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https://github.com/kirolos00daniel/tokyo-olympic-analysis-2020-azure
Tokyo Olympics Analysis Using AZURE platform
https://github.com/kirolos00daniel/tokyo-olympic-analysis-2020-azure
azure azuredatabricks azuredatafactory azurestorage databricks powerbi python sql
Last synced: about 1 month ago
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Tokyo Olympics Analysis Using AZURE platform
- Host: GitHub
- URL: https://github.com/kirolos00daniel/tokyo-olympic-analysis-2020-azure
- Owner: Kirolos00Daniel
- Created: 2024-09-28T15:20:08.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-11-09T13:34:24.000Z (2 months ago)
- Last Synced: 2024-11-09T14:29:32.324Z (2 months ago)
- Topics: azure, azuredatabricks, azuredatafactory, azurestorage, databricks, powerbi, python, sql
- Homepage:
- Size: 28.7 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Security: SECURITY.md
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README
# 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.