Implementing an Azure Databricks Environment in Microsoft Azure
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Every day, we have more and more data, and the problem is how do we get to where we can use the data for business needs. In this course, Implementing a Databricks Environment in Microsoft Azure, you will learn foundational knowledge and gain the ability to implement Azure Databricks for use by all your data consumers like business users and data scientists. First, you'll learn the basics of Azure Databricks and how to implement ts components. Next, you will discover how to work with Azure Databricks during ETL (Extract, Transform, Load) operations. Then, you'll move on to performing batch scoring with machine learning models. Finally, you will explore how to work with streaming data from HDInsight Kafka. When you’re finished with this course, you will have the skills and knowledge of Azure Databricks needed to implement data pipeline solutions for your data consumers. Software required: Microsoft Azure Subscription
About the author
Michael is a six-time Microsoft Most Valuable Professional, author, technical trainer, and community leader. Having been in the IT industry since the 90's, his experiences covers the gamut of Microsoft technologies, with his main focus being Windows Server, PowerShell and cloud technologies like AWS and Azure. Along with training, he has a passion for connecting people and building community in the IT Pro space. He is the current president and a founding member of The Krewe User Groups, Inc., a world-wide networking group for IT Pros and Developers.
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Course Overview
Hi, everyone. My name is Michael Bender and welcome to my course, Implementing Azure Databricks in Microsoft Azure. I'm an author evangelist at Pluralsight. Working with big data is a challenge. Azure Databricks make spinning up resources to solve your big data analysis issues quick and easy. With this course, you'll gain the ability to implement Azure Databricks for use by all of your data consumers like business users and data scientists. Some of the major topics that we'll cover include: Fundamental components of Azure Databricks, working with data and Databricks notebooks, building an ML scoring pipeline and ingesting streaming data. By the end of this course, you'll have the skills and knowledge of Azure Databricks needed to implement data pipeline solutions for your data consumers. Before beginning the course, you should be familiar with building and deploying Azure data solutions like Azure SQL, Azure SQL Data Warehouse, and Azure Data Lakes. You should be able to understand machine learning concepts and model building techniques and also know a common language like Python, Scala or SQL for working with data. From here, you should feel comfortable diving into Azure Data Pipelines with courses on batch scoring, Azure DevOps and Azure automation. I hope you'll join me on this journey to learn Azure Databricks with the Implementing Azure Databricks in Microsoft Azure course at Pluralsight.
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