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.
提供方
Data Processing with Azure
LearnQuest课程信息
2,119 次近期查看
可灵活调整截止日期
根据您的日程表重置截止日期。
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。
中级
完成时间大约为13 小时
英语（English）
字幕：法语（French）, （欧洲人讲的）葡萄牙语, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
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 Analysis
- Microsoft Azure
- Big Data
- Data Lake
- Data Factory
可灵活调整截止日期
根据您的日程表重置截止日期。
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。
中级
完成时间大约为13 小时
英语（English）
字幕：法语（French）, （欧洲人讲的）葡萄牙语, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
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.
授课大纲 - 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）
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
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
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
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
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
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
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
常见问题
我什么时候能够访问课程视频和作业？
我购买证书后会得到什么？
有助学金吗？
还有其他问题吗？请访问 学生帮助中心。
