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Data Engineering: Pipelines, ETL, Hadoop · LearnSpace
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Data Engineering: Pipelines, ETL, Hadoop

Курс от Coursera
Средний≈ 4.2 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course provides a comprehensive guide to mastering data engineering, where you'll learn to build robust data pipelines, delve into ETL (Extract, Transform, Load) processes, and handle large datasets using Hadoop. You will gain expertise in extracting data from various sources, transforming it into a usable format, and loading it into data warehouses or big data platforms. With hands-on experience in Hadoop, the industry-standard framework for handling massive datasets, you’ll learn to manage and process massive datasets efficiently. Whether you're a beginner or an experienced professional, this course equips you with the skills to design, implement, and manage data pipelines, making you a valuable asset in any data-focused organization. This course is ideal for aspiring data engineers, software developers interested in data processing, and IT professionals looking to expand their expertise into data engineering. It is also suitable for business analysts and other professionals who seek a foundational understanding of data handling technologies to improve decision-making capabilities and enhance their roles in data-driven environments. Whether you are just starting your journey in data engineering or looking to strengthen your existing skills, this course will provide the knowledge and tools you need to succeed. To get the most out of this course, you should have a basic understanding of programming concepts and some familiarity with database systems. A foundational knowledge of Python programming and SQL will be helpful, as will an understanding of relational database systems. No prior experience with Hadoop is required, but a keen interest in big data and data analytics will greatly enhance your learning experience. By the end of this course, you will be able to analyze the architecture and components of data pipelines and understand their impact on data flow and processing efficiency. You will learn how to implement robust ETL processes that are scalable and maintainable, and you will be equipped to handle big data challenges using Hadoop’s ecosystem tools, such as HDFS, MapReduce, Hive, Pig, and Spark. This course will prepare you to design, implement, and manage data solutions that can drive meaningful insights and support strategic decision-making in any organization.

Навыки, которые вы освоите

Data PipelinesApache HadoopExtract, Transform, LoadApache HiveScalabilityData AnalysisData TransformationBig DataData ProcessingData ManagementDataflowData StrategyData ArchitectureData WarehousingData IntegrationApache SparkData-Driven Decision-Making

Программа курса

1 модулей · 24 учебных материалов

01Data Engineering: Pipelines, ETL, Hadoop24 материалов

Lesson 1: Introduction to Data Engineering and Data Pipelines

Welcome to the Course: Course OverviewЧтениеIntroduction and WelcomeВидеоExplaining The Role of Data EngineeringВидеоAnalyzing Data PipelinesВидео

Учитесь у экспертов

Soheil Haddadi

Data Scientist | AI & ML Research Scientist | Interested in LLMs and GenAI

Starweaver

Global Leaders in Professional & Technology Education

Data Engineering: Pipelines, ETL, Hadoop
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Обучение на Coursera

≈ 4.2 ч

1 модулей

Язык: Английский

Субтитры: Арабский, Французский, Узбекский, Итальянский, Бразильский португальский, Корейский, Немецкий, Испанский, Японский, Казахский, Венгерский

Часть программы вашего университета
Identifying Tools and Technologies for Data PipelinesВидео
Empowering Organizations: The Crucial Role of Data Engineers in Data Management and AnalysisЧтение
From Data Engineering Theory to Real-World PracticeDIALOGUE
Data Engineering and Pipeline FundamentalsЗадание

Lesson 2: ETL Processes and Big Data Basics

Examining the ETL processesВидеоAnalysing Big Data Challenges and SolutionsВидеоDecoding Hadoop EcosystemВидеоApplying Hadoop for Processing Data Processing Data with HadoopВидеоFuture Shifts in Hadoop - Real-World ApplicationDIALOGUEETL and Data Warehousing BasicsЧтениеETL Implementation and Big Data FundamentalsЗадание

Lesson 3: Implementing a Data Solution

Designing a Data Solution ProjectВидеоExecuting ETL ProcessesВидеоAnalyzing Data InsightsВидеоCongratulations and Continuous Learning JourneyВидеоBalancing Ethics in Data Engineering: Privacy, Security, FairnessОбсуждениеBalancing Ethics in Data EngineeringDIALOGUEStreamlining Business Solutions: The Role of Data Analysis and Visual ToolsЧтениеAdvanced Hadoop Implementation and Ethical Data EngineeringЗаданиеData Engineering: Pipelines, ETL, HadoopЗадание