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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/data-processing-with-azure
课程评论：没有评论
课程名称：Azure数据处理 概述：此Azure培训课程旨在为学生提供处理、存储和分析数据以作出明智商业决策所需的知识。通过本课程，学生将理解大数据及其分析的重要性，从而提升数学和编程技能。学生将学习使用Python、R和Apache Spark等重要分析工具的最有效方法。 课程大纲： 1. **介绍**：本课程为学生提供处理、存储和分析数据的知识，使他们能做出明智的商业决策。学生将了解大数据的概念及其分析的重要性，并提升其数学和编程技能。 2. **第一部分 - 在Azure上利用Databricks和Data Factory进行批处理**：本模块探讨Azure Databricks如何与多种数据环境集成，并通过ETL或ELT流程拉取数据。 3. **第二部分 - 创建管道和活动**：实时处理大数据是许多企业的运营必要。Azure Stream Analytics是微软面向复杂事件处理的无服务器实时分析产品。本部分将探讨如何利用大数据获得有价值的洞察和竞争优势。 4. **第三部分 - 链接服务与数据集**：数据工厂可拥有一个或多个管道，管道是活动的逻辑分组。创建数据集之前，必须先创建链接服务，以将数据存储与数据工厂链接。 5. **第四部分 - 调度和触发器**：Azure Data Factory是一种完全托管的云数据编排服务，支持数据移动和转换。本部分探讨如何为Azure Data Factory调度触发器，以自动化管道执行。 6. **第五部分 - 选择窗口函数**：在时间流场景中，针对时间窗口内的数据进行操作是常见模式。Stream Analytics原生支持窗口函数，使开发人员能够轻松编写复杂的流处理作业。 7. **第六部分 - 配置流数据解决方案的输入和输出**：本部分教会如何使用Azure Stream Analytics分析电话数据，并过滤掉一些欺诈电话。 8. **第七部分 - Polybase中的ELT与ETL**：传统的SMP数据仓库使用ETL过程加载数据，而Azure SQL数据仓库利用大规模并行处理架构，采用ELT过程，可以利用MPP的优势，消除在加载之前转换数据所需的资源。 通过本课程，学生将掌握Azure数据处理的核心技能，从而在数据分析领域立足。
Name:Introduction
Description: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.
Name:Section 1 - Batch Processing with Databricks and Data Factory on Azure
Description: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.
Name:Section 2 - Creating Pipelines and Activities
Description: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.
Name:Section 3 - Link Services and Datasets
Description: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.
Name:Section 4 - Schedules and Triggers
Description: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.
Name:Section 5 - Selecting Windowing Functions
Description: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.
Name:Section 6 - Configuring Input and Output for Streaming Data Solutions
Description: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.
Name:Section 7 - ELT versus ETL in Polybase
Description: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.
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.
