MOC 20776 kursus: Performing Big Data Engineering on Microsoft Cloud Services kursus

Kursusmål

Dette kursus i Big Data er rettet mod dig, der ønsker at lære at arbejde med Big Data i Microsoft Azure værktøjer. Du vil på kurset bl.a. lære at anvende Azure Stream Analytics, Azure Data Lake, SQL Data Warehouse og Azure Data Factory.

Efter kurset vil du være i stand til at:
  • Anvende Azure Stream Analytics
  • Anvende Azure Data Lake som data repository
  • Anvende Azure Data Lake Analytics
  • Anvende SQL Data Warehouse
  • Analysere data med SQL Data Warehouse
  • Anvende Azure Data Factory til at importere og omforme data
  • Anvende Azure Data Factory til at overføre data mellem Azure Services

Deltagere

Få det optimale ud af kurset

Dette Big Data kursus indgår som en del af vores samlede udbud af Business Intelligence kurser og forudsætter kendskab til Microsoft Azure Services og indgående kendskab til [link=/kurser/business-intelligence/datamodellering-og-design/1129/grundlæggende-business-intelligence-(bi)[/link].

Kursusmateriale

Før kurset
  • Mulighed for at tale med en instruktør, der kan hjælpe dig med at finde det helt rigtige kursus.
På kurset
    • Undervisning af Danmarks mest erfarne instruktørteam i hyggelige og fuldt opdaterede kursuslokaler i centrum af København.
    • Et kursus bestående af en vekslen mellem teori og praktiske øvelser. Vi ved, hvor vigtigt det er, at du får tid til at arbejde med opgaverne i praksis, og derfor har vi altid fokus på hands-on i undervisningen.
    • Adgang til Microsofts digitale kursusmateriale (DMOC) samt Microsoft Labs Online.*
    • Fuld forplejning, som inkluderer morgenmad, friskbrygget kaffe, te, frugt, sodavand, frokost på en italiensk restaurant på Gråbrødretorv, kage, slik, og naturligvis Wi-Fi til dine devices.
    • Et kursuscertifikat med bevis på dine nye kvalifikationer.
Efter kurset
  • Adgang til vores gratis hotline, som betyder, at du op til et år efter kurset kan ringe eller skrive til os, hvis du har spørgsmål til de emner, der er blevet gennemgået på kurset.
  • Vores unikke tilfredshedsgaranti, som er din tryghed for at få fuldt udbytte af dit kursus.

Kurset bliver afholdt på dansk, men vi benytter Microsofts digitale materiale (DMOC), som er på engelsk. På kurset bliver der stillet en Surface tablet til rådighed, som kan anvendes til læsning af materialet. Du vil efterfølgende have adgang til materialet både online og lokalt. I tilfælde af at Microsoft laver en ny version af kursusmaterialet, vil du automatisk få adgang til det. Derudover vil du have adgang til øvelser via Microsoft Online Labs i 19 dage i alt, og du kan derfor fortsætte eller starte forfra på en øvelse hjemmefra, under eller efter kurset, alt efter behov.

Kursusindhold

Module 1: Architectures for Big Data Engineering with Azure
This module describes common architectures for processing big data using Azure tools and services.

Lessons
  • Understanding Big Data
  • Architectures for Processing Big Data
  • Considerations for designing Big Data solutions
  • Design a big data architecture

After completing this module, students will be able to:
  • Explain the concept of Big Data.
  • Describe the Lambda and Kappa architectures.
  • Describe design considerations for building Big Data Solutions with Azure.
Module 2: Processing Event Streams using Azure Stream Analytics
This module describes how to use Azure Stream Analytics to design and implement stream processing over large-scale data.

Lessons
  • Introduction to Azure Stream Analytics
  • Configuring Azure Stream Analytics jobs

Lab : Processing Event Streams with Azure Stream Analytics
  • Create an Azure Stream Analytics job
  • Create another Azure Stream job
  • Add an Input
  • Edit the ASA job
  • Determine the nearest Patrol Car

After completing this module, students will be able to:
  • Describe the purpose and structure of Azure Stream Analytics.
  • Configure Azure Stream Analytics jobs for scalability, reliability and security.

Module 3: Performing custom processing in Azure Stream Analytics
This module describes how to include custom functions and incorporate machine learning activities into an Azure Stream Analytics job.

Lessons
  • Implementing Custom Functions
  • Incorporating Machine Learning into an Azure Stream Analytics Job

Lab : Performing Custom Processing with Azure Stream Analytics
  • Add logic to the analytics
  • Detect consistent anomalies
  • Determine consistencies using machine learning and ASA

After completing this module, students will be able to:
  • Describe how to create and use custom functions in Azure Stream Analytics.
  • Describe how to use Azure Machine Learning models in an Azure Stream Analytics job.

Module 4: Managing Big Data in Azure Data Lake Store
This module describes how to use Azure Data Lake Store as a large-scale repository of data files.

Lessons
  • Using Azure Data Lake Store
  • Monitoring and protecting data in Azure Data Lake Store

Lab : Managing Big Data in Azure Data Lake Store
  • Update the ASA Job
  • Upload details to ADLS

After completing this module, students will be able to:
  • Describe how to create an Azure Data Lake Store, create folders, and upload data.
  • Explain how to monitor an Azure Data Lake account, and protect the data that it contains.
Module 5: Processing Big Data using Azure Data Lake Analytics
This module describes how to use Azure Data Lake Analytics to examine and process data held in Azure Data Lake Store.

Lessons
  • Introduction to Azure Data Lake Analytics
  • Analyzing Data with U-SQL
  • Sorting, grouping, and joining data

Lab : Processing Big Data using Azure Data Lake Analytics
  • Add functionality
  • Query against Database
  • Calculate average speed

After completing this module, students will be able to:
  • Describe the purpose of Azure Data Lake Analytics, and how to create and run jobs.
  • Describe how to use USQL to process and analyse data.
  • Describe how to use windowing to sort data and perform aggregated operations, and how to join data from multiple sources.

Module 6: Implementing custom operations and monitoring performance in Azure Data Lake Analytics
This module describes how to create and deploy custom functions and operations, integrate with Python and R, and protect and optimize jobs.

Lessons
  • Incorporating custom functionality into Analytics jobs
  • Managing and Optimizing jobs

Lab : Implementing custom operations and monitoring performance in Azure Data Lake Analytics
  • Custom extractor
  • Custom processor
  • Integration with R/Python
  • Monitor and optimize a job

After completing this module, students will be able to:
  • Describe how to incorporate custom features and assemblies into USQL.
  • Describe how to implement security to protect jobs, and how to monitor and optimize jobs to ensure efficient operations.

Module 7: Implementing Azure SQL Data Warehouse
This module describes how to use Azure SQL Data Warehouse to create a repository that can support large-scale analytical processing over data at rest.

Lessons
  • Introduction to Azure SQL Data Warehouse
  • Designing tables for efficient queries
  • Importing Data into Azure SQL Data Warehouse

Lab : Implementing Azure SQL Data Warehouse
  • Create a new data warehouse
  • Design and create tables and indexes
  • Import data into the warehouse.

After completing this module, students will be able to:
  • Describe the purpose and structure of Azure SQL Data Warehouse.
  • Describe how to design table to optimize the processing performed by the data warehouse.
  • Describe tools and techniques for importing data into a warehouse at scale.

Module 8: Performing Analytics with Azure SQL Data Warehouse
This module describes how to import data in Azure SQL Data Warehouse, and how to protect this data.

Lessons
  • Querying Data in Azure SQL Data Warehouse
  • Maintaining Performance
  • Protecting Data in Azure SQL Data Warehouse

Lab : Performing Analytics with Azure SQL Data Warehouse
  • Performing queries and tuning performance
  • Integrating with Power BI and Azure Machine Learning
  • Configuring security and analysing threats

After completing this module, students will be able to:
  • Describe how to perform queries and use the data held in a data warehouse to perform analytics and generate reports.
  • Describe how to configure and monitor a data warehouse to maintain good performance.
  • Describe how to protect data and manage security in a data warehouse.

Module 9: Automating the Data Flow with Azure Data Factory
This module describes how to use Azure Data Factory to import, transform, and transfer data between repositories and services.

Lessons
  • Introduction to Azure Data Factory
  • Transferring Data
  • Transforming Data
  • Monitoring Performance and Protecting Data

Lab : Automating the Data Flow with Azure Data Factory
  • Automate the Data Flow with Azure Data Factory

After completing this module, students will be able to:
  • Describe the purpose of Azure Data Factory, and explain how it works.
  • Describe how to create Azure Data Factory pipelines that can transfer data efficiently.
  • Describe how to perform transformations using an Azure Data Factory pipeline.
  • Describe how to monitor Azure Data Factory pipelines, and how to protect the data flowing through these pipelines.

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Her bor vi

Hovedindgang
Amagertorv 21
1160 København K
Kursusindgang
Læderstræde 22-26
1201 København K
Åbningstider
Mandag: 08.00 - 16.30 (Indgang for kursister i Læderstræde åbner 8.30) 
Tirsdag: 08.30 - 16.30
Onsdag: 08.30 - 16.30
Torsdag: 08.30 - 16.30
Fredag: 08.30 - 16.30

 

Kontaktoplysninger

 
Amagertorv 21
1160 København K