Fabric Projects
Welcome to my Fabric project portfolio. This page highlights my Fabric Analytics Engineering path through a curated selection of projects focused on scalable architecture and data pipelines within the Fabric environment. I invite you to review my work and reach out to discuss potential future collaborations.
DISCLAIMER
The following case studies are representative scenarios created solely to demonstrate my technical skills, design thinking, and problem-solving approach.
They do not represent real client data, production systems, or proprietary architectures. Any resemblance to actual organizations, datasets, or implementations is purely coincidental.
What if a thirty-million-pound deal was decided on data that was two days old? That's the exact failure this case study solves. I designed and built a fully automated data ingestion pipeline in Microsoft Fabric for a fictional ultra-luxury real estate firm, replacing a fragile, error-prone spreadsheet process with a governed, self-healing architecture — wildcard ingestion, upsert logic, immutable audit trails and PII exclusion, all engineered to close a 48-hour intelligence gap down to zero.
This project delivers an automated, incremental data ingestion solution for Global Freight Forwarders (GFF) using Microsoft Fabric. The solution replaces a manual, inefficient workflow of processing raw JSON shipping logs with a pipeline that intelligently identifies and loads only new files into a Delta Lake table. The outcome enables the operations team to access consolidated, near real-time shipment data without manual intervention, ensuring data integrity and accelerating critical operational analysis.
What happens when nobody's checking the inbox? For most teams, that question doesn't get asked until something's already gone missing. This project is my answer to it — a pipeline that never sleeps on the job, sorting every incoming file, backing it up, and catching anything unfamiliar before it can quietly disappear. It's a small case study with a bigger point: good data engineering isn't about writing clever code, it's about building systems you can trust to work when you're not watching. Take a look through the build below and see how it comes together.
What happens when a company's HR data is only as fresh as its last manual export? At Innovate Solutions, it meant 48-hour turnarounds, spreadsheets full of errors, and salary data floating through email inboxes. This case study walks through how I rebuilt that broken process from the ground up on Microsoft Fabric — engineering a Bronze-to-Silver pipeline that automatically quarantines bad data, resolves duplicates, enriches records with the attributes HR actually needs, and proves, row by row, that nothing ever falls through the cracks.
What happens when a two-hour pipeline stands between your finance team and the numbers they need every morning? In this case study, I tackled that exact problem, transforming a slow, unreliable accounts receivable pipeline into a fast, trustworthy system using Microsoft Fabric. From eliminating network shuffles with broadcast joins to enforcing data quality with Delta Lake and enabling one-command recovery through Time Travel, this project shows how the right architectural decisions can turn a two-hour bottleneck into a system finance leaders can actually rely on.
From five-way SQL joins to a star schema the board can read — this case study walks through how I turned North Meridian Retail Group's biggest reporting headache into a Fabric-powered analytics platform that actually keeps up with the business. If you're curious how a legacy, normalized SQL warehouse becomes a fast, trustworthy Medallion architecture — with metadata-driven pipelines, surrogate keys, pre-aggregated facts, and a Power BI dashboard the CFO can open at 8 AM — dive in to see my project.
A single misplaced decimal in an exchange rate can cost millions — so what happens when that decimal is being copied by hand from a public website into a spreadsheet, every single day? That was the reality at Global Credit Corp before this project, and it's the exact gap this pipeline closes. No Spark, no notebooks, no unnecessary complexity — just a T-SQL warehouse doing what it does best, parsing live market data straight from an API and turning it into a clean, audit-ready system of record the Risk desk can actually stand behind. Curious how a stored procedure and a loop can replace hours of manual work and eliminate a multi-million dollar blind spot? Take a look and see how it came together.
A retailer that can't trust its own inventory data is a retailer making million-dollar decisions in the dark. This case study walks through exactly how I closed that gap for RetailCore Ltd — swapping a fragile, human-dependent CSV export for a governed, self-service Fabric Pipeline. If you're curious what it actually takes to connect an on-premise database to the cloud securely, design a pipeline a non-technical warehouse supervisor can run with confidence, and build in the kind of governance that survives an audit — take a look through the full build below.
What happens when a regulator asks a question your data architecture simply can't answer? For Veritas Global, that question was about a single date in October — and the honest answer was silence. This case study walks through how I closed that gap: from a governed view that shrugs off dirty source data, to a recursive hierarchy that traces accountability five levels deep, to an SCD Type 2 pattern that makes sure history never gets overwritten again. It's not just about clean queries — it's about building systems that can be trusted when it actually matters. Take a look and see how the pieces come together.
What does it actually cost a business when it can't trust its own customer data? For TelcoPrime, it meant wasted marketing dollars, shaky churn predictions, and engineers stuck firefighting the same problems on repeat. This case study walks through how a low-code Dataflow Gen2 pipeline turned that mess into a clean, dependable customer dataset — standardizing addresses, fixing state values, validating phone numbers, and flagging exactly which records could be trusted. It's a practical look at how data engineering solves real business pain, not just technical checkboxes. Take a look and see how it all comes together.
Twenty minutes doesn't sound like much until you're trading against a market that's already moved on without you. That's the gap this project set out to close — swapping a batch pipeline that refreshed every 15 to 45 minutes for a live streaming feed that reacts as prices change, tick by tick. If you've ever wondered what it actually takes to build real-time infrastructure in Microsoft Fabric — from wiring an Eventstream pipeline, to writing KQL queries that catch bad data before it spreads, to standing up a real-time intelligence dashboard a trading desk could genuinely rely on — this case study walks through exactly that. Take a look and see how it came together.
