Unlocking the Power of Azure for Scalable Data Solutions
Cloud computing has revolutionized the way we store, process, and analyze data, and Azure stands at the forefront of this transformation. My expertise with Azure Data Factory, Azure Databricks, and Azure Synapse Analytics enables me to design end-to-end data solutions that streamline workflows, enhance data integrity, and unlock powerful insights.
On this page, you'll find a collection of Azure-driven projects, where I harness the power of data pipelines, machine learning, automation, and big data analytics to deliver innovative, scalable, and efficient solutions.
Let’s explore how Azure fuels data-driven innovation!
Project: ETL Pipeline with Azure Data Factory
Objective:
Design and implement an automated ETL process using Azure Data Factory to extract, transform, and load PayPal Payments and PayPal Products datasets into a structured database for analysis.
Key Components:
Data Sources:
PayPal Transactions: Dataset sourced from Azure storage.
PayPal Products: Dataset added via source settings in Azure.
Transformations:
Created a data flow named PayPal Transformation.
Used Left Inner Join to merge PayPal Transactions and PayPal Products datasets.
Applied filters to analyze only "Paid" transactions using the condition: equals(status, "Paid")in the Expression Builder.
Sink:
Configured the data sink named Transfer to DB using Azure SQL Database.
Converted CSV-formatted data into a structured database format, using schema "staging" and table name "PayPal Data".
Pipeline:
Created and published the PayPal Transformation Pipeline.
Scheduled triggers to automate the ETL process and ensure final data integrity.
Pipeline Orchestration:
Designed PayPal Copy Pipeline to integrate multiple pipeline executions.
Added activities to automate Copy Data tasks and trigger PayPal Transformation Pipeline upon successful completion.
Implemented a failure handling mechanism(Copy Data Failure) to generate error messages when the transformation fails.
Extracting & Exporting Processed Data:
Configured Azure SQL Database as the source, using the dbo schema and the customers table.
Created a sink with an Inline Dataset of Delimited Text, linking it to Azure Blob Storage df.
Specify the destination storage for exported files.
Ensured final data was transferred to a CSV file for external use.
Skills and Tools Utilized:
Azure Data Factory(Data Flows, Pipelines, Datasets, Expression Builder)
SQL Database Integration
Data Transformation and Filtering Techniques
End-to-End Automation of ETL Processes
Outcome:
Successfully designed and executed an ETL pipeline within Azure Data Factory, resulting in structured, analyzable data stored in a SQL database. The pipeline orchestration ensures seamless automation, data integrity, and efficient workflow execution.