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Transformation Engineering & Reusable Components · LearnSpace
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Transformation Engineering & Reusable Components

Курс от Coursera
Уровень не указан≈ 23.7 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Learn how to design, transform, and operationalize high-quality data pipelines through this comprehensive course in the Data Engineering Skill Path. You will develop essential skills in cleansing and standardizing raw datasets, applying complex business-rule transformations, constructing multi-step pipelines, and enriching data through joins with master sources. Through hands-on experience with Python, SQL, Airflow, Spark, and generative AI tools, you will learn to build scalable, reusable transformation components that support both operational and analytical workloads. This course integrates perspectives from Microsoft, IBM, and Meta, offering a multi-tool, real-world view of how transformation logic is designed, automated, and reused across modern data ecosystems. You will progress from foundational data manipulation in Python to advanced SQL techniques, stored procedures, parameterized pipeline components, Spark-based transformation pipelines, and AI-assisted enrichment workflows. Ideal for aspiring data engineers and professionals looking to deepen their transformation engineering capabilities, this course prepares you to design robust, repeatable, and efficient transformation systems that ensure consistent, high-quality data across the enterprise.

Навыки, которые вы освоите

Data PipelinesStored ProcedureData ManipulationData CleansingBusiness LogicApache SparkApache AirflowData PreprocessingSQLData EthicsGenerative AIData TransformationExtract, Transform, LoadPandas (Python Package)Data Integration

Программа курса

9 модулей · 123 учебных материалов

01Start Here: Get Oriented and Check Your Skills2 материалов
Start Here: How This Skill-Based Course WorksЧтениеSkill Diagnostic: Find Your Recommended Starting PointЗадание
02Data processing and manipulation22 материалов

pandas: Your data manipulation powerhouse

Manipulating data with pandasВидео

Учитесь у экспертов

Professionals from the Industry

Преподаватель курса

Transformation Engineering & Reusable Components
В каталоге вашей программы

Инвестируйте в себя

Новые знания — в удобное для вас время.

Начать на Coursera

Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 23.7 ч

9 модулей

Язык: Английский

Субтитры: Арабский, Французский, Узбекский, Украинский, Китайский (Китай), Греческий, Итальянский, Бразильский португальский, Вьетнамский, Нидерландский, Корейский, Немецкий, Пушту, Русский, Тайский, Индонезийский, Шведский, Турецкий, Азербайджанский, Испанский, Дари, Хинди, Японский, Казахский, Венгерский, Польский

Часть программы вашего университета
pandas Dataframes: The basicsВидео
pandas indexing explainedЧтение
Demo: Loading and inspecting datasets in pandasВидео
Exploring data transformationsВидео
pandas cheat sheetЧтение
Demo: Transforming data with pandasВидео
Activity: Loading and inspecting datasets in pandasЗадание
pandas: Your data manipulation powerhouseЗадание

The hero of data analysis: Data cleaning

Exploratory data analysis (EDA)ВидеоThe importance of data cleaningВидеоEssential tactics for data manipulationЧтениеIdentifying and handling missing dataВидеоHandling duplicate valuesВидеоCommon causes of missing dataЧтениеDetecting and removing outliersВидеоThe hero of data analysis: Data cleaningЗадание

Using pandas for cleaning and exploration

Data types in Python: Choosing the right fitВидеоpandas for essential analysis tasksЧтениеDemo: pandas for exploration and cleaningВидеоTaming messy data with pandasВидеоUsing pandas for cleaning and explorationЗадание
03Data Processing Techniques9 материалов

ETL and ELT Processes

ETL FundamentalsВидеоELT BasicsВидеоComparing ETL and ELTВидеоData Extraction TechniquesВидеоIntroduction to Data Transformation TechniquesВидеоData Loading TechniquesВидеоInteractivity: Tell the Difference between ETL and ELTPLUGIN

Summary and Practice Quiz

Summary & HighlightsЧтениеETL and ELT ProcessesЗадание
04Intermediate SQL19 материалов

Refining your Results

Using String Patterns and RangesВидеоSorting Result SetsВидеоGrouping Result SetsВидеоHands-on Lab: String Patterns, Sorting and GroupingВнешний инструмент[Optional] Hands-on Labs Using IBM Db2ЧтениеSummary: Refining Your ResultsЧтениеPractice Quiz: Refining Your ResultsЗаданиеSQL Cheat Sheet: Intermediate - LIKE, ORDER BY, GROUP BYPLUGIN

Functions, Multiple Tables, and Sub-queries

Built-in Database FunctionsВидеоDate and Time Built-in FunctionsВидеоHands-on Lab: Built-in functionsВнешний инструментSub-Queries and Nested SelectsВидеоHands-on Lab: Sub-queries and Nested SelectsВнешний инструментWorking with Multiple TablesВидео
05Database queries procedures and prepared statements18 материалов

Adding sales reports

Creating report queries for Little Lemon sales dataВидеоRecap: MySQL virtual tables and JOINSЧтениеExercise: Create a virtual table to summarize dataЧтениеReadiness Check: Did you create the virtual table with the correct JOINS?ЗаданиеRecap: MySQL query optimization with stored procedures and Prepared StatementsЧтениеExercise: Create optimized queries to manage and analyze dataЧтениеReadiness Check: Have you created the optimized queries and committed your progress to Git?ЗаданиеKnowledge checkЗаданиеAdditional resourcesЧтение

Table booking system

Develop a table booking systemВидеоRecap: SQL queries and transactionsЧтениеExercise: Create SQL queries to check available bookings based on user inputЧтениеReadiness Check: Testing the available bookings queriesЗаданиеExercise: Create SQL queries to add and update bookingsЧтениеReadiness Check: Did you commit your progress to Git?Задание
06Building Data Pipelines using Airflow12 материалов

Using Apache Airflow to build Data Pipelines

Apache Airflow OverviewВидеоAdvantages of Representing Data Pipelines as DAGs in Apache AirflowВидеоApache Airflow UIВидеоReading: DAG Structure and OperatorsPLUGINHands-on Lab: Getting Started with Apache AirflowВнешний инструментBuild a DAG Using AirflowВидеоHands-on Lab: Create a DAG for Apache Airflow with PythonOperatorВнешний инструментHands-on Lab: Create a DAG for Apache Airflow with BashOperatorВнешний инструментAirflow Logging and MonitoringВидеоHands-on Lab: Monitoring a DAGВнешний инструментSummary & HighlightsЧтениеPractice Quiz: Building Data Pipelines using AirflowЗадание
07Data Engineering for Machine Learning using Apache Spark17 материалов

Data Engineering for Machine Learning using Apache Spark

Spark SQLВидеоHands-on Lab: Analyze a dataset using SparkSQLВнешний инструментETL WorkloadsВидеоHands-on Lab: ETL using SparkВнешний инструментSpark Structured StreamingВидеоHands-on Lab: Leveraging Apache Spark for Smart Building HVAC MonitoringВнешний инструментFeature Extraction and TransformationВидеоHands-on Lab: Feature Extraction and Transformation LabВнешний инструментReading: Data Engineering vs Machine Learning PipelinesPLUGINMachine Learning Pipelines using SparkВидеоHands-on Lab: PipeLine creation using SparkMLВнешний инструментModel PersistenceВидеоReading: Real-Time Use Case of Model PersistencePLUGINHands-on Lab: Model PersistenceВнешний инструментPractice Quiz: Data Engineering for Machine Learning using Apache SparkЗаданиеGlossaryЧтениеSummary and HighlightsЧтение
08Use of Generative AI for Data Engineering22 материалов

Generative AI for ETL and Data Repositories

Demo: Generative AI for Data Pipelines and ETL WorkflowsВидеоHands-on Lab: Generative AI for Data Pipelines and ETL Workflows Внешний инструментDemo: Generative AI for Data Repository Maintenance and AdministrationВидеоHands-on Lab: Generative AI for Data Repository Maintenance and Administration Внешний инструментDemo: Generative AI for Querying DatabasesВидеоHands-on Lab: Generative AI for Querying DatabasesВнешний инструментDemo: Generative AI for Data Mining and AnalysisВидеоHands-on Lab: Generative AI for Data Analysis and Mining Внешний инструментGenerative AI for Data LakehouseВидеоExpert's Viewpoint: AI's Role in Shaping Data RepositoriesВидеоSuccessful Implementations of Generative AI for ETL and Data Repositories  ВидеоHands-on Lab: Testing EnvironmentВнешний инструментPractice Quiz: Generative AI for ETL and Data Repositories Задание

Generative AI Considerations for Data Professionals

Considerations While Using Generative AI in IndustriesВидеоChallenges While Using Generative AIВидеоReading: Responsible Generative AI for Data ProfessionalsPLUGINCase Study: Considerations While Using Generative AI in HealthcarePLUGINHands-on Lab: Considerations for Data Professionals using Gen AIВнешний инструментPractice Quiz: Generative AI Considerations for Data ProfessionalsЗадание

Summary

Summary: Use of Generative AI for Data EngineeringЧтениеReading: Cheat Sheet: Use of Generative AI for Data EngineeringPLUGIN
09Assessment2 материалов

Lesson

Learner Expectations for Skill AssessmentЧтениеSkill AssessmentЗадание
Hands-on Lab: Working with Multiple TablesВнешний инструмент
[Optional] Hands-on Labs Using IBM Db2Чтение
Summary: Functions, Multiple Tables, and Sub-queriesЧтение
Practice Quiz: Functions, Multiple Tables, and Sub-queriesЗадание
SQL Cheat Sheet: FUNCTIONS and Implicit JOINPLUGIN
Knowledge checkЗадание
SummaryВидео
Additional resourcesЧтение
Data Engineering |What are the key risks and challenges of using Generative AI for data professionals?Видео