This repository demonstrates a comprehensive real-time Data Engineering Project using various Azure technologies. The project is structured to provide a detailed understanding of how to utilize different Azure services to design and build a fully operational data engineering pipeline. It's particularly useful for professionals preparing for Azure Data Engineering interviews or looking to include a real-world project on their resumes.
The goal is to migrate data from an on-premise SQL Server database to the Azure cloud, transform it, and analyze it using popular Azure tools. The project follows a typical data lakehouse architecture, leveraging Azure Data Factory, Azure Databricks, Azure Synapse Analytics, Azure Data Lake Gen2, and Power BI to handle different stages of data ingestion, transformation, storage, and reporting.
The project simulates one of the most common real-world scenarios: migrating an on-premise SQL Server database to the cloud. The database consists of six or seven tables, which are copied to Azure Data Lake Gen2 using Azure Data Factory.
More detail in this repository
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Data Ingestion:
- Using Azure Data Factory, data from the on-premise SQL Server is ingested and stored in the Bronze layer of the Azure Data Lake.
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Bronze, Silver, and Gold Layers:
- The Bronze layer holds raw data, serving as the source of truth.
- Data transformations occur in the Silver layer, where basic modifications such as renaming columns and changing data types are performed using Azure Databricks.
- The Gold layer contains the clean, curated data, ready for analytics and reporting.
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Data Transformation:
- Azure Databricks is used to perform various data transformations using PySpark. The code modifies the raw data into usable, cleaned data that can be further processed.
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Data Loading:
- The final transformed data from the Gold layer is loaded into Azure Synapse Analytics, where a database and tables are created to store the data.
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Data Reporting:
- Using Power BI, reports are created based on the clean data stored in Azure Synapse Analytics, visualizing insights through charts, dashboards, and various other data representation formats.
- This is sample Dashboard
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Security and Governance:
- Azure Active Directory (AAD) manages identity and access control.
- Azure Key Vault is used for securely storing sensitive information like connection strings and passwords.
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Automation:
- Pipelines are automated such that when new rows are added to the on-premise database, the entire pipeline—data ingestion, transformation, and reporting—executes to reflect the updates in Power BI in real-time.
- Data Lakehouse Architecture: Understanding the Bronze, Silver, and Gold layers for data storage and transformation.
- Real-Time Data Engineering: Building a pipeline that responds to real-time data updates.
- Azure Integration: How to seamlessly use Azure services to build a scalable and secure data engineering solution.
- Power BI Integration: Creating effective reports and dashboards.
- Data Engineers looking to enhance their skills with Azure tools.
- Professionals preparing for data engineering interviews.
- Anyone looking to understand real-world use cases for data lakehouse architectures in Azure.
- YouTube Channel: Mr. K Talks Tech