Building Batch Data Analytics Solutions on AWS

Course 1239

  • Duration: 1 day
  • Language: English
  • Level: Intermediate

In this Building Batch Data Analytics Solutions on AWS course, you will learn to build batch data analytics solutions using Amazon EMR, an enterprise-grade Apache Spark and Apache Hadoop managed service. You will learn how Amazon EMR integrates with open-source projects such as Apache Hive, Hue, and HBase, and with AWS services such as AWS Glue and AWS Lake Formation. The course addresses data collection, ingestion, cataloging, storage, and processing components in the context of Spark and Hadoop. You will learn to use EMR Notebooks to support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost management best practices to the operation of Amazon EMR.

Building Batch Data Analytics Solutions on AWS Delivery Methods

  • Online

Building Batch Data Analytics Solutions on AWS

In this Building Batch Data Analytics Solutions on AWS course, you will learn how to:

  • Compare the features and benefits of data warehouses, data lakes, and modern data architectures.
  • Design and implement a batch data analytics solution.
  • Identify and apply appropriate techniques, including compression, to optimize data storage.
  • Select and deploy appropriate options to ingest, transform, and store data.
  • Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case.
  • Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights.
  • Secure data at rest and in transit.
  • Monitor analytics workloads to identify and remediate problems.
  • Apply cost management best practices.

Building Batch Data Analytics Solutions on AWS Prerequisites

We recommend that attendees of this course have:

  • Completed the AWS Technical Essentials classroom course.
  • One year of experience building data analytics pipelines or have completed the Data Analytics Fundamentals digital course.

Building Batch Data Analytics Solutions on AWS Course Outline

  • Data analytics use cases
  • Using the data pipeline for analytics
  • Using Amazon EMR in analytics solutions
  • Amazon EMR cluster architecture

Interactive Demo 1: Launching an Amazon EMR cluster

  • Cost management strategies
  • Storage optimization with Amazon EMR
  • Data ingestion techniques
  • Apache Spark on Amazon EMR use cases
  • Why Apache Spark on Amazon EMR
  • Spark concepts

Interactive Demo 2: Connect to an EMR cluster and perform Scala commands using the Spark shell

  • Transformation, processing, and analytics
  • Using notebooks with Amazon EMR

Practice Lab 1: Low-latency data analytics using Apache Spark on Amazon EMR

  • Using Amazon EMR with Hive to process batch data
  • Transformation, processing, and analytics

Practice Lab 2: Batch data processing using Amazon EMR with Hive

  • Introduction to Apache HBase on Amazon EMR
  • Serverless data processing, transformation, and analytics
  • Using AWS Glue with Amazon EMR workloads

Practice Lab 3: Orchestrate data processing in Spark using AWS Step Functions

  • Securing EMR clusters

Interactive Demo 3: Client-side encryption with EMRFS

  • Monitoring and troubleshooting Amazon EMR clusters

Demo: Reviewing Apache Spark cluster history

  • Batch data analytics use cases

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Building Batch Data Analytics Solutions on AWS FAQs

Students with a minimum one-year experience managing open-source data frameworks such as Apache Spark or Apache Hadoop will benefit from this course.

No, this course is not accompanied by an exam or certification.

We recommend Learning Tree course 1240, Building Data Analytics Solutions Using Amazon Redshift.

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