Tokoyo-Olympic-Analysis-2020-Azure

Tokyo Olympics Analysis Using AZURE platform

Azure Data Pipeline Project

Overview

This project demonstrates an end-to-end data pipeline using Azure services for data ingestion, transformation, and analytics. The pipeline is designed to facilitate data flow from various sources into a data lake, transform the data, and finally visualize it through dashboards using Power BI.

Architecture

1. Data Source

• Description: The data source can be any structured or unstructured data, such as databases, files, or external APIs.
• Role: It serves as the initial point of data entry into the pipeline.

2. Data Ingestion with Azure Data Factory

• Azure Data Factory: A cloud-based data integration service used to create data-driven workflows for orchestrating and automating data movement and transformation.
• Raw Data Store: The ingested data is stored in its raw format in the Data Lake Gen 2.

3. Raw Data Store (Data Lake Gen 2)

• Description: Azure Data Lake Storage Gen 2 provides a highly scalable and secure data lake for big data analytics.
• Role: It stores the ingested raw data, serving as a staging area before transformation.

4. Data Transformation with Azure Databricks

• Azure Databricks: An Apache Spark-based analytics platform optimized for Azure. It is used for large-scale data processing and machine learning.
• Role: It performs data transformations, cleaning, and enrichment before loading the transformed data back into the Data Lake.

5. Transformed Data Store (Data Lake Gen 2)

• Description: After transformation, the clean and structured data is stored back in Data Lake Gen 2.
• Role: This data serves as the source for further analytics and reporting.

6. Analytics with Azure Synapse Analytics

• Azure Synapse Analytics: An integrated analytics service that accelerates time to insight across data warehouses and big data systems.
• Role: It provides data exploration and analytics capabilities, enabling complex queries and insights on the transformed data.

7. Dashboards and Reporting

• Tools: Power BI, Looker Studio, Tableau.
• Role: These tools are used to create interactive dashboards and reports for data visualization and business insights.

How It Works

1. Data Ingestion:
   • Data from various sources is ingested using Azure Data Factory and stored in a raw format in Data Lake Gen 2.

2. Data Transformation:
   • Azure Databricks processes the raw data, performing transformations such as filtering, aggregation, and data enrichment.

3. Data Storage:
   • Transformed data is stored in Data Lake Gen 2, making it ready for analytics and reporting.

4. Data Analytics:
   • Azure Synapse Analytics is used for querying and analyzing the transformed data, creating a bridge between big data and data warehousing.

5. Data Visualization:
   • Visualization tools such as Power BI, Looker Studio, and Tableau connect to Azure Synapse Analytics for creating dashboards that provide insights into the data.

Prerequisites

• Azure Subscription: Required for accessing Azure services like Data Factory, Databricks, and Synapse Analytics.
• Data Sources: Identify and configure the data sources for ingestion.
• Visualization Tools: Power BI, Looker Studio, or Tableau installed and configured.

Setup and Deployment

1. Provision Azure Services:

   • Set up Azure Data Factory, Data Lake Gen 2, Databricks, and Synapse Analytics within your Azure subscription.

2. Configure Data Ingestion:

   • Create pipelines in Azure Data Factory to ingest data from the source to the Data Lake.

3. Create Databricks Notebooks:

   • Develop Databricks notebooks for data transformation processes and connect them to the raw data stored in Data Lake.

4. Setup Synapse Analytics:

   • Configure Azure Synapse Analytics to connect with the transformed data in Data Lake for querying and analysis.

5. Build Dashboards:

   • Use visualization tools to connect to Synapse Analytics and create interactive dashboards and reports.

Conclusion

This project outlines a scalable and efficient data pipeline using Azure's suite of tools. It allows for seamless data ingestion, transformation, and visualization, enabling businesses to gain valuable insights from their data.