Reference portfolio demonstrating Azure data engineering patterns, Medallion architecture, and end-to-end analytics solutions
Amee Joshi Data Engineering Portfolio is a reference skill that documents production-grade Azure data engineering patterns and architectures for building scalable, cloud-native data platforms. It solves the problem of not having a concrete, worked reference for end-to-end analytics solutions by demonstrating ingestion, transformation, modeling, and analytics using Azure services, Databricks, SQL Server, and BI tools, organized around the Medallion (Bronze-Silver-Gold) architecture.
As documented content, it walks through implementations rather than executing anything: Medallion architecture with Delta Lake, Azure platform components (ADF, ADLS Gen2, Databricks, Synapse Analytics), lakehouse patterns, dimensional modeling with star schemas and slowly changing dimensions (SCD Type 1 and 2), metadata-driven ingestion frameworks, incremental ETL/ELT loading, and Power BI and Tableau reporting. It includes PySpark code sketches for each Medallion layer — raw ingestion with lineage columns into a Bronze Delta table, cleansing and deduplication into Silver, and an SCD Type 2 dimension builder for Gold.
It targets data engineers and analytics engineers who want reference patterns and project structure for Azure lakehouse and data-warehouse work, or who are learning how to design these systems end to end. Because it is a read-only reference portfolio of architectural patterns and illustrative code, with no destructive operations, credential handling, or external calls, it is a benign educational resource.
A reference portfolio of Azure data engineering patterns — Medallion architecture, lakehouse with Delta Lake, dimensional modeling, metadata-driven ingestion, and BI reporting — with illustrative PySpark code for each layer.
Azure Data Factory, ADLS Gen2, Databricks, Synapse Analytics, Delta Lake, SQL Server, and BI tools like Power BI and Tableau.
It is a reference portfolio demonstrating patterns and architecture, with representative code snippets rather than a deployable, end-to-end runnable pipeline.
Data and analytics engineers building Azure lakehouse or data-warehouse platforms, and people learning end-to-end data engineering design patterns.
Yes. It demonstrates dimensional modeling including star schemas and SCD Type 1 and Type 2, with a sample function that applies SCD Type 2 logic in the Gold layer.
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