Lakehouse

Online Retail Lakehouse & Analytics Pipeline

Transactional analytics processing over 540K records with Power BI

Online retail analytics pipeline from Azure Databricks Lakehouse to Power BI

541,909

Raw Transactions Ingested

527,390

Validated Records Cleaned

Project Overview

Engineered an Azure Databricks Lakehouse solution for an online retail dataset. The pipeline ingests raw transaction feeds, identifies cancellations, handles missing values, and applies deduplication logic. Out of 541,909 raw transactions ingested into Bronze, the Silver layer validated 527,390 clean records. Aggregated Gold datasets drive revenue dashboards and customer behavior models directly inside Power BI.

The Problem

Transaction volumes contained duplicate orders, cancellation records, and missing customer IDs, causing skewed revenue metrics and inaccurate product performance reporting.

The Solution & Architecture

Architected a multi-stage Azure Databricks pipeline that filters non-standard transactions, executes Delta MERGE operations for incremental updates, and delivers audited sales KPIs to Power BI.

System Data Flow:

Raw Retail Transactions→Azure Databricks Ingestion→Bronze Layer→Silver Layer (527k Validated)→Gold Layer (KPIs)→Power BI Dashboards

Key Engineering Highlights

Processed approximately 541,909 raw retail transactions

Produced 527,390 valid records after automated cleansing and filtering

Removed duplicate transactions and filtered out cancellation orders

Handled missing values and malformed records systematically

Implemented full Bronze/Silver/Gold Lakehouse architecture

Used Delta MERGE for incremental data processing and stateful updates

Built curated revenue, customer analytics, and product performance datasets

Connected Gold-layer data tables to Power BI dashboards

Technologies Used

Azure DatabricksPySparkSQLSpark SQLDelta LakePower BI

Interested in this Pipeline?

I can build similar production-grade Lakehouse or API data ingestion systems for your organization.

Contact Owais Ahmad