Enterprise Database Systems
Designing and Implementing Big Data Analytics
Automation and Machine Learning
Designing Batch Processing and Data Security
Designing the Lambda Architecture and Real-time Processing
Ingesting Data and Computing for Batch Processing
Ingesting Data and Computing for Real-time Processing
Managing Activities and Data for Azure Big Data Analytics
Provisioning an Azure Data Factory

Automation and Machine Learning

Course Number:
df_dibd_a07_it_enus
Lesson Objectives

Automation and Machine Learning

  • start the course
  • create a Data Factory with Azure PowerShell
  • create a linked service for an Azure storage account
  • create a linked service for an Azure SQL database
  • create an input dataset using Azure PowerShell
  • create an output dataset using Azure PowerShell
  • create a pipeline with a copy activity using Azure PowerShell
  • create a pipeline to transform data using Azure PowerShell
  • monitor a pipeline with Azure PowerShell
  • recognize key features of Azure Machine Learning
  • identify key concepts and terms of Azure Machine Learning
  • recognize key steps in the team data science process and the process lifecycle
  • recognize key features of the Azure Machine Learning Studio
  • recognize key capabilities of the Azure Machine Learning Studio
  • identify key steps to creating an experiment in Azure Machine Learning Studio
  • recognize keys steps to deploy an Azure Machine Learning web service
  • retrain a published experiment
  • recognize key aspects of updating models using the Update Resource Activity
  • use batch scoring activity
  • retrain a new Resource Manager using the Machine Learning Management PowerShell cmdlets
  • recognize key concepts, features and capabilities of Azure Machine Learning

Overview/Description
Azure Big Data Analytics solutions provide great automation and Machine Learning capabilities. This course covers the automation of key tasks using Azure PowerShell and key capabilities of Azure Machine Learning for big data analysis.

Target Audience
Professionals who are preparing to take the 70-475: Designing and Implementing Big Data Analytics Solutions certification exam, and who are experienced in designing, programming, implementing, automating, and monitoring Microsoft Azure cloud platform solutions. Exam candidates should also be adept at using development tools, techniques, and design methodologies associated with the implementations of cloud-based big data analytics solutions.

Designing Batch Processing and Data Security

Course Number:
df_dibd_a02_it_enus
Lesson Objectives

Designing Batch Processing and Data Security

  • start the course
  • recognize key features of Apache Sqoop
  • demonstrate how to import data from an RDBMS to the Hadoop Distributed File System
  • manage and monitor clusters with Apache Ambari
  • manage workflows with Oozie
  • use HCatalog for table and storage management in Hadoop
  • recognize the key functions of Apache Zookeeper
  • use Apache Pig for data analysis
  • manage large datasets with Apache Hive
  • recognize key features and functionalities of Azure Batch
  • recognize key features and functionalities of Apache Mahout
  • identify key features and data sources of Spark SQL
  • use MapReduce for writing applications
  • use PowerShell to handle big data
  • use SQL Server Analysis Services
  • process large datasets with Data Factory and Batch
  • recognize Azure's technical data security capabilities
  • features of role-based and row-based security
  • configure firewall and proxy server settings
  • recognize key functions of Shared Access Signatures
  • recognize key features and capabilities of batch processing technologies

Overview/Description
Big data analytics with Microsoft Azure requires effective deployment of various frameworks and technologies. This course covers batch processing technologies and data security tools in Azure, and prepares you for exam 70-475.

Target Audience
Professionals who are preparing to take the 70-475: Designing and Implementing Big Data Analytics Solutions certification exam, and who are experienced in designing, programming, implementing, automating, and monitoring Microsoft Azure cloud platform solutions. Exam candidates should also be adept at using development tools, techniques, and design methodologies associated with the implementations of cloud-based big data analytics solutions.

Designing the Lambda Architecture and Real-time Processing

Course Number:
df_dibd_a04_it_enus
Lesson Objectives

Designing the Lambda Architecture and Real-time Processing

  • start the course
  • recognize what the Lambda architecture is and how it is used
  • list considerations for the Lambda batch layer design
  • identify considerations for the Lambda serving layer design
  • list considerations for the Lambda speed layer design
  • recognize the key capabilities and limitations of the Lambda architecture
  • recognize the difference between Lambda and Kappa architectures
  • recognize traditional data analytics approaches and how they differ from streaming solutions
  • recognize how value is generated through real-time data analytics solutions
  • identify how Azure Stream Analytics work
  • recognize the benefits and capabilities of Azure Stream analytics
  • compare Apache Storm and Azure Stream Analytics
  • recognize the Azure architecture and the various components of data sources, integration, and real-time analytics
  • recognize the Azure output storage and consumption components
  • design reference data streams from Blob Storage
  • design and configure stream reference data from Event Hubs and IoT source
  • store and view Stream Analytics jobs
  • visualize big data with Power Pivot
  • visualize big data with Power View
  • create custom reports with SQL Server Reporting Services
  • recognize the features of the Lambda architecture and the capabilities of Azure Stream

Overview/Description
The importance of data processing architectures and data visualization to successfully implement real-time big data analytics solutions in Azure cannot be overstated. This course covers the Lambda architecture and Azure Stream Analytics.

Target Audience
Professionals who are preparing to take the 70-475: Designing and Implementing Big Data Analytics Solutions certification exam, and who are experienced in designing, programming, implementing, automating, and monitoring Microsoft Azure cloud platform solutions. Exam candidates should also be adept at using development tools, techniques, and design methodologies associated with the implementations of cloud-based big data analytics solutions.

Ingesting Data and Computing for Batch Processing

Course Number:
df_dibd_a01_it_enus
Lesson Objectives

Ingesting Data and Computing for Batch Processing

  • start the course
  • identify basic features of Microsoft big data solutions
  • recognize storage options for big data and identify methods to load data into Azure Blob storage
  • list key features of the Azure Data Factory and the Azure Data Lake Store
  • use Azure PowerShell with Azure Storage
  • recognize best practices and considerations for data collection and loading in HDInsight
  • recognize key features of Apache Storm and Apache Flume
  • recognize key features of Azure Cosmos DB and DocumentDB
  • store and access .NET web application data with Azure Cosmo DB
  • install and use the Microsoft Azure Storage Explorer
  • load data into an Azure SQL Data Warehouse
  • install and use PolyBase to query data in an Azure Storage account
  • recognize common methods for moving data from an on-premises SQL Server to an Azure Virtual Machine SQL Server
  • recognize features of Hadoop and HDInsight clusters
  • identify how Apache Spark is used with HDInsight
  • recognize the capabilities of HBase in HDInsight
  • identify how Apache Kafka is used with HDInsight
  • recognize the capabilities of Interactive Hive in HDInsight
  • identify how R is used with HDInsight
  • determine which tools to use and identify important security features
  • recognize key features and capabilities of various tools used with HDInsight

Overview/Description
There are many considerations when designing and implementing big data analytics solutions with Microsoft Azure. This course covers data ingesting and storage and designing and provisioning compute clusters, and it aligns with exam 70-475.

Target Audience
Professionals who are preparing to take the 70-475: Designing and Implementing Big Data Analytics Solutions certification exam, and who are experienced in designing, programming, implementing, automating, and monitoring Microsoft Azure cloud platform solutions. Exam candidates should also be adept at using development tools, techniques, and design methodologies associated with the implementations of cloud-based big data analytics solutions

Ingesting Data and Computing for Real-time Processing

Course Number:
df_dibd_a03_it_enus
Lesson Objectives

Ingesting Data and Computing for Real-time Processing

  • start the course
  • identify some common use cases for real-time analytics
  • recognize the Apache NiFi capabilities for real-time big data analytics
  • identify the Apache NiFi architecture and performance characteristics
  • list key features of Apache NiFi
  • recognize how Azure Event Hubs capabilities are used for real-time big data analytics
  • list key features of Azure Event Hubs
  • create an Event Hub using the Azure portal
  • send messages to Azure Event Hubs in .NET Standard
  • receive messages with the event processor host in .NET Standard
  • receive events from Event Hubs using Apache Storm
  • enable Event Hubs capture using the Azure portal
  • recognize aspects of row key design in HBase
  • recognize various data partitioning schemes
  • design partitions for scalability, query performance, and availability
  • describe fundamental architectural concepts of enterprise analytics
  • recognize key components for real-time event processing
  • create Linux-based clusters in HDInsight using Azure PowerShell
  • manage Hadoop clusters in HDInsight using Azure PowerShell
  • list main phases in setting up a storm cluster
  • use data ingesting tools for real-time analytics

Overview/Description
Real-time big data analytics with Microsoft Azure is used in numerous critical applications that require immediate analysis and action. This course covers data ingesting and designing compute resources for real-time processing.

Target Audience
Professionals who are preparing to take the 70-475: Designing and Implementing Big Data Analytics Solutions certification exam, and who are experienced in designing, programming, implementing, automating, and monitoring Microsoft Azure cloud platform solutions. Exam candidates should also be adept at using development tools, techniques, and design methodologies associated with the implementations of cloud-based big data analytics solutions.

Managing Activities and Data for Azure Big Data Analytics

Course Number:
df_dibd_a06_it_enus
Lesson Objectives

Managing Activities and Data for Azure Big Data Analytics

  • start the course
  • launch and navigate the Monitoring and Management app
  • navigate the Resource Explorer tab
  • create alerts to get notified of errors
  • rerun selected activities and pause or resume multiple pipelines
  • navigate the Diagram View
  • identify potential errors and solutions with Data Factory
  • recognize common problems with installation and registration and possible solutions
  • recognize common problems with limited functionality and possible solutions
  • recognize other possible problems and solutions
  • use Azure Data Factory Copy Wizard
  • recognize steps to load big data from Azure Blob Storage into Azure SQL Data Warehouse
  • recognize steps to copy data from on-premise to a cloud storage and from one cloud storage to another
  • recognize methods for data transformation and prerequisites
  • transform data with a pipeline using Visual Studio
  • use Hive Activity to transform data
  • use Pig Activity to transform data
  • use MapReduce Activity
  • recognize how to use stored procedure activities
  • recognize how to create custom activities
  • monitor and identify common problems and recognize their solutions

Overview/Description
Implementing Azure big data analytics requires managing the Data Factory, loading and transforming data. This course covers monitoring the Azure Data Factory and troubleshooting problems as well as loading and transforming data.

Target Audience
Professionals who are preparing to take the 70-475: Designing and Implementing Big Data Analytics Solutions certification exam, and who are experienced in designing, programming, implementing, automating, and monitoring Microsoft Azure cloud platform solutions. Exam candidates should also be adept at using development tools, techniques, and design methodologies associated with the implementations of cloud-based big data analytics solutions.

Provisioning an Azure Data Factory

Course Number:
df_dibd_a05_it_enus
Lesson Objectives

Provisioning an Azure Data Factory

  • start the course
  • identify key features of Azure Data Factory
  • identify key components and data sources for Azure Data Factory
  • list Azure Data Factory functions, variables, and naming rules
  • recognize the main steps and prerequisites to create and publish a Data Factory with Visual Studio
  • create and publish a Data Factory with Visual Studio
  • recognize the capabilities of Data Factory Datasets
  • identify key features of Data Factory Datasets
  • recognize the structure of Data Factory Datasets
  • create a Data Factory Dataset with Visual Studio
  • recognize key properties and the JSON structure of pipelines and activities in Azure Data Factory
  • identify the key policies that affect the run-time behavior of an activity in Azure Data Factory
  • create and publish pipelines
  • monitor pipelines with the Azure Portal
  • configure activity and dataset scheduling
  • configure dataset availability
  • configure dataset policies
  • recognize data slicing features and concepts for parallel processing and re-running failed data slices
  • identify how to chain multiple activities
  • model complex dataset schedules
  • create and publish a Data Factory and monitor pipelines with Azure Portal

Overview/Description
Azure's Data Factory is a key component for end-to-end cloud analytics solutions. This course covers the provisioning of the components of an Azure Data Factory and implementation of data processing activities in a data-driven workflow.

Target Audience
Professionals who are preparing to take the 70-475: Designing and Implementing Big Data Analytics Solutions certification exam, and who are experienced in designing, programming, implementing, automating, and monitoring Microsoft Azure cloud platform solutions. Exam candidates should also be adept at using development tools, techniques, and design methodologies associated with the implementations of cloud-based big data analytics solutions.

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