MOC 20776 - Performing Big Data Engineering on Microsoft Cloud Services
This five-day Performing Big Data Engineering on Microsoft Cloud Services training class 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.
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.
Microsoft Certified Partner
Webucator is a Microsoft Certified Partner for Learning Solutions (CPLS). This class uses official Microsoft courseware and will be delivered by a Microsoft Certified Trainer (MCT).
Public Classes: Delivered live online via WebEx and guaranteed to run . Join from anywhere!
Private Classes: Delivered at your offices , or any other location of your choice.
- Learn to describe common architectures for processing big data using Azure tools and services.
- Learn to describe how to use Azure Stream Analytics to design and implement stream processing over large-scale data.
- Learn to describe how to include custom functions and incorporate machine learning activities into an Azure Stream Analytics job.
- Learn to describe how to use Azure Data Lake Store as a large-scale repository of data files.
- Learn to describe how to use Azure Data Lake Analytics to examine and process data held in Azure Data Lake Store.
- Learn to describe how to create and deploy custom functions and operations, integrate with Python and R, and protect and optimize jobs.
- Learn to describe how to use Azure SQL Data Warehouse to create a repository that can support large-scale analytical processing over data at rest.
- Learn to describe how to use Azure SQL Data Warehouse to perform analytical processing, how to maintain performance, and how to protect the data.
- Learn to describe how to use Azure Data Factory to import, transform, and transfer data between repositories and services.
- Architectures for Big Data Engineering with Azure
- Understanding Big Data
- Architectures for Processing Big Data
- Considerations for designing Big Data solutions
- Lab: Designing a Big Data Architecture
- Design a big data architecture
- Processing Event Streams using Azure Stream Analytics
- 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
- Performing custom processing in Azure Stream Analytics
- 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
- Managing Big Data in Azure Data Lake Store
- 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
- Processing Big Data using Azure Data Lake Analytics
- 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
- Implementing custom operations and monitoring performance in Azure Data Lake Analytics
- 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
- Implementing Azure SQL Data Warehouse
- 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.
- Performing Analytics with Azure SQL Data Warehouse
- 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
- Automating the Data Flow with Azure Data Factory
- 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
Each student in our Live Online and our Onsite classes receives a comprehensive set of materials, including course notes and all the class examples.
Experience in the following is required for this Microsoft Big Data class:
- 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. .