-featured web application (React + Spring Boot) was developed. Phase 3: Output Generation The system generates both HTML previews and XLSX files for structured data delivery. Final outputs are sent to the data engineering team for extraction execution. 4. Results 5. Technologies Used