Your accelerated 2-day Azure Academy course will teach you how to unleash the analytics power of Azure Data Lake and Data Factory. Firebrand has worked closely with Microsoft and partners to develop this deep-dive Azure course, which includes more than 80% in-depth technical content not found in Microsoft Official Curriculum.
Using technologies like Hadoop, SQL, and Azure Data Lake Analytics, you'll learn to use Azure Data Factory to orchestrate the movement and transformation of your data. You'll also learn how to use Azure Data Lake technologies in your big data applications to generate insights from structured and unstructured data sources.
You'll cover a range of exciting topics, as you learn:
You'll be immersed in these Azure technologies, using Firebrand's Lecture | Lab | Review technique to help you learn faster. You'll do this by combining theory with hands-on practical labs, supported by structured review sessions to reinforce learning and accelerate knowledge development. The labs are also designed to develop skills that you can transfer directly into real world working scenarios.
Introduction to Data Lake - You'll learn what Azure Data Lake is as well as key concepts and capabilities. This includes how to make it easy for developers, data scientists and analysts to store data of any size, shape and speed, and do all types of processing and analytics across platforms and languages.
In this topic you will learn to write, run and manage on-demand analytics jobs using the U-SQL language.
In this topic you will learn how to extend the U-SQL language by programming in C#.
In this topic you will learn how to schedule, manage and troubleshoot U-SQL jobs
Learn how to use Data Factory to compose data storage, movement, and processing services into automated data pipelines.
Learn to develop end-to-end Data Factory pipelines using Visual Studio.
Learn scheduling and execution aspects of the Azure Data Factory application model. Including tumbling windows, concurrency, dependency and troubleshooting failed jobs.
Learn how to transform and process raw data into predictions and insights. You'll do this using Hadoop, Azure Machine Learning, Stored Procedures, and Azure Data Lake Analytics technologies.
The following is included on your accelerated Data Lake & Data Factory course:
Your accelerated course includes:
It is recommended you have achieved the following three Microsoft Specialist certifications, or have equivalent knowledge:
If you don't have the above certifications, you should possess the following equivalent knowledge:
Unsure whether you meet the prerequisites? Don’t worry. Your training consultant will discuss your background with you to understand if this course is right for you.
Here's the Firebrand Training review section. Since 2001 we've trained exactly 134561 students and asked them all to review our Accelerated Learning. Currently, 96.41% have said Firebrand exceeded their expectations.
Read reviews from recent accelerated courses below or visit Firebrand Stories for written and video interviews from our alumni.
"Really impressed with the training and the on-site facilities. Its been a great experience. "
Matt Hill, Coeo Ltd. (30/3/2017 (Thursday) to 31/3/2017 (Friday))
"Good experience at the course. Great introduction and working experience with Data Lakes and Data Factory.
The instructor was very patient and clear."
Terry Choo, eBECS. (30/3/2017 (Thursday) to 31/3/2017 (Friday))
"Great trainers and facilities, and being away from work & family really helps you focus on the material."
R.C.M., PGI - Protection Group International Ltd.. (22/4/2024 (Monday) to 27/4/2024 (Saturday))
"Excellent training. Material and instructor very good. Good facilities. Preferred method of learning moving forward."
J.B.. (22/4/2024 (Monday) to 27/4/2024 (Saturday))
"The in-person training is always effective. I have never done certification before I have around 20+ yrs of exp and I did my first one with Firebrand. The only reason I passed the exam due to focused time and no distraction."
Arul Jayaraman, Yayati consulting ltd. (22/4/2024 (Monday) to 27/4/2024 (Saturday))
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