Building Batch Data Analytics Solutions on AWS

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Building Batch Data Analytics Solutions on AWS

Building Batch Data Analytics Solutions on AWS

Course ID: AWS-DABATC

Duration: 1 Day

Training Fee: HK$6,000

Overview

In this 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 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.

Private in-house training
Apart from public, instructor-led classes, we also offer private in-house trainings for organizations based on their needs. Call us at +852 2116 3328 or email us at [email protected] for more details.

Why Choose Us?

The First Authorised Training Partner of AWS with full license
The Most Training Schedules delivered by AWS Authorized Instructors (AAI) and AAI Champion
Best Price Guaranteed
Trained over 50,000 talents in Asia
High Passing Rate: 90%
Appointed Exam Centre

Course Objectives

In this course, you will learn 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

Prerequisites

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

We suggest the AWS Hadoop Fundamentals course for those that need a refresher on Apache Hadoop.

We recommend that attendees of this course have:

Completed either AWS Technical Essentials or Architecting on AWS
Completed either Building Data Lakes on AWS or Getting Started with AWS Glue

Intended Audience

This course is intended for:

Data platform engineers
Architects and operators who build and manage data analytics pipelines

Activities

This course includes presentations, interactive demos, practice labs, discussions, and class exercises.

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: Interactive analytics using Apache Spark on Amazon EMR
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 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 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: Encrypting data at rest in Amazon EMR
Monitoring and troubleshooting EMR clusters
Demo: Reviewing Apache Spark cluster history
Monitoring and troubleshooting Amazon EMR clusters

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

Building Batch Data Analytics Solutions on AWS