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Data Processing with Azure · LearnSpace
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courseraIT и технологии

Data Processing with Azure

Курс от LearnQuest
Средний≈ 12.7 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This Azure training course is designed to equip students with the knowledge need to process, store and analyze data for making informed business decisions. Through this Azure course, the student will understand what big data is along with the importance of big data analytics, which will improve the students mathematical and programming skills. Students will learn the most effective method of using essential analytical tools such as Python, R, and Apache Spark.

Навыки, которые вы освоите

Data ProcessingData WarehousingData PipelinesExtract, Transform, LoadReal Time DataData TransformationMicrosoft AzureDatabricksBig DataData IntegrationData StoreApache SparkSchedulingData AnalysisAnalyticsData Storage

Программа курса

8 модулей · 68 учебных материалов

01Introduction1 материалов

Course Introduction

Course IntroductionВидео
02Section 1 - Batch Processing with Databricks and Data Factory on Azure10 материалов

1.1 Introduction

1.1 Batch Processing with Databricks and Data Factory in AzureВидеоMicrosoft Video: Cloud Computing OverviewPLUGIN

1.2 ELT Processing using Azure

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LearnQuest Network

Преподаватель курса

Data Processing with Azure
В каталоге вашей программы

Инвестируйте в себя

Новые знания — в удобное для вас время.

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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 12.7 ч

8 модулей

Язык: Английский

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

Часть программы вашего университета
1.2 - ELT Processing using AzureВидео

1.3 Databricks and Azure Spark

1.3 - Databricks and Azure SparkВидеоAzure Databricks and Apache SparkЧтение

1.4 Transform Data using Databricks in Azure Data Factory

1.4 Transform Data using Databricks in ADFВидео

1.5 Use Case: Azure Data Factory and Spark

1.5 Use Case: ADF and SparkВидео

Labs & Exercises

Exercise 1 - Use Batch Processing with Databricks and Data Factory on AzureЧтениеExercise 2 - Intro to Databricks and Data Factory PageЧтение

Module Assessment

Module 1 QuizЗадание
03Section 2 - Creating Pipelines and Activities10 материалов

2.1 Introduction

Pipelines and Activities - IntroductionВидеоMicrosoft Video: Azure SQL Data WarehousePLUGIN

2.2 Processing using Pipelines and Activities with Azure Data Factory

Processing using a PipelineВидео2.2 ActivityPLUGIN

2.3 Analyzing Logs for an HDInsight Cluster

Analyzing Logs for an HDInsight ClusterВидео

2.4 Using Azure Blob Storage within HDInsight

Using Azure Blob Storage within HDInsightВидеоUsing Azure Blob Storage with HDInsightЧтение

Labs & Exercises

Exercise 1 - Pipeline Activities & Usage in Azure Data FactoryЧтениеExercise 2 - Examine Logs within the HDInsight/Blob StorageЧтение

Module Assessment

Module 2 QuizЗадание
04Section 3 - Link Services and Datasets9 материалов

3.1 Introduction

Link Services and Datasets - IntroductionВидео

3.2 Identifying pipelines for a Data Factory

Identifying Pipelines for a Data FactoryВидео3.2 ActivityPLUGIN

3.3 Data stores and Azure Blob Datasets, Containers, and Folders

Data Stores and Azure Blob StorageВидео

3.4 Linked Service and Connecting Data Factory to External Resources

Linked Service and Connecting Data Factory to External ResourcesВидео

3.5 Processing Input Blobs with Azure Data Factory

Processing Input Blobs with Azure Data FactoryВидеоVideo: Processing Input Blobs with Azure Data FactoryPLUGIN

Labs & Exercises

Exercise 1 - Link Data within Datasets in Azure StorageЧтение

Module Assessment

Module 3 QuizЗадание
05Section 4 - Schedules and Triggers9 материалов

4.1 Introduction

Schedules and Triggers - IntroductionВидеоVideo: Azure Data Factory Event TriggersPLUGIN

4.2 Creating a Trigger that Runs A Pipeline on a Schedule

Creating a Trigger that Runs a Pipeline on a ScheduleВидео

4.3 Scheduling a Trigger in Azure Data Factory

Scheduling a Trigger in Azure Data FactoryВидео

4.4 Pipeline Execution and Triggers in Azure Data Factory

Pipeline Execution and Triggers in ADFВидеоPipeline Execution and Triggers in ADFЧтение

4.5 Use Case: Azure Schedule/Trigger/Events

Use Case: Azure Schedule, Trigger, and EventsВидео

Labs & Exercises

Module 4 ExercisePLUGIN

Module Assessment

Module 4 QuizЗадание
06Section 5 - Selecting Windowing Functions10 материалов

5.1 Introduction

Selecting Windowing Functions - IntroductionВидео

5.2 How Stream Analytics Support Native Windowing Functions to Enable Developers to Author Complex Stream Processing Jobs

How Stream Analytics Support Native Windowing Functions ВидеоVideo: Window Functions for AnalysisPLUGIN

5.3 Four Kinds of Temporal Windows

Temporal WindowsВидеоUnderstanding Stream Analytics Windowing FunctionsЧтение

5.4 Using Window Functions in the GROUP BY Clause of the Query Syntax in Your Stream Analytics Job

Using Window Functions in the GROUP BY ClauseВидео

5.5 Aggregating Events over Multiple Windows using WindowsQ

Aggregating Events over Multiple Windows using WindowsQВидео5.5 ActivityPLUGIN

Labs & Exercises

Module 5 ExercisePLUGIN

Module Assessment

Module 5 QuizЗадание
07Section 6 - Configuring Input and Output for Streaming Data Solutions12 материалов

6.1 Introduction

How Stream Analytics Relate to Data SolutionsВидео

6.2 Generating Sample Phone Call Data and Sending it to Azure Event Hubs

Generate Sample Call Data and Send it to Event HubsВидео

6.3 Creating a Stream Analytics Job

Creating a Stream Analytics Job ВидеоVideo: Create a Stream Analytics JobPLUGIN6.3 ActivityPLUGIN

6.4 Configuring Job Input and Output

Configuring Job Input and Output Видео

6.5 Defining a Query to Filter Fraudulent Calls

Define a Query to Filter Fraudulent Calls Видео

6.6 Testing and Starting the Job

Test and Start the JobВидео

6.7 Visualizing Results in Power BI

Visualize Results in Power BIВидеоOutput Real-Time Stream Analytics Data to a Power BI DashboardЧтение6.7 ActivityPLUGIN

Module Assessment

Module 6 QuizЗадание
08Section 7 - ELT versus ETL in Polybase7 материалов

7.1 Introduction

ELT vs ETL in PolyBase - Introduction Видео

7.2 How SQL Data Warehouse in Microsoft Offers Extract Load Transform Solutions

How SQL Data Warehouse in Microsoft Offers ELT Solutions ВидеоVideo: Ingesting Data using Polybase | Azure SQL Data WarehouseЧтение

7.3 SQL Data Warehouse Loading Methods using non-Polybase Options

Loading Methods using Non-PolyBase Options Видео

7.4 Use Case: A Deeper Dive into ETL Processing using Polybase and ELT Solutions using Microsoft Datawarehouse ELT Approach

Use Case: A Deeper Dive into ETL Processing Видео

Labs & Exercises

Module 7 ExercisePLUGIN

Module Assessment

Module 7 QuizЗадание