Project Documentation Introduction This documentation provides an overview of the project that involves data processing and transformation using various Azure services and Databricks. The project consists of several key steps, including data ingestion, data transformation, and data storage, which are orchestrated using Azure Data Factory, Azure Databricks, and Azure Synapse Analytics.

Project Workflow The project workflow can be summarized into the following steps:

Step 1: Azure Data Factory Setup Objective: Set up Azure Data Factory (ADF) to orchestrate data movement from an external source to Azure Data Lake Storage Gen2. Details: In this step, an Azure Data Factory is created to manage data movement tasks. A pipeline is defined to copy raw data from a GitHub repository URL to an Azure Data Lake Storage Gen2 container. Step 2: Data Movement to Azure Data Lake Gen2 Objective: Copy raw data from an external source (GitHub) to Azure Data Lake Storage Gen2. Details: A pipeline in Azure Data Factory is executed to move data from a GitHub repository to the designated folder in Azure Data Lake Storage Gen2. This step ensures that raw data is available for further processing. Step 3: Azure Databricks Setup Objective: Create an Azure Databricks instance for data transformation and processing. Details: In this step, an Azure Databricks workspace is set up, which provides a collaborative environment for data engineers and data scientists to work on data transformation tasks. Step 4: Data Ingestion into Azure Databricks Objective: Mount data from Azure Data Lake Storage Gen2 to Azure Databricks for data processing. Details: An Azure Databricks cluster is created and configured. Application registration is used to securely mount the data from Azure Data Lake Storage Gen2 into Azure Databricks, making the data accessible for analysis and transformation. Step 5: Data Transformation in Azure Databricks Objective: Transform the raw data using Azure Databricks notebooks. Details: Data engineers and data scientists collaborate to read and transform the raw data using PySpark in Azure Databricks notebooks. Data types are adjusted, and any necessary transformations are applied to prepare the data for analysis. Step 6: Data Loading into Azure Synapse Analytics Objective: Load the transformed data into Azure Synapse Analytics for analytics and reporting. Details: The transformed data is loaded into Azure Synapse Analytics (formerly known as SQL Data Warehouse) to facilitate data analytics, reporting, and visualization. Step 7: Data Storage and Organization Objective: Organize and store the data in Azure Data Lake Storage Gen2. Details: The transformed data is organized into structured directories within Azure Data Lake Storage Gen2 for improved data management and accessibility. Code Samples Below are code samples that demonstrate key tasks within the project:

Mounting Data from Azure Data Lake to Azure Databricks python Copy code

configs = { "fs.azure.account.auth.type": "OAuth", "fs.azure.account.oauth2.provider.type": "org.apache.hadoop.fs.azurebfs.oauth2.ClientCredsTokenProvider", "fs.azure.account.oauth2.client.id": "YOUR_CLIENT_ID", "fs.azure.account.oauth2.client.secret": "YOUR_CLIENT_SECRET", "fs.azure.account.oauth2.client.endpoint": "YOUR_AUTH_ENDPOINT" }

directory_to_check = "/mnt/olympics" mounted_directories = [mount.mountPoint for mount in dbutils.fs.mounts()]

if directory_to_check in mounted_directories: print(f"The directory {directory_to_check} is already mounted.") else: dbutils.fs.mount( source="abfss://[email protected]", mount_point="/mnt/olympics", extra_configs=configs ) Data Transformation Using PySpark python Copy code from pyspark.sql.functions import col from pyspark.sql.types import IntegerType

entriesgender_df = entriesgender_df.select( col("Discipline"), col("Female").cast(IntegerType()).alias("Female"), col("Male").cast(IntegerType()).alias("Male"), col("Total").cast(IntegerType()).alias("Total") ) Data Loading into Azure Synapse Analytics python Copy code

athletes_df.repartition(1).write.mode("overwrite").option("header", 'true').csv("/mnt/olympics/transformed-data/athletes") Conclusion This project demonstrates the end-to-end data processing and transformation workflow using Azure services such as Azure Data Factory, Azure Databricks, and Azure Synapse Analytics. It highlights the steps involved in ingesting, transforming, and storing data for analytics and reporting purposes. The provided code samples illustrate key tasks within the projec