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Big Data on AWS Training

This course is designed to give the participants an insight into big data solutions based on Cloud such as Amazon EMR, Amazon Redshift, Amazon Kinesis and the other services available on the AWS big data platform.

Virtual

Learning Style

Amazon Web Services

Provider

Intermediate

Difficulty

3 Days

Course Duration

Course Info

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This course is designed to give the participants an insight into big data solutions based on Cloud such as Amazon EMR, Amazon Redshift, Amazon Kinesis and the other services available on the AWS big data platform.

Course Information

About this Course:

This course is designed to give the participants an insight into big data solutions based on Cloud such as Amazon EMR, Amazon Redshift, Amazon Kinesis and the other services available on the AWS big data platform. This course will introduce and help students gain the skills needed to use Amazon EMR to process data via the use of multiple available Hadoop tools such as Hive and Hue. This course will also teach students how to design efficient big data environments. The participants will also gain skills needed to effectively use Amazon DynamoDB Amazon Redshift, Amazon Quicksight, Amazon Athena and Amazon Kinesis. They will be taught the best practices to design  cost-efficient, secure and effective big data environments

Course Objective:

 Upon completion of this course, the participant should have an advanced skill set and a sound working knowledge of the following principals while also be able to;

  • Learn how to incorporate AWS solutions in big data ecosystems
  • Learn how to Leverage Apache Hadoop for use in Amazon EMR
  • Learn the different components of a cluster in Amazon EMR
  • Gain the skills needed to configure and launch and Amazon EMR cluster
  • Learn how to leverage common programming frameworks available on Amazon EMR such as but not limited to Hive, Pig, and Streaming
  • Learn how to effectively leverage Hue to improve the ease-of-use of Amazon EMR
  • Learn how to use in memory analytics with Spark on Amazon EMR
  • Learn how to choose the best storage options from the data storage options available on AWS
  • Recognize the pros of Amazon Kinesis for near real-time big data processing
  • Learn how to efficiently store and analyze data using Amazon Redshift
  • Create a cost effective big data solution and manage its security
  • Learn how to secure a big data solution
  • Learn how to identify options for ingesting, transferring, and compressing data
  • Learn how to leverage Amazon Athena for ad-hoc query analytics
  • Learn how to Leverage AWS Glue to automate ETL workloads
  • Learn the use of visualization software for the depiction of data and queries through Amazon QuickSight
  • Plan and demonstrate big data workflows using AWS Data Pipeline

Audience:

This particular course is aimed at the following audience;

  • Solutions architects or those responsible for designing and executing big data solutions
  • Data scientists and data analysts with a wish to lean about the patterns of architecture lying behind the big data solutions available on AWS

Prerequisites:

The following prerequisites are absolutely necessary to be eligible to take this Microsoft Word 2019 course;

  • Have a basic understanding of big data tech namely Apache, Hadoop, Mapreduce, HDFS and SQL/NoSQL queries.
  • Have completed the Big Data technology Fundamentals web based training available for free or have experience equivalent to the course
  • Possess a functional knowledge of the core services provided by AWS and the implementation of public cloud
  • Have a fundamental know how of data warehousing, relational database systems and database design

Outline

More Information

More Information
BrandAmazon Web Services
SubjectsBig Data
Lab AccessNo
TechnologyAWS
TopicsBig Data
Learning StyleVirtual Classroom
Learning TypeCourse
DifficultyIntermediate
Course Duration3 Days
LanguageEnglish

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Course Expert:

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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