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所在平台: Udemy |
课程主页: https://www.udemy.com/course/azure-data-engineering-mastery-real-world-projects/
课程评论：没有评论
课程名称：Azure数据工程精通：真实世界项目 课程概述： 欢迎来到Azure数据工程项目课程，这是您掌握Microsoft Azure数据工程的门户！在当今数据驱动的世界中，组织迫切需要能够利用数据推动洞察和创新的专业人才。Azure提供了一系列针对数据工程任务的强大工具，从数据摄取到分析。本课程将为您提供在这一动态领域中脱颖而出的技能。 课程动机： 数据工程在将数据转化为可操作的业务洞察中起着关键作用。随着对精通Azure的数据工程师的需求上升，掌握Azure数据工程项目将为您打开激动人心的职业机会。通过实际项目和练习，您将发展解决现实世界挑战的专业知识，推动影响深远的结果。 选择本课程的理由： 本课程因其动手实践的方法和真实项目而脱颖而出。您将使用Azure服务（如Data Factory和Databricks）探索应用分析、交通洞察等多个领域。您将获得实践技能，建立引人注目的作品集，以展示您对潜在雇主的专业知识。无论您是开启新职业生涯还是提升技能，我们灵活的学习环境都能满足您的目标与期望。 课程亮点： - 涵盖应用分析、体育数据、开发者调查和交通洞察的动手项目。 - 深入探索Azure服务，包括Data Factory、Databricks和Azure存储。 - 资深数据工程专业人士的见解。 - 灵活的自主学习，提供互动作业。 - 与同行和行业专家合作和交流的机会。 项目示例： 1. **Play Store数据集分析** - 集成Azure服务，使用关键保管库提供加密安全层，构建动态获取GitHub上压缩tar.gzip文件的复制活动管道，并将其上传至存储账户。再通过Databricks进行预处理，生成应用安装、评分和价格等方面的洞察。 2. **奥运会数据集分析** - 通过API请求从Kaggle获取数据集，对奥运会事件进行分析，深入探讨性别参与、国家表现等多个方面。 3. **Stack Overflow数据集分析** - 构建动态数据工厂管道以处理Stack Overflow年度开发者调查数据，深入分析开发者的教育水平及其在学习编码过程中的偏好。 4. **Uber出租车数据集分析** - 建立强大的数据工厂管道，从纽约TLC网站递归获取数据，分析整体需求、支付方式分布及其与行程距离的相关性。 适合人群： - 有志向的数据工程师 - 对云计算感兴趣的开发者 - 希望开始使用云的商业组织 先决条件： 不需要Azure方面的经验，但对Python和PySpark有基础了解将会有所帮助。 您将收获： - 扎实的Azure基础知识 - 实际操作多个AWS服务的经验 - 学习如何动态获取数据源的集成 - 通过云进行数据转化和洞察构建的曝光 加入我们，共同踏上Azure数据工程项目的变革之旅。释放您的潜力，提升技能，成为数据革命的推动力。立即注册，迈出在数据工程领域成功的第一步！
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.
