Azure Data Engineering Mastery Real-World Projects

Real Projects, Tangible Skills: Accelerate Career Growth with Comprehensive Azure Data Engineering Training
What you'll learn:
A Solid Grasp of Azure Basics
Hands-on Experience in deploying and using multiple AWS services
Learn integration of different source for dynamic fetching of data
Exposure with transformation and building insights of source data using cloud
Requirements:
No prior experience with Azure is required, but a basic understanding of Python and PySpark will be helpful.
Description:
Introduction: Welcome to Azure Projects for Data Engineering, your gateway to mastering data engineering with Microsoft Azure! In today's data-driven world, organizations crave skilled professionals who can harness data to drive insights and innovation. Azure offers a robust suite of tools tailored for data engineering tasks, from ingestion to analysis. Our course equips you with the skills to excel in this dynamic field using Azure services.Motivation: Data engineering is pivotal in transforming data into actionable insights for business growth. As demand for Azure-savvy data engineers rises, mastering Azure Projects for Data Engineering opens doors to exciting career opportunities. With hands-on projects and practical exercises, you'll develop the expertise needed to tackle real-world challenges and drive impactful outcomes.Why Choose This Course? Our course stands out for its hands-on approach and real-world projects. From app analytics to transportation insights, you'll explore diverse domains using Azure services like Data Factory and Databricks. Gain practical skills and build a compelling portfolio to showcase your expertise to potential employers. Whether you're launching a new career or upskilling, our flexible learning environment caters to your goals and aspirations.Course Highlights:Hands-on projects covering app analytics, sports data, developer surveys, and transportation insights.In-depth exploration of Azure services including Data Factory, Databricks, and Azure Storage.Insights from seasoned data engineering professionals.Flexible, self-paced learning with interactive assignments.Opportunities for collaboration and networking with peers and industry experts.Projects1. Title: Play Store Dataset AnalysisDescription:Integrate azure service using key vault that provide encrypted security layer. Then, build a copy activity pipeline using azure data factory (ADF) that will dynamically fetch compressed tar.gzip file from GitHub and upload it to container within a storage account. Mount the container on databricks, and pre-processing it to make data accurate and reliable. Then we build insights for better data understanding. As,Installs Analysis:• What is the most installed category of apps?• Top 5 Apps in the top 5 installed category.Rating Analysis:• What is the most rated category of apps?• Top 5 Apps in the top 5 rated category.Free vs. Paid Apps Analysis:• What is the proportion of free vs. paid apps available?• Distribution of Paid and Free Apps in Each Category.• Do paid apps have higher average ratings than free apps?Price Analysis:• What is the average price of paid apps in different categories?Key Features:a. Integration of key vault with other servicesb. GitHubc. Copy compressed data using data factory to storage accountd. Mounting containere. Play Store Insight2. Title: Olympics Dataset AnalysisProject Description:API request to fetch dataset from kaggle. Unzip dataset and write source in particular storage account container. Pre-process and build insights on Olympics events. As,Gender Level Analysis:• How has the participation of male and female athletes changed over time?• Are there sports that have seen significant increases in participation by one gender?National Level Analysis:• Which countries have historically performed well in the Olympics based on the total number of medals won?• Are there specific sports where certain countries excel?Sports Level Analysis:• Which sports have the highest and lowest participation rates?• Are there sports that have gained or lost popularity over the years?Key Features:a. API request for kaggle datab. Write data on storage account from databricksc. Olympics Insights3. Title: Stack Overflow Dataset AnalysisProject Description: Build dynamic robust data factory pipeline for stack overflow annual developer survey 2023 data, which first copy zipped folder from stack overflow official website, write it to storage account. Then, second copy activity unzip folder to extract source files. Mount the container on databricks, and pre-processing it to make data accurate and reliable. Then we build insights for better developer background understanding. As,Developers Education Analysis:• Analyzing the distribution of Developer's Education Levels on Stack Overflow?• Whats the education diversity among Developers at various career levels?"How to Learn Code?" Analysis:• What are the most preferred sources of learning coding by Developers?• What are the most preferred sources of learning by developers who are in the learning phase?• What is the distribution of preferred ways of learning code by different age groups of developers?Key Features:a. Copy zipped folder containing different format of datab. Unzip folder and write data within container on storage accountc. Developer Survey Insights4. Title: Uber Taxi Dataset AnalysisProject Description: Build a robust data factory pipeline that recursively fetch data files from NYC TLC site using forEach activity and copy it to the container in storage account. Mount the container on databricks, and pre-processing it to make data accurate and reliable. Then we build insights for better understanding. As,Taxi Demand Analysis:• What is the overall demand for Uber rides during different time-periods (days of the week, hours of the day, etc.)?• How does the passenger count vary during peak and off-peak hours?Payment Analysis:• What is the distribution of payment types (cash, credit card, etc.)?• Is there any correlation between payment types and trip distance?Key Features:a. ForEach activity in data factoryb. Dynamic filepath creationc. Uber Taxi InsightsWho Should Enroll:Aspiring Data EngineersDevelopers Interested in CloudBusiness Concerns who want to start using CloudPrerequisites: No prior experience with Azure is required, but a basic understanding of Python and PySpark will be helpful.What You'll Gain:A Solid Grasp of Azure BasicsHands-on Experience in deploying and using multiple AWS servicesLearn integration of different source for dynamic fetching of dataExposure with transformation and building insights of source data using cloudJoin us on a transformative journey into the world of Azure Projects for Data Engineering. Unlock your potential, elevate your skills, and become a driving force in the data revolution. Enroll now and take the first step toward success in data engineering with Azure.
Who this course is for:
Aspiring Data Engineers
Developers Interested in Cloud
Business Concerns who want to start using Cloud
Data Analytics Enthusiast
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