Building Batch Data Analytics Solutions on AWS

1 day

English

In Kooperation mit

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.

Content

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

Benefits

Comparing the features and benefits of data warehouses, data lakes, and modern data architectures
Designing and implementing a batch data analytics solution
Identifying and applying appropriate techniques, including compression, to optimize data storage
Selecting and deploying appropriate options to ingest, transform, and store data
Choosing the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case
Understandv how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights
Securing data at rest and in transit
Monitoring analytics workloads to identify and remediate problems
Applying cost management best practices

IMPORTANT: This course prepares you for AWS Data Analytics Certification, among other courses of the Data Analytics job role track.

Instructor

Yuri Nikulin

Methods

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

Who should attend

This course is intended for the following job roles:

Data Analytics

We recommend that attendees of this course have the following prerequisites:

a minimum one-year experience managing open-source data frameworks such as Apache Spark or Apache Hadoop
basic knowledge on AWS Hadoop Fundamentals or a refresher on Apache Hadoop

and have attended the following course (or have equivalent knowledge):

Building Data Analytics Solutions Using Amazon Redshift
Data Warehousing on AWS