Part-4: Real time end to end Azure Data Engineering Project
4.9 هزار بار بازدید -
9 ماه پیش
-
Welcome to AnalytixCloud, In this
Welcome to AnalytixCloud, In this hands-on tutorial, we'll dive into a real-time project on Azure Data Engineering. Whether you're an experienced data engineer looking to expand your skills or a newcomer to Azure, this project will provide you with valuable insights and practical experience.
This is the continuation to our 3 parts shared earlier. If you haven't watched it yet. We recommend to watch these videos before this :
Part-1 : Part-1: Real time end to end Azure Da...
Part-2: Part-2 : Resource Provisioning || Re...
Part-3: Part-3: Real time end to end Azure Da...
🚀 Unlocking Supply Chain Insights with Unified Pipeline: Bronze, Silver, Gold Layers 🚀
In this video, we showcase a single dynamic pipeline designed to handle both full load and incremental data processing, ensuring seamless integration into your supply chain analytics. Our pipeline transforms raw data into refined layers - Bronze, Silver, and Gold, enabling powerful analytics and business intelligence.
🔗 Bronze Layer: Raw to Refined 🔗
We start by mounting the raw data from the Azure Data Lake Storage using Databricks notebook.
The Bronze layer focuses on data quality, type validation, cleaning, enrichment, and partitioning.
Script validates data types, handles missing values, and ensures the integrity of the raw data.
💿 Silver Layer: Business Rule Application 💿
Moving to the Silver layer, we read Delta tables from the Bronze layer.
Business rules are applied to enhance the data's quality and relevance.
Examples include prioritizing transportation modes, categorizing product prices, and calculating total costs for purchase orders.
🏆 Gold Layer: Advanced Analytics & Recommendations 🏆
The Gold layer combines Silver layer data for comprehensive insights.
Advanced analytics showcase:
Average services provided by suppliers.
Top material providers and their rankings.
Ranking of suppliers based on the total number of services provided.
We calculate a Recommendation Score for each supplier based on various factors and create a ranking.
📊 Visualizing Insights & Final Thoughts 📊
The Gold layer data is saved in Delta Lake, enabling efficient querying and analytics.
SQL queries and interactive visualizations provide a deeper understanding of supplier performance.
Concluding with a demonstration of visualizing top suppliers based on the Recommendation Score.
🔗 Project Files and Resources 🔗
Find the complete script, data processing steps, and code snippets on our GitHub repository (https://github.com/AnalytixCloud/ADF_... ).
Don't forget to subscribe to Analytix Cloud for more exciting projects and insights!
🔜 Coming Next: ADF Best Practices & Interview Scenarios! 🔜
Stay tuned for the next part of our series, where we'll delve into Azure Data Factory (ADF) best practices. Discover the optimal ways to design, implement, and manage data pipelines for enhanced efficiency and performance.
🚀 What to Expect:
ADF Best Practices: Unlock the secrets to maximizing the potential of Azure Data Factory. From design considerations to optimization tips, we'll guide you through best practices for seamless data integration.
Interview-Based Scenarios: Whether you're preparing for an interview or looking to enhance your data engineering skills, we'll present real-world scenarios and solutions. Get ready to tackle common challenges and showcase your expertise in ADF.
You can also visit our website for our courses: https://www.analytixcloud.com
Mob: +91-7411310205
Telegram: https://t.me/azuredatafactory_azureda...
Whatsapp: https://www.youtube.com/redirect?even...
Please support us for more videos: @AnalytixCloud
Don't forget to like, subscribe, and hit the notification bell to stay updated with our latest Azure and data engineering tutorials. If you have any questions or need further clarification on any topic covered in this video, please feel free to leave a comment below, and we'll be happy to assist you.
Thank you for watching, and let's dive into the world of Azure Data Engineering together.
This is the continuation to our 3 parts shared earlier. If you haven't watched it yet. We recommend to watch these videos before this :
Part-1 : Part-1: Real time end to end Azure Da...
Part-2: Part-2 : Resource Provisioning || Re...
Part-3: Part-3: Real time end to end Azure Da...
🚀 Unlocking Supply Chain Insights with Unified Pipeline: Bronze, Silver, Gold Layers 🚀
In this video, we showcase a single dynamic pipeline designed to handle both full load and incremental data processing, ensuring seamless integration into your supply chain analytics. Our pipeline transforms raw data into refined layers - Bronze, Silver, and Gold, enabling powerful analytics and business intelligence.
🔗 Bronze Layer: Raw to Refined 🔗
We start by mounting the raw data from the Azure Data Lake Storage using Databricks notebook.
The Bronze layer focuses on data quality, type validation, cleaning, enrichment, and partitioning.
Script validates data types, handles missing values, and ensures the integrity of the raw data.
💿 Silver Layer: Business Rule Application 💿
Moving to the Silver layer, we read Delta tables from the Bronze layer.
Business rules are applied to enhance the data's quality and relevance.
Examples include prioritizing transportation modes, categorizing product prices, and calculating total costs for purchase orders.
🏆 Gold Layer: Advanced Analytics & Recommendations 🏆
The Gold layer combines Silver layer data for comprehensive insights.
Advanced analytics showcase:
Average services provided by suppliers.
Top material providers and their rankings.
Ranking of suppliers based on the total number of services provided.
We calculate a Recommendation Score for each supplier based on various factors and create a ranking.
📊 Visualizing Insights & Final Thoughts 📊
The Gold layer data is saved in Delta Lake, enabling efficient querying and analytics.
SQL queries and interactive visualizations provide a deeper understanding of supplier performance.
Concluding with a demonstration of visualizing top suppliers based on the Recommendation Score.
🔗 Project Files and Resources 🔗
Find the complete script, data processing steps, and code snippets on our GitHub repository (https://github.com/AnalytixCloud/ADF_... ).
Don't forget to subscribe to Analytix Cloud for more exciting projects and insights!
🔜 Coming Next: ADF Best Practices & Interview Scenarios! 🔜
Stay tuned for the next part of our series, where we'll delve into Azure Data Factory (ADF) best practices. Discover the optimal ways to design, implement, and manage data pipelines for enhanced efficiency and performance.
🚀 What to Expect:
ADF Best Practices: Unlock the secrets to maximizing the potential of Azure Data Factory. From design considerations to optimization tips, we'll guide you through best practices for seamless data integration.
Interview-Based Scenarios: Whether you're preparing for an interview or looking to enhance your data engineering skills, we'll present real-world scenarios and solutions. Get ready to tackle common challenges and showcase your expertise in ADF.
You can also visit our website for our courses: https://www.analytixcloud.com
Mob: +91-7411310205
Telegram: https://t.me/azuredatafactory_azureda...
Whatsapp: https://www.youtube.com/redirect?even...
Please support us for more videos: @AnalytixCloud
Don't forget to like, subscribe, and hit the notification bell to stay updated with our latest Azure and data engineering tutorials. If you have any questions or need further clarification on any topic covered in this video, please feel free to leave a comment below, and we'll be happy to assist you.
Thank you for watching, and let's dive into the world of Azure Data Engineering together.
9 ماه پیش
در تاریخ 1402/09/02 منتشر شده
است.
4,988
بـار بازدید شده