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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.

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Microsoft

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Advanced

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16 Hours

Course Duration

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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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BrandMicrosoft
SubjectsBig Data, Cloud Computing, DevOps
Lab AccessNo
TechnologyMicrosoft
Learning StyleSelf-Paced Learning
Learning TypeCourse
DifficultyAdvanced
Course Duration16 Hours
LanguageEnglish

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Author

Tom Robertson
(Data Science Enthusiast)

Tom Robertson is a Python-based data scientist with domain knowledge in behavioral neuroscience research and non-profit development. He is currently employeed as Data Science Instructor at QuickStart Technologies. Tom is inspired by the belief that data science can improve quality of life by providing our researchers, doctors and policy-makers with new, data-driven insights.

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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