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Building Batch Data Analytics Solutions on AWS

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Course description

You will develop batch data analytics solutions in this course utilizing Amazon EMR; an enterprise-grade managed service for Apache Spark and Apache Hadoop. You will see how Amazon EMR works with AWS services like AWS Glue and AWS Lake Formation, as well as with open-source initiatives like Apache Hive, Hue, and HBase. The course covers Spark and Hadoop’s data gathering, ingestion, categorization, storage, and processing components. You will also learn to support analytics and machine learning workloads with EMR Notebooks. Additionally, you will discover how to use Amazon EMR’s best practices for security, performance, and cost management.

Activities

This AWStraining course will include the following activities: 

  • Live Demonstrations 
  • Presentations 
  • Group Discussions 
  • Hands-On Labs 
  • Frequent Knowledge Checks

Course Objectives

Here’s what you’ll learn in this AWS course: 

  • Compare the attributes and advantages of data lakes, data warehouses, and contemporary data architectures. 
  • Create and put into action a batch data analytics system. 
  • Determine the most effective methods for data storage, including compression, and use them. 
  • Choose and implement the best alternatives for ingesting, transforming, and storing data. 
  • For a specific business use case, select the proper clusters, auto-scaling, instance and node kinds, and network topology. 
  • Recognize how data processing and storage impact the analysis and visualization techniques required to produce relevant business insights. 
  • Secure analytics workloads to find and fix issues with data in transit and at rest. 
  • Implement best practices for cost management

Intended Audience

You can take on this course if you belong to the following category of individuals: 

  • Platform engineers for data 
  • Designers and managers of the data analytics pipelines

Prerequisites

This Data Analytics on AWS course will be most helpful to students with at least a year of experience handling open-source data frameworks like Apache Spark or Apache Hadoop, and for individuals who need a refresher on Apache Hadoop, we advise taking the AWS Hadoop Fundamentals course. 

Module Breakdown

Course outline

Module A: Overview of Data Analytics and the Data Pipeline

  • Data analytics use cases
  • Using the data pipeline for analytics

Module 1: Introduction to Amazon EMR

  • Using Amazon EMR in analytics solutions
  • Amazon EMR cluster architecture
  • Interactive Demo 1: Launching an Amazon EMR cluster
  • Cost management strategies

Module 2: Data Analytics Pipeline Using Amazon EMR: Ingestion and Storage

  • Storage optimization with Amazon EMR
  • Data ingestion techniques

Module 3: High-Performance Batch Data Analytics Using Apache Spark on Amazon EMR

  • 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

Module 4: Processing and Analyzing Batch Data with Amazon EMR and Apache Hive

  • 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

Module 5: Serverless Data Processing

  • 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

Module 6: Security and Monitoring of Amazon EMR Clusters

  • Securing EMR clusters
  • Interactive Demo 3: Client-side encryption with EMRFS
  • Monitoring and troubleshooting Amazon EMR clusters
  • Demo: Reviewing Apache Spark cluster history

Module 7: Designing Batch Data Analytics Solutions

  • Batch data analytics use cases
  • Activity: Designing a batch data analytics workflow

Module B: Developing Modern Data Architectures on AWS

  • Modern data architectures

CERTIFICATION: AWS CERTIFIED DATA ANALYTICS - SPECIALTY

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EXAM READINESS: AWS CERTIFIED DATA ANALYTICS - SPECIALTY

The AWS Certified Data Analytics – Specialty exam verifies candidates’ proficiency in building, deploying, and fine-tuning data models, as well as in utilizing AWS services to scale up this process. In this intermediate-level course, learn how to study for the exam by reviewing the subject areas and how they relate to Data analysis on AWS.

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FAQ's

Is Cloud Wizard Consulting an official AWS training partner?

Yes, we have been since 2015.

Who should take up this course?

You can take on this course if you belong to the following category of individuals: 

  • Platform engineers for data 
  • Designers and managers of the data analytics pipelines

What prior experience do you need for this course?

This Data Analytics on AWS course will be most helpful to students with at least a year of experience handling open-source data frameworks like Apache Spark or Apache Hadoop, and for individuals who need a refresher on Apache Hadoop, we advise taking the AWS Hadoop Fundamentals course. 

  • It is advised that participants in this course have completed either Architecting on AWS or AWS Technical Essentials. 
  • Completed the Getting Started with AWS Glue or Building Data Lakes on AWS course.

How do I enroll in this course?

To enroll in this course, choose the starting date and make an online payment. Once your payment is confirmed, our team will reach out to you.

Course Schedule

Course Name Date Register
No schedule found for this Course.

Course Overview

Duration 1D / 8 HRS
Modality ViLT/ Classroom
Data Sheet DOWNLOAD
Check Dates CLICK HERE
Price 15,000.00

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