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Big Data Analytics

Big Data on Amazon Web Services (AWS)

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  • Overview

    Big Data on AWS introduces you to cloud-based big data solutions such as Amazon Elastic MapReduce (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, and Amazon Kinesis, and leverage best practices to design big data environments for security and cost-effectiveness.

    Upon completing this course, students will be able 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 and Spark SQL on Amazon EMR
    • Choose appropriate AWS data storage options
    • Identify the benefits of using Amazon Kinesis for near real-time big data processing
    • Define data warehousing and columnar database concepts
    • Leverage Amazon Redshift to efficiently store and analyze data
    • Comprehend and manage costs and security for Amazon EMR and Amazon Redshift deployments
    • Identify options for ingesting, transferring, and compressing data
    • Use visualization software to depict data and queries
    • Orchestrate big data workflows using AWS Data Pipeline
  • Who Should Take This Course


    This course is for individuals responsible for designing and implementing big data solutions, namely Solutions Architects and SysOps Administrators. It is also for Data Scientists and Data Analysts interested in learning about big data solutions on AWS.


    Students should have basic familiarity with big data technologies, including Apache Hadoop, MapReduce, and HDFS, and SQL/NoSQL querying. They should also have completed the Big Data Technology Fundamentals web-based training or have equivalent experience and have working knowledge of core AWS services and public cloud implementation.

  • Schedule
  • Course Outline

    Day 1

    • Overview of Big Data
    • Ingestion, Transfer, and Compression
    • Storage Solutions
    • Storing and Querying Data on DynamoDB
    • Big Data Processing and Amazon Kinesis
    • Introduction to Apache Hadoop and Amazon EMR
    • Using Amazon Elastic MapReduce

    Day 2

    • Hadoop Programming Frameworks
    • Processing Server Logs with Hive on Amazon EMR
    • Processing Chemistry Data Using Hadoop Streaming on Amazon EMR
    • Streamlining Your Amazon EMR Experience with Hue
    • Running Pig Scripts with in Hue on Amazon EMR
    • Spark on Amazon EMR
    • Interactively Creating and Querying Tables with Spark and Spark SQL on Amazon EMR
    • Managing Amazon EMR Costs
    • Securing your Amazon EMR Deployments

    Day 3

    • Data Warehouses and Columnar Datastores
    • Amazon Redshift and Big Data
    • Optimizing Your Amazon Redshift Environment
    • Big Data Design Patterns
    • Visualizing and Orchestrating Big Data
    • Using Tibco Spotfire to Visualize Big Data
  • FAQs
    Is there a discount available for current students?

    UMBC students and alumni, as well as students who have previously taken a public training course with UMBC Training Centers are eligible for a 10% discount, capped at $250. Please provide a copy of your UMBC student ID or an unofficial transcript or the name of the UMBC Training Centers course you have completed. Online courses are excluded from this offer.

    What is the cancellation and refund policy?

    Student will receive a refund of paid registration fees only if UMBC Training Centers receives a notice of cancellation at least 10 business days prior to the class start date for classes or the exam date for exams.

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