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.
제공자:
Data Processing with Azure
LearnQuest이 강좌에 대하여
최근 조회 2,175회
유연한 마감일
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지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
중급 단계
완료하는 데 약 13시간 필요
영어
자막: 프랑스어, 포르투갈어 (유럽), 러시아어, 영어, 스페인어
배울 내용
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
귀하가 습득할 기술
- Data Analysis
- Microsoft Azure
- Big Data
- Data Lake
- Data Factory
유연한 마감일
일정에 따라 마감일을 재설정합니다.
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
중급 단계
완료하는 데 약 13시간 필요
영어
자막: 프랑스어, 포르투갈어 (유럽), 러시아어, 영어, 스페인어
제공자:
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.
강의 계획표 - 이 강좌에서 배울 내용
완료하는 데 7분 필요
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.
완료하는 데 7분 필요
1개 동영상 (총 7분)
완료하는 데 2시간 필요
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.
완료하는 데 2시간 필요
5개 동영상 (총 40분), 3 개의 읽기 자료, 1 개의 테스트
완료하는 데 2시간 필요
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.
완료하는 데 2시간 필요
4개 동영상 (총 46분), 3 개의 읽기 자료, 1 개의 테스트
완료하는 데 2시간 필요
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.
완료하는 데 2시간 필요
5개 동영상 (총 38분), 1 개의 읽기 자료, 1 개의 테스트
완료하는 데 1시간 필요
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.
완료하는 데 1시간 필요
5개 동영상 (총 17분), 1 개의 읽기 자료, 1 개의 테스트
완료하는 데 2시간 필요
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.
완료하는 데 2시간 필요
5개 동영상 (총 30분), 1 개의 읽기 자료, 1 개의 테스트
완료하는 데 2시간 필요
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.
완료하는 데 2시간 필요
7개 동영상 (총 33분), 1 개의 읽기 자료, 1 개의 테스트
완료하는 데 1시간 필요
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.
완료하는 데 1시간 필요
4개 동영상 (총 21분), 1 개의 읽기 자료, 1 개의 테스트
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