An introduction to data science techniques in Azure will also be covered. Individual case studies will focus on specific real-world problems that represent common big data patterns and practices. Students will also experience several hands-on labs to introduce them to some of the key services available.
- Students will be taught basic Lambda architecture patterns in Azure, leveraging the scalability and elasticity of Azure in Big Data and IoT solutions.
- An introduction to data science techniques in Azure will also be covered.
- Individual case studies will focus on specific real-world problems that represent common big data patterns and practices.
- Students will also experience several hands-on labs to introduce them to some of the key services available.
- How to design and explain data focused solutions
- Suggested to have core understanding of cloud technologies
- Background in database administration, data architecture or data science
MODULE 1: Overview Big Data and IoT Solutions in Azure
This module will provide an overview of big data and IoT solutions in Azure. We will look at the Azure components used in big data solution and how they fit together to build an end-to-end lambda architecture in Azure.
MODULE 2: Architecting an Enterprise Data warehouse
This module will focus on architecting Azure SQL Data Warehouse solutions. We will cover the basic architecture of Azure SQL Data Warehouse, loading data into Azure SQL Data Warehouse and managing Azure SQL Data Warehouse. We will also look at various integration points such as interoperability with Power BI, Azure Analysis Services, Azure Data Factory, Azure Blob Storage, and on-premises Hadoop clusters.
MODULE 3: Managing and Orchestrating Data Processes in Azure
This module will be focused on managing and monitoring data pipelines in Azure. We will use Azure Data Factory to show how to build data pipelines with built-in monitoring and the ability to connect to a variety of data sources both in Azure and on-premises.
Whiteboard Design and Lab
In this architecture session students will work as a team and learn to migrate and existing enterprise data warehouse to Azure SQL Data Warehouse.
In the lab, the students will migrate an existing data warehouse to Azure SQL Data Warehouse. During the lab students will learn how to leverage Polybase to optimize data loads and investigate various table distribution methods to optimize query performance of the data warehouse.
MODULE 4: Big Data Processing in Azure
This module will teach students how to architect big data processing solutions in Azure including both batch and interactive soltions. We will cover a variety of scenarios including HDInsight, Spark, Storm, R Server, Hadoop on IaaS, and Azure Data Lake.
MODULE 5: Real-time Data Ingestion in Azure
In this module, students will learn about real-time data ingest and the storage of data received using queue based services provided by IoT Hub and Event Hubs. We will also look at processing real-time data using Stream Analytics.
Whiteboard Design and Lab
In this architecture session students will work as a team and learn to design real-time ingestion solutions in Azure.
In the lab students will build a full IoT solution including IoT device management, IoT data ingestion, data storage and processing
MODULE 6: Understanding Machine Learning
This module provides students with a jump start into machine learning processes critical to understanding more advanced Azure ML architectures.
MODULE 7: Azure Machine Learning Services
This module will focus on more advanced services for machine learning in Azure. We will discuss Azure Machine Learning Services, Microsoft Machine Learning Workbench, Microsoft Cognitive Toolkit and the Microsoft Data Science Virtual Machine.
MODULE 8: Enterprise Scale Machine Learning
This module will teach students how to leverage Microsoft Machine Learning Server to handle big data machine learning scenarios. Students will be introduced to Microsoft Machine Learning Server including Machine Learning Services for SQL Server and R-Server for HDInsight.
MODULE 9: Designing AI Solutions with Cognitive Services
In this module, attendees will learn how to develop solutions that take advantage of artificial intelligence and deep learning using Microsoft Azure Cognitive Services.
Whiteboard Design and Lab
In this architecture session students will design a big data solution leveraging patterns learned throughout the class. The solution will focus on real-time analytics with Azure Machine Learning.
In the lab, students will build an end-to-end big data and machine learning solution. This will include model training and evaluation, operationalizing machine learning models and visualizing machine learning.
Dedicated instructor-led training is designed for group training and is delivered by the experts at Opsgility. Delivery availability is anywhere in the world at your location or using advanced virtual training software.
- Standard or Customized Curriculum
- Globally Available for Delivery
- Holistic Learning Plans are Available
- Industry Recognized Subject Matter Experts