Description

Duration

1 Days

6 CPD hours

This course is intended for

This course is intended for:
Data platform engineers
Architects and operators who build and manage data analytics pipelines

Overview

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

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 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.

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

Course Images

Building Batch Data Analytics Solutions on AWS

By Nexus Human

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