Seminar Details

Performing Big Data Engineering on Microsoft Cloud Services (MOC20776)

ETC
MOC20776
Hersteller-ID: 20776
Prüfungs ID: 70-776
Dauer: 5 Tage
Preis: € 2.465.- exkl.MwST
Wissensgarantie: 12 Monate

Info: Dieses Seminar behandelt prüfungsrelevante Themen zum Microsoft Examen 70-776 Perform Big Data Engineering on Microsoft Cloud Services. 

Mögliche Zertifizierung: MCSA: Data Engineering with Azure
Dieses Seminar wird ab 06/2019 von Microsoft aus dem Programm genommen. Als Ersatz können wir Ihnen folgende Seminare nennen:
DP-200T01 Implementing an Azure Data Solution
DP-201T01 Designing an Azure Data Solution

This five-day instructor-led course describes how to process Big Data using Azure tools and services including Azure Stream Analytics, Azure Data Lake, Azure SQL Data Warehouse and Azure Data Factory. The course also explains how to include custom functions, and integrate Python and R.


After completing this course, students will be able to:
  • Describe common architectures for processing big data using Azure tools and services.
  • Describe how to use Azure Stream Analytics to design and implement stream processing over large-scale data.
  • Describe how to include custom functions and incorporate machine learning activities into an Azure Stream Analytics job.
  • Describe how to use Azure Data Lake Store as a large-scale repository of data files.
  • Describe how to use Azure Data Lake Analytics to examine and process data held in Azure Data Lake Store.
  • Describe how to create and deploy custom functions and operations, integrate with Python and R, and protect and optimize jobs.
  • Describe how to use Azure SQL Data Warehouse to create a repository that can support large-scale analytical processing over data at rest.
  • Describe how to use Azure SQL Data Warehouse to perform analytical processing, how to maintain performance, and how to protect the data.
  • Describe how to use Azure Data Factory to import, transform, and transfer data between repositories and services.

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Zielgruppe

The primary audience for this course is data engineers (IT professionals, developers, and information workers) who plan to implement big data engineering workflows on Azure.

Vorkenntnisse

In addition to their professional experience, students who attend this training should already have the following technical knowledge:
  • A good understanding of Azure data services.
  • A basic knowledge of the Microsoft Windows operating system and its core functionality.
  • A good knowledge of relational databases.
  • Schwerpunkte

    1. Module 1: Architectures for Big Data Engineering with Azure
      1. Understanding Big Data
      2. Architectures for Processing Big Data
      3. Considerations for designing Big Data solutions
    2. Lab: Designing a Big Data Architecture
      1. Design a big data architecture
    3. Module 2: Processing Event Streams using Azure Stream Analytics
      1. Introduction to Azure Stream Analytics
      2. Configuring Azure Stream Analytics jobs
    4. Lab: Processing Event Streams with Azure Stream Analytics
      1. Create an Azure Stream Analytics job
      2. Create another Azure Stream job
      3. Add an Input
      4. Edit the ASA job
      5. Determine the nearest Patrol Car
    5. Module 3: Performing custom processing in Azure Stream Analytics
      1. Implementing Custom Functions
      2. Incorporating Machine Learning into an Azure Stream Analytics Job
    6. Lab: Performing Custom Processing with Azure Stream Analytics
      1. Add logic to the analytics
      2. Detect consistent anomalies
      3. Determine consistencies using machine learning and ASA
    7. Module 4: Managing Big Data in Azure Data Lake Store
      1. Using Azure Data Lake Store
      2. Monitoring and protecting data in Azure Data Lake Store
    8. Lab: Managing Big Data in Azure Data Lake Store
      1. Update the ASA Job
      2. Upload details to ADLS
    9. Module 5: Processing Big Data using Azure Data Lake Analytics
      1. Introduction to Azure Data Lake Analytics
      2. Analyzing Data with U-SQL
      3. Sorting, grouping, and joining data
    10. Lab: Processing Big Data using Azure Data Lake Analytics
      1. Add functionality
      2. Query against Database
      3. Calculate average speed
    11. Module 6: Implementing custom operations and monitoring performance in Azure Data Lake Analytics
      1. Incorporating custom functionality into Analytics jobs
      2. Managing and Optimizing jobs
    12. Lab: Implementing custom operations and monitoring performance in Azure Data Lake Analytics
      1. Custom extractor
      2. Custom processor
      3. Integration with R/Python
      4. Monitor and optimize a job
    13. Module 7: Implementing Azure SQL Data Warehouse
      1. Introduction to Azure SQL Data Warehouse
      2. Designing tables for efficient queries
      3. Importing Data into Azure SQL Data Warehouse
    14. Lab: Implementing Azure SQL Data Warehouse
      1. Create a new data warehouse
      2. Design and create tables and indexes
      3. Import data into the warehouse.
    15. Module 8: Performing Analytics with Azure SQL Data Warehouse
      1. Querying Data in Azure SQL Data Warehouse
      2. Maintaining Performance
      3. Protecting Data in Azure SQL Data Warehouse
    16. Lab: Performing Analytics with Azure SQL Data Warehouse
      1. Performing queries and tuning performance
      2. Integrating with Power BI and Azure Machine Learning
      3. Configuring security and analysing threats
    17. Module 9: Automating the Data Flow with Azure Data Factory
      1. Introduction to Azure Data Factory
      2. Transferring Data
      3. Transforming Data
      4. Monitoring Performance and Protecting Data
    18. Lab: Automating the Data Flow with Azure Data Factory
      1. Automate the Data Flow with Azure Data Factory
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