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Processing Big Data with Azure Data Lake Analytics

This course of data analysis shows you how to implement the intensity of the cloud of Azure to big data utilizing the technologies of Azure Data Lake.

Self-Paced

Learning Style

Microsoft

Provider

Advanced

Difficulty

16 Hours

Course Duration

Course Info

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This course of data analysis shows you how to implement the intensity of the cloud of Azure to big data utilizing the technologies of Azure Data Lake.

Course Information

About this course:

Need to process and store information at scale? This course of data analysis shows you how to implement the intensity of the cloud of Azure to big data utilizing the technologies of Azure Data Lake.

Figure out how to oversee information in Azure Data Lake Store and run the jobs of U-SQL in the Analytics of Azure Data Lake to produce bits of knowledge from unstructured and structured information sources.

Note: To finish this course, you will require a subscription to MS Azure. You can log in to a free preliminary membership at http://azure.microsoft.com, or you can utilize your current membership. The labs have been intended to limit the costs of resources required to finish the hands-on exercises.

Course Objective:

  • Technologies of Azure Data Lake to process and store data utilizing U-SQL jobs
  • Make and use U-SQL index objects
  • Expand your scripts of data handling with custom C# code
  • Look and optimize U-SQL jobs
  • Overview to the Analytics of Azure Data Lake
  • Learn the fundamentals of U-SQL
  • Getting Started with Azure Data Lake
  • U-SQL Database Objects
  • Using C# in U-SQL
  • Monitoring U-SQL Execution
  • Optimizing U-SQL Jobs

Audience:

Programmers

Data Analyst

Prerequisite:

No prerequisite required for this course

Outline

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More Information
Brand Microsoft
Subjects Big Data, Cloud Computing, DevOps
Lab Access No
Technology Microsoft
Learning Style Self-Paced Learning
Learning Type Course
Difficulty Advanced
Course Duration 16 Hours
Language English

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Author

Tom Robertson
(Data Science Enthusiast)

Tom is an innovator first, and then a Data Scientist & Software Architect. He has integrated expertise in business, product, technology and management. Tom has been involved in creating category defining new products in AI and big data for different industries, which generated more than hundred million revenue cumulatively, and served more than 10 million users.
As a Data Scientist and Software Architect Tom has extensive experience in data science, engineering, architecture and software development. To date Tom has accumulated over a decade of experience in R, Python & Linux Shell programming.

Tom has expertise on Python, SQL, and Spark. He has worked on several libraries including but not limited to Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn, SciPy, NLTK, Keras, and Tensorflow.

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