MOC On-Demand: 20776-Performing Big Data Engineering on Microsoft Cloud Services - On-Demand Course

Section 1: Architectures for Big Data Engineering with Azure
This section discusses common architectures for processing big data using Azure tools and services.

Topics :
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

Section 2: Processing Event Streams using Azure Stream Analytics
This section discusses how to use Azure Stream Analytics to design and implement stream processing over large-scale data.

Topics :
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

Section 3: Performing custom processing in Azure Stream Analytics
This section discusses how to include custom functions and incorporate machine learning activities into an Azure Stream Analytics job.

Topics :
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

Section 4: Managing Big Data in Azure Data Lake Store
This section discusses how to use Azure Data Lake Store as a large-scale repository of data files.

Topics :
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

Section 5: Processing Big Data using Azure Data Lake Analytics
This section discusses how to use Azure Data Lake Analytics to examine and process data held in Azure Data Lake Store.

Topics :
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

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

Topics :
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

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

Topics :
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.

Section 8: Performing Analytics with Azure SQL Data Warehouse
This section discusses how to import data in Azure SQL Data Warehouse, and how to protect this data.

Topics :
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

Section 9: Automating the Data Flow with Azure Data Factory
This section discusses how to use Azure Data Factory to import, transform, and transfer data between repositories and services.

Topics :
Introduction to Azure Data Factory
Transferring Data
Transforming Data
Monitoring Performance and Protecting Data

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