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.
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Data Processing with Azure
LearnQuest课程信息
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可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
可灵活调整截止日期
根据您的日程表重置截止日期。
中级
完成时间大约为13 小时
英语（English）
字幕：英语（English）
您将学到的内容有
Configure batch processing with Databricks and Data Factory on Azure
Use ETL and ELT to load and transform data
Create linked services and identify pipelines for data stored within Data Factory
Explain Data Virtualization in PolyBase
您将获得的技能
Data AnalysisMicrosoft AzureBig DataData LakeData Factory
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
可灵活调整截止日期
根据您的日程表重置截止日期。
中级
完成时间大约为13 小时
英语（English）
字幕：英语（English）
提供方
LearnQuest
LearnQuest is the preferred training partner to the world’s leading companies, organizations, and government agencies. Our team boasts 20+ years of experience designing, developing and delivering a full suite industry-leading technology education classes and training solutions across the globe. Our trainers, equipped with expert industry experience and an unparalleled commitment to quality, facilitate classes that are offered in various delivery formats so our clients can obtain the training they need when and where they need it.
教学大纲 - 您将从这门课程中学到什么
完成时间为 7 分钟
Introduction
This Azure training course is designed to equip the 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 R, and Apache Spark.
完成时间为 7 分钟
1 个视频 （总计 7 分钟）
1 个视频
完成时间为 2 小时
Section 1 - Batch Processing with Databricks and Data Factory on Azure
One of the primary benefits of Azure Databricks is its ability to integrate with many other data environments to pull data through an ETL or ELT process. In module course, we examine each of the E, L, and T to learn how Azure Databricks can help ease us into a cloud solution.
完成时间为 2 小时
5 个视频 （总计 40 分钟）, 3 个阅读材料, 1 个测验
5 个视频
1.2 - ELT Processing using Azure9分钟
1.3 - Databricks and Azure Spark7分钟
1.4 Transform Data using Databricks in ADF8分钟
1.5 Use Case: ADF and Spark7分钟
3 个阅读材料
Azure Databricks and Apache Spark10分钟
Exercise 1 - Use Batch Processing with Databricks and Data Factory on Azure10分钟
Exercise 2 - Intro to Databricks and Data Factory Page30分钟
1 个练习
Module 1 Quiz30分钟
完成时间为 2 小时
Section 2 - Creating Pipelines and Activities
Processing big data in real-time is now an operational necessity for many businesses. Azure Stream Analytics is Microsoft’s serverless real-time analytics offering for complex event processing. In this section we examine how customers unlock valuable insights and gain competitive advantage by harnessing the power of big data.
完成时间为 2 小时
4 个视频 （总计 46 分钟）, 3 个阅读材料, 1 个测验
4 个视频
Processing using a Pipeline7分钟
Analyzing Logs for an HDInsight Cluster9分钟
Using Azure Blob Storage within HDInsight7分钟
3 个阅读材料
Using Azure Blob Storage with HDInsight5分钟
Exercise 1 - Pipeline Activities & Usage in Azure Data Factory15分钟
Exercise 2 - Examine Logs within the HDInsight/Blob Storage15分钟
1 个练习
Module 2 Quiz30分钟
完成时间为 2 小时
Section 3 - Link Services and Datasets
A data factory can have one or more pipelines. A pipeline is a logical grouping of activities that together perform a task. The activities in a pipeline define actions to perform on your data. Before you create a dataset, you must create a linked service to link your data store to the data factory. This section deals with linked services and data sets within Azure Blob Storage.
完成时间为 2 小时
5 个视频 （总计 38 分钟）, 1 个阅读材料, 1 个测验
5 个视频
Identifying Pipelines for a Data Factory9分钟
Data Stores and Azure Blob Storage5分钟
Linked Service and Connecting Data Factory to External Resources3分钟
Processing Input Blobs with Azure Data Factory5分钟
1 个阅读材料
Exercise 1 - Link Data within Datasets in Azure Storage10分钟
1 个练习
Module 3 Quiz30分钟
完成时间为 1 小时
Section 4 - Schedules and Triggers
Azure Data Factory is a fully managed, cloud-based data orchestration service that enables data movement and transformation. In this section, we explore scheduling triggers for Azure Data Factory to automate your pipeline execution.
完成时间为 1 小时
5 个视频 （总计 17 分钟）, 1 个阅读材料, 1 个测验
5 个视频
Creating a Trigger that Runs a Pipeline on a Schedule2分钟
Scheduling a Trigger in Azure Data Factory3分钟
Pipeline Execution and Triggers in ADF3分钟
Use Case: Azure Schedule, Trigger, and Events2分钟
1 个阅读材料
Pipeline Execution and Triggers in ADF5分钟
1 个练习
Module 4 Quiz30分钟
完成时间为 2 小时
Section 5 - Selecting Windowing Functions
In time-streaming scenarios, performing operations on the data contained in temporal windows is a common pattern. Stream Analytics has native support for windowing functions, enabling developers to author complex stream processing jobs with minimal effort. In this section, we study windowing functions related to in-stream analytics.
完成时间为 2 小时
5 个视频 （总计 30 分钟）, 1 个阅读材料, 1 个测验
5 个视频
How Stream Analytics Support Native Windowing Functions 9分钟
Temporal Windows5分钟
Using Window Functions in the GROUP BY Clause2分钟
Aggregating Events over Multiple Windows using WindowsQ6分钟
1 个阅读材料
Understanding Stream Analytics Windowing Functions10分钟
1 个练习
Module 5 Quiz30分钟
完成时间为 2 小时
Section 6 - Configuring Input and Output for Streaming Data Solutions
This section teaches how to analyze phone call data using Azure Stream Analytics. The phone call data, generated by a client application, contains some fraudulent calls, which will be filtered by the Stream Analytics job.
完成时间为 2 小时
7 个视频 （总计 33 分钟）, 1 个阅读材料, 1 个测验
7 个视频
Generate Sample Call Data and Send it to Event Hubs10分钟
Creating a Stream Analytics Job 7分钟
Configuring Job Input and Output 3分钟
Define a Query to Filter Fraudulent Calls 2分钟
Test and Start the Job4分钟
Visualize Results in Power BI2分钟
1 个阅读材料
Output Real-Time Stream Analytics Data to a Power BI Dashboard10分钟
1 个练习
Module 6 Quiz30分钟
完成时间为 1 小时
Section 7 - ELT versus ETL in Polybase
Traditional SMP data warehouses use an Extract, Transform and Load (ETL) process for loading data. Azure SQL Data Warehouse is a massively parallel processing (MPP) architecture that takes advantage of the scalability and flexibility of compute and storage resources. Utilizing an Extract, Load, and Transform (ELT) process can take advantage of MPP and eliminate resources needed to transform the data prior to loading.
完成时间为 1 小时
4 个视频 （总计 21 分钟）, 1 个阅读材料, 1 个测验
4 个视频
How SQL Data Warehouse in Microsoft Offers ELT Solutions 3分钟
Loading Methods using Non-PolyBase Options 3分钟
Use Case: A Deeper Dive into ETL Processing 3分钟
1 个阅读材料
Video: Ingesting Data using Polybase | Azure SQL Data Warehouse10分钟
1 个练习
Module 7 Quiz30分钟
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