In this blog post, I will walk you through a data engineering project that leverages various azure services to extract, transform, and load (ETL) COVID-19 patient details from a website. We will store the raw data in Azure Data Lake Storage Gen2, transform it using Azure Databricks and HDInsight, and finally load it into Azure SQL Database for visualization and further machine learning (ML) processes. The Below Image Show the Architecture of project.
Project Overview
The goal of this project is to create a robust data pipeline on Azure, utilizing its various services to handle each stage of the ETL process. Here is the high-level architecture of our solution:
- Data Extraction: Extract COVID-19 patient details from a website using Azure Data Factory (ADF).
- Data Storage: Store the raw data in Azure Data Lake Storage Gen2.
- Data Transformation: Clean and transform the data using Azure Databricks and HDInsight.
- Data Loading: Load the transformed data back into Azure Data Lake Storage Gen2 for ML and into Azure SQL Database for visualization
Step-by-Step Implementation
1. Data Extraction with Azure Data Factory
Azure Data Factory (ADF) is a powerful cloud-based ETL service that allows you to create data-driven workflows for orchestrating data movement and transformation.
- Create a Data Factory: Start by creating a new Data Factory instance in the Azure portal.
- Set up Linked Services: Configure linked services to connect to the source website and Azure Data Lake Storage Gen2.
- Create a Pipeline: Develop a pipeline that uses a web activity to extract data from the COVID-19 patient details website. The pipeline should also include a copy activity to move the extracted data into Azure Data Lake Storage Gen2.
Click here to see how we can create a data factory and setting up linked services
2. Data Storage in Azure Data Lake Storage Gen2
Azure Data Lake Storage Gen2 combines the capabilities of a high-performance file system with massive scale and economy to help you speed up your big data analytics.
- Create a Data Lake Storage Account: Set up a new Data Lake Storage Gen2 account.
- Create Containers: Organize your data by creating containers within your storage account. For this project, you might create containers such as
raw-data,transformed-data, andml-data.
3. Data Transformation with Azure Databricks and HDInsight
Data transformation is a critical step in the ETL process. Azure Databricks and HDInsight provide scalable and efficient solutions for big data processing and transformation.
- Set up Azure Databricks: Create a Databricks workspace in the Azure portal. Develop notebooks to clean and transform the raw COVID-19 data. Use Apache Spark to handle large datasets and perform operations such as filtering, aggregating, and joining data.
- Leverage HDInsight: For additional transformation needs, set up an HDInsight cluster. HDInsight supports a variety of open-source frameworks such as Hadoop, Spark, and Hive, providing flexibility in data processing
4. Data Loading into Azure Data Lake Storage Gen2 and Azure SQL Database
Once the data is transformed, it needs to be loaded back into Azure Data Lake Storage Gen2 for machine learning and into Azure SQL Database for visualization and reporting.
- Store Transformed Data: Save the cleaned and transformed data into the
transformed-datacontainer in Azure Data Lake Storage Gen2. - Load Data into Azure SQL Database: Set up an Azure SQL Database instance and create tables to hold the transformed data. Use Azure Data Factory to create a pipeline that copies the data from Data Lake Storage Gen2 to the SQL database.
Visualization and Machine Learning
With the transformed data in Azure SQL Database, you can use tools like Power BI to create interactive dashboards and reports for visualization. Additionally, the data in Data Lake Storage Gen2 can be used for machine learning models to gain deeper insights into COVID-19 trends and patterns.
Conclusion
Building a comprehensive data engineering pipeline on Azure enables you to efficiently manage and analyze large datasets. By leveraging services like Azure Data Factory, Data Lake Storage Gen2, Databricks, HDInsight, and Azure SQL Database, you can create a scalable and flexible solution for extracting, transforming, and loading data. This project demonstrates the power of Azure’s ecosystem in handling real-world data engineering challenges.
Feel free to reach out with any questions or feedback on this project. Happy data engineering!
