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.
43개의 평가
제공자:
Data Processing with Azure
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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 AnalysisMicrosoft AzureBig DataData LakeData Factory
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
유동적 마감일
일정에 따라 마감일을 재설정합니다.
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 계획 - 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)
1개의 동영상
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
5개의 동영상
1.2 - ELT Processing using Azure9m
1.3 - Databricks and Azure Spark7m
1.4 Transform Data using Databricks in ADF8m
1.5 Use Case: ADF and Spark7m
3개의 읽기 자료
Azure Databricks and Apache Spark10m
Exercise 1 - Use Batch Processing with Databricks and Data Factory on Azure10m
Exercise 2 - Intro to Databricks and Data Factory Page30m
1개 연습문제
Module 1 Quiz30m
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
4개의 동영상
Processing using a Pipeline7m
Analyzing Logs for an HDInsight Cluster9m
Using Azure Blob Storage within HDInsight7m
3개의 읽기 자료
Using Azure Blob Storage with HDInsight5m
Exercise 1 - Pipeline Activities & Usage in Azure Data Factory15m
Exercise 2 - Examine Logs within the HDInsight/Blob Storage15m
1개 연습문제
Module 2 Quiz30m
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
5개의 동영상
Identifying Pipelines for a Data Factory9m
Data Stores and Azure Blob Storage5m
Linked Service and Connecting Data Factory to External Resources3m
Processing Input Blobs with Azure Data Factory5m
1개의 읽기 자료
Exercise 1 - Link Data within Datasets in Azure Storage10m
1개 연습문제
Module 3 Quiz30m
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
5개의 동영상
Creating a Trigger that Runs a Pipeline on a Schedule2m
Scheduling a Trigger in Azure Data Factory3m
Pipeline Execution and Triggers in ADF3m
Use Case: Azure Schedule, Trigger, and Events2m
1개의 읽기 자료
Pipeline Execution and Triggers in ADF5m
1개 연습문제
Module 4 Quiz30m
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
5개의 동영상
How Stream Analytics Support Native Windowing Functions 9m
Temporal Windows5m
Using Window Functions in the GROUP BY Clause2m
Aggregating Events over Multiple Windows using WindowsQ6m
1개의 읽기 자료
Understanding Stream Analytics Windowing Functions10m
1개 연습문제
Module 5 Quiz30m
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
7개의 동영상
Generate Sample Call Data and Send it to Event Hubs10m
Creating a Stream Analytics Job 7m
Configuring Job Input and Output 3m
Define a Query to Filter Fraudulent Calls 2m
Test and Start the Job4m
Visualize Results in Power BI2m
1개의 읽기 자료
Output Real-Time Stream Analytics Data to a Power BI Dashboard10m
1개 연습문제
Module 6 Quiz30m
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
4개의 동영상
How SQL Data Warehouse in Microsoft Offers ELT Solutions 3m
Loading Methods using Non-PolyBase Options 3m
Use Case: A Deeper Dive into ETL Processing 3m
1개의 읽기 자료
Video: Ingesting Data using Polybase | Azure SQL Data Warehouse10m
1개 연습문제
Module 7 Quiz30m
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