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Big Data on AWS

In this AWS Big Data certification course, we show you how to use Amazon EMR to process data using the broad ecosystem of Hadoop tools like Hive and Hue.

Virtual

Learning Style

Amazon Web Services

Provider

Intermediate

Difficulty

3 Days

Course Duration

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In this AWS Big Data certification course, we show you how to use Amazon EMR to process data using the broad ecosystem of Hadoop tools like Hive and Hue.

Course Information

About this Course:

Big Data on AWS introduces you to cloud-based big data solutions such as Amazon EMR, Amazon Redshift, Amazon Kinesis and the rest of the AWS big data platform. In this course, we show you how to use Amazon EMR to process data using the broad ecosystem of Hadoop tools like Hive and Hue. We also teach you how to create big data environments, work with Amazon DynamoDB, Amazon Redshift, Amazon Quicksight, Amazon Athena and Amazon Kinesis, and leverage best practices to design big data environments for security and cost-effectiveness.

Objectives:

This AWS Big Data Certification course teaches you how to:

  • Fit AWS solutions inside of a big data ecosystem
  • Leverage Apache Hadoop in the context of Amazon EMR
  • Identify the components of an Amazon EMR cluster
  • Launch and configure an Amazon EMR cluster
  • Leverage common programming frameworks available for Amazon EMR including Hive, Pig, and Streaming
  • Leverage Hue to improve the ease-of-use of Amazon EMR
  • Use in-memory analytics with Spark on Amazon EMR
  • Choose appropriate AWS data storage options
  • Identify the benefits of using Amazon Kinesis for near real-time big data processing
  • Leverage Amazon Redshift to efficiently store and analyze data
  • Comprehend and manage costs and security for a big data solution
  • Secure a Big Data solution
  • Identify options for ingesting, transferring, and compressing data
  • Leverage Amazon Athena for ad-hoc query analytics
  • Leverage AWS Glue to automate ETL workloads
  • Use visualization software to depict data and queries using Amazon QuickSight
  • Orchestrate big data workflows using AWS Data Pipeline

Audience:

This AWS Big Data Certification course is intended for:

  • Individuals responsible for designing and implementing big data solutions, namely Solutions Architects
  • Data Scientists and Data Analysts interested in learning about the services and architecture patterns behind big data solutions on AWS

Prerequisites:

We recommend that attendees of this Big Data Certification course have the following prerequisites:

  • Basic familiarity with big data technologies, including Apache Hadoop, MapReduce, HDFS, and SQL/NoSQL querying
  • Students should complete the free Big Data Technology Fundamentals web-based training or have equivalent experience
  • Working knowledge of core AWS services and public cloud implementation
  • Students should complete the AWS Technical Essentials course or have equivalent experience
  • Basic understanding of data warehousing, relational database systems, and database design

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Outline

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Brand Amazon Web Services
Subjects Big Data
Lab Access No
Technology AWS
Topics Big Data
Learning Style Virtual Classroom
Learning Type Course
Difficulty Intermediate
Course Duration 3 Days
Language English

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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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