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Enterprise ETL Platform for Retail Analytics

Designed and implemented an enterprise-grade ETL pipeline using Databricks to integrate POS, P&L, and promotional data into analytics-ready datasets. The solution was built for a top global management consulting firm and designed to support multiple enterprise clients, each with configurable pipelines tailored to their business needs.

The platform serves as a centralized data processing layer, providing consistent, high-quality data to downstream dashboards and analytical tools across different domains, geographies, and product categories.

Problem Context

Enterprise retail analytics often relies on data from multiple independent systems:

  • Point-of-sale data from third-party providers

  • Financial data from retailers or distributors

  • Promotional data from manufacturers or client-owned systems

 

These datasets typically:

  • Arrive at different granularities

  • Follow different refresh cycles

  • Require extensive validation and standardization before use

 

The objective was to build a scalable and configurable ETL framework that could reliably ingest, validate, transform, and integrate these datasets while supporting multiple clients and use cases.

My Role

  • Designed and implemented the end-to-end ETL pipeline using Databricks

  • Built modular, reusable PySpark-based transformation logic

  • Implemented a config-driven validation and quality framework

  • Enabled pipeline configurability to support multiple clients and use cases

  • Automated orchestration using Databricks Jobs and APIs

Architecture & Implementation

The platform was architected to support enterprise-scale analytics use cases across multiple clients and cloud environments.

high-level-etl-architecture_edited_edite

High-level architecture of a configurable, enterprise-grade ETL platform built on Databricks

High-Level Architecture

  • Implemented using PySpark exclusively (no Spark SQL)

  • All intermediate and final datasets stored as Delta tables

  • Modular notebook-based design:

    • One notebook per ETL step

    • Shared functions abstracted into reusable notebooks

​

​Configurable Pipelines

  • Pipeline execution controlled via:

  • Databricks notebook widgets

  • Configuration files

  • Supports multiple pipelines per client (e.g., by geography or product category)

​

Multi-Pipeline Data Design

  • Data for all pipelines stored in shared Delta tables

  • Distinguished using a pipeline identifier column

  • Partitioned by pipeline for efficient access and scalability

Data Sources & Ingestion

  • Input format: Standardized CSV files

    • Core datasets:

    • POS data (e.g., market / transaction-level data)

    • P&L data (financial aggregates)

    • Promotional data (campaign-level inputs)

​

Schema Management

  • Schemas are largely standardized across clients

  • Schema definitions and validations are configurable via Excel-based templates

  • Schema updates can be applied without changing core ETL code

​

Storage Compatibility

  • Input files are uploaded to:

    • Azure Data Lake Storage (ADLS), or

    • Databricks Unity Catalog

  • The pipeline dynamically supports Unity Catalog or Hive Metastore, based on client architecture.

Transformation & Processing

etl-workflow.png

End-to-end transformation flow from raw ingested datasets through stepwise processing to analytics-ready outputs consumed by downstream tools and dashboards.

The pipeline processes data in incremental, well-defined steps:

  1. Bronze Layer

    • Raw standardized CSV ingestion

    • Stored as Delta tables

  2. Silver Layer

    • Stepwise joins across POS, P&L, and promotional datasets

    • Each dataset processed and joined in a separate ETL stage

    • Handles differences in grain and refresh cycles

    • Calculation of derived metrics and business logic

    Additional analytical datasets—generated outside the ETL via modeling or analytical processes—are joined to enrich computations.

  3. Gold Layer

    • Final computed, analytics-ready datasets

    • Used as input for dashboards and downstream analytical tools

Validation & Data Quality

Before ingestion into the main pipeline, all inputs pass through a staging layer:

  • File-level validations executed during upload

  • Validation rules defined via configuration files

  • Only validated data is promoted to the final input schema

​

Quality Checks

  • Basic checks: nulls, ranges, value constraints

  • Relationship checks: one-to-one and many-to-one mappings, foreign key integrity

  • Advanced checks: configurable SQL-based validations applied at different pipeline stages

  • This approach ensures data consistency while remaining flexible across clients.​

Orchestration & Automation

  • Pipelines orchestrated using Databricks Jobs

  • Jobs are:

    • Created and managed programmatically

    • Automated using Databricks APIs

  • Enables scalable onboarding of new pipelines and consistent execution across environments

Outcome

  • Delivered a reliable, configurable ETL platform for enterprise retail analytics

  • Enabled consistent, analytics-ready datasets across multiple clients and use cases

  • Reduced manual data preparation through standardized ingestion and validation

  • Provided a scalable foundation for dashboards, reporting, and further analytical modeling

Key Technologies

  • Databricks

  • PySpark

  • Delta Lake

  • Azure Data Lake Storage

  • Databricks Jobs & APIs

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