Building Streaming Data Analytics Solutions on AWS

current course dates can be found at the bottom of this page … company training available on request!

Course description

In this course, you will learn to build streaming data analytics solutions using AWS services, including Amazon Kinesis and Amazon Managed Streaming for Apache Kafka (Amazon MSK). Amazon Kinesis is a massively scalable and durable real-time data streaming service. Amazon MSK offers a secure, fully managed, and highly available Apache Kafka service. You will learn how Amazon Kinesis and Amazon MSK integrate with AWS services such as AWS Glue and AWS Lambda. The course addresses the streaming data ingestion, stream storage, and stream processing components of the data analytics pipeline. You will also learn to apply security, performance, and cost management best practices to the operation of Kinesis and Amazon MSK.

Course objectives

In this course, you will learn to:

• Understand the features and benefits of a modern data architecture. Learn how AWS streaming services fit into a modern data architecture
• Design and implement a streaming data analytics solution
• Identify and apply appropriate techniques, such as compression, sharding, and partitioning, to optimize data storage
• Select and deploy appropriate options to ingest, transform, and store real-time and near real-time data
• Choose the appropriate streams, clusters, topics, scaling approach, 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 streaming data at rest and in transit
• Monitor analytics workloads to identify and remediate problems
• Apply cost management best practices

Intended audience

This course is intended for:

• Data engineers and architects
• Developers who want to build and manage real-time applications and streaming data analytics solutions

Prerequisites

We recommend that attendees of this course have:

• At least one year of data analytics experience or direct experience building real-time applications or streaming analytics solutions. We suggest the Streaming Data Solutions on AWS whitepaper for
  those that need a refresher on streaming concepts.
• Completed either Architecting on AWS or Data Analytics Fundamentals
• Completed Building Data Lakes on AWS

Activities

This course includes:

• presentations
• practice labs
• discussions
• class exercises

Course duration / Price

• 1 Day
• € 750.00 (excl. tax) per person (DE)

Course outline

This course covers the following concepts:

Module A: Overview of Data Analytics and the Data Pipeline

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

Module 1: Using Streaming Services in the Data Analytics Pipeline

• The importance of streaming data analytics
• The streaming data analytics pipeline
• Streaming concepts

Module 2: Introduction to AWS Streaming Services

• Streaming data services in AWS
• Amazon Kinesis in analytics solutions
• Demonstration: Explore Amazon Kinesis Data Streams
• Practice Lab: Setting up a streaming delivery pipeline with Amazon Kinesis
• Using Amazon Kinesis Data Analytics
• Introduction to Amazon MSK
• Overview of Spark Streaming

Module 3: Using Amazon Kinesis for Real-time Data Analytics

• Exploring Amazon Kinesis using a clickstream workload
• Creating Kinesis data and delivery streams
• Demonstration: Understanding producers and consumers
• Building stream producers
• Building stream consumers
• Building and deploying Flink applications in Kinesis Data Analytics
• Demonstration: Explore Zeppelin notebooks for Kinesis Data Analytics
• Practice Lab: Streaming analytics with Amazon Kinesis Data Analytics and Apache Flink

Module 4: Securing, Monitoring, and Optimizing Amazon Kinesis

• Optimize Amazon Kinesis to gain actionable business insights
• Security and monitoring best practices

Module 5: Using Amazon MSK in Streaming Data Analytics Solutions

• Use cases for Amazon MSK
• Creating MSK clusters
• Demonstration: Provisioning an MSK Cluster
• Ingesting data into Amazon MSK
• Practice Lab: Introduction to access control with Amazon MSK
• Transforming and processing in Amazon MSK

Module 6: Securing, Monitoring, and Optimizing Amazon MSK

• Optimizing Amazon MSK
• Demonstration: Scaling up Amazon MSK storage
• Practice Lab: Amazon MSK streaming pipeline and application deployment
• Security and monitoring
• Demonstration: Monitoring an MSK cluster

Module 7: Designing Streaming Data Analytics Solutions

• Use case review
• Class Exercise: Designing a streaming data analytics workflow

Module B: Developing Modern Data Architectures on AWS

• Modern data architectures

IMPORTANT: Please bring your notebook (Windows, Linux or Mac) to our trainings. If this is not possible, please contact us in advance.

Course materials are in English, on request also in German (if available).
Course language is German, on request also in English.

Authoring Visual Analytics Using Amazon QuickSight

current course dates can be found at the bottom of this page … company training available on request!

Course description

In this course, you will build a data visualization solution using Amazon QuickSight. QuickSight allows everyone in your organization to understand your data by exploring through interactive dashboards, asking questions in natural language, or automatically looking for patterns and outliers powered by machine learning. This course focuses on connecting to data sources, building visuals, designing interactivity, and creating calculations. You will learn how to apply security best practices to your analyses. You will also explore the machine learning capabilities built into QuickSight.

Course objectives

In this course, you will learn to:

• Explain the benefits, use cases, and key features of Amazon QuickSight
• Design, create, and customize QuickSight dashboards to visualize data and extract business insights from it
• Select and configure appropriate visualization types to identify, explore, and drill down on business insights
• Describe how to use one-click embed to incorporate analytics into applications
• Connect, transform, and prepare data for dashboarding consumption
• Perform advanced data calculations on QuickSight analyses
• Describe the security mechanisms available for Amazon QuickSight
• Apply fine-grained access control to a dataset
• Implement machine learning on data sets for anomaly detection and forecasting
• Explain the benefits and key features of QuickSight Q to enhance the dashboard user experience

Intended audience

This course is intended for:

• Data and business analysts who build and manage business analytics dashboards

Prerequisites

Students with a minimum one-year experience authoring visual analytics will benefit from this course. We recommend that attendees of this course have:

• Completed Data Analytics Fundamentals

Activities

This course includes:

• presentations
• demonstrations
• group exercises
• practice and challenge labs

Course duration / Price

• 2 days
• € 1,295.00 (excl. tax) per person (DE)

Course outline

This course covers the following concepts:

Day 1:

Module 1: Introduction and Overview of Amazon QuickSight

• Introducing Amazon QuickSight
• Why use Amazon QuickSight for data visualization

Module 2: Getting Started with Amazon QuickSight

• Interacting with Amazon QuickSight
• Loading data into Amazon QuickSight
• Visualizing data in Amazon QuickSight
• Demonstration: Walkthrough of Amazon QuickSight interface
• Practice Lab: Create your first dashboard

Module 3: Enhancing and Adding Interactivity to Your Dashboard

• Enhancing your dashboard
• Demonstration: Optimize the size, layout, and aesthetics of a dashboard
• Enhancing visualizations with interactivity
• Demonstration: Walkthrough of dashboard interactivity features
• Practice Lab: Enhancing your dashboard

Module 4: Preparing Datasets for Analysis

• Working with datasets
• Demonstration: Transform your datasets for analysis
• Practice Lab: Preparing data for analysis

Module 5: Performing Advanced Data Calculations

• Transform data using advanced calculations
• Practice Lab: Performing advanced data calculations
• Activity: Designing a Visual Analytics Solution

Day 2:

Module 6: Overview of Amazon QuickSight Security and Access Control

• Overview of Amazon QuickSight security and access control
• Dataset access control in Amazon QuickSight
• Lab: Implementing access control in Amazon QuickSight visualizations

Module 7: Exploring machine learning capabilities

• Introducing Machine Learning (ML) insights
• Natural Language Query with QuickSight Q
• Demonstration: Using QuickSight Q
• Lab: Using machine learning for anomaly detection and forecasting

End of day challenge labs

• Join data sources together
• Create a dashboard
• Enhance the dashboard and add interactivity
• Perform advanced data calculations
• Integrate machine learning tools into the dashboard

IMPORTANT: Please bring your notebook (Windows, Linux or Mac) to our trainings. If this is not possible, please contact us in advance.

Course materials are in English, on request also in German (if available).
Course language is German, on request also in English.

AWS Security Best Practices

current course dates can be found at the bottom of this page … company training available on request!

Course description

Currently, the average cost of a security breach can be upwards of $4 million. AWS Security Best Practices provides an overview of some of the industry best practices for using AWS security and control types. This course helps you understand your responsibilities while providing valuable guidelines for how to keep your workload safe and secure. You will learn how to secure your network infrastructure using sound design options. You will also learn how you can harden your compute resources and manage them securely. Finally, by understanding AWS monitoring and alerting, you can detect and alert on suspicious events to help you quickly begin the response process in the event of a potential compromise.

Course objectives

In this course, you will learn to:

• Design and implement a secure network infrastructure
• Design and implement compute security
• Design and implement a logging solution

Intended audience

This course is intended for:

• Solutions architects, cloud engineers, including security engineers, delivery and implementation engineers, professional services, and Cloud Center of Excellence (CCOE)

Prerequisites

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

• AWS Security Fundamentals
• AWS Security Essentials

Activities

This course includes:

• Training with instructor
• Practical exercises

Course duration / Price

• 1 day / € 795.00 (excl. tax) per person (DE)

Course outline

This course covers the following concepts:

Module 1: AWS Security Overview

• Shared responsibility model
• Customer challenges
• Frameworks and standards
• Establishing best practices
• Compliance in AWS

Module 2: Securing the Network

• Flexible and secure
• Security inside the Amazon Virtual Private Cloud (Amazon VPC)
• Security services
• Third-party security solutions

Lab 1: Controlling the Network

• Create a three-security zone network infrastructure.
• Implement network segmentation using security groups, Network Access Control Lists (NACLs), and public and private subnets.
• Monitor network traffic to Amazon Elastic Compute Cloud (EC2) instances using VPC flow logs.

Module 3: Amazon EC2 Security

• Compute hardening
• Amazon Elastic Block Store (EBS) encryption
• Secure management and maintenance
• Detecting vulnerabilities
• Using AWS Marketplace

Lab 2: Securing the starting point (EC2)

• Create a custom Amazon Machine Image (AMI).
• Deploy a new EC2 instance from a custom AMI.
• Patch an EC2 instance using AWS Systems Manager.
• Encrypt an EBS volume.
• Understand how EBS encryption works and how it impacts other operations.
• Use security groups to limit traffic between EC2 instances to only that which is encrypted.

Module 4: Monitoring and Alerting

• Logging network traffic
• Logging user and Application Programming Interface (API) traffic
• Visibility with Amazon CloudWatch
• Enhancing monitoring and alerting
• Verifying your AWS environment

Lab 3: Security Monitoring

• Configure an Amazon Linux 2 instance to send log files to Amazon CloudWatch.
• Create Amazon CloudWatch alarms and notifications to monitor for failed login attempts.
• Create Amazon CloudWatch alarms to monitor network traffic through a Network Address Translation (NAT) gateway

IMPORTANT: Please bring your notebook (Windows, Linux or Mac) to our trainings. If this is not possible, please contact us in advance.

Course materials are in English, on request also in German (if available).
Course language is German, on request also in English.

Building Batch Data Analytics Solutions on AWS

current course dates can be found at the bottom of this page … company training available on request!

Course description

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.

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

Intended audience

This course is intended for:

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

Prerequisites

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

Activities

This course includes:

• Training with instructor
• Practical exercises

Course duration / Price

• 1 day / € 795.00 (excl. tax) per person (DE)

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

IMPORTANT: Please bring your notebook (Windows, Linux or Mac) to our trainings. If this is not possible, please contact us in advance.

Course materials are in English, on request also in German (if available).
Course language is German, on request also in English.