Building Batch Data Analytics Solutions on AWS

This course is intended for:

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

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

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
  
  

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

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

Select any or all of our four one-day courses to learn best practices in Building Modern Data Analytics Solutions on AWS collection. Choose only the courses you need to match the services and solutions that are relevant to you.

When you buy all the courses included in the collection at the same time, you get them for combined price of €2490 + VAT (norm. €3590). If you want to take advantage of this benefit, mention it in the additional information field of the enrollment form.