This certification is intended for candidates who may be:
From a non-IT background and exploring a career in the AWS Cloud
In sales/marketing/business analyst roles looking to communicate more effectively with stakeholders and customers about the AWS Cloud
In on-premises IT or cloud roles, but new to AWS Cloud, and need a primer before diving into role-based AWS Certification(s)
Prerequisites
To earn this certification, you will need to take and pass the AWS Certified Cloud Practitioner exam. The exam features a combination of two question formats: multiple choice and multiple response. Additional information, such as a detailed exam content outline, is in the exam guide.
AWS Certifications are valid for three years. To maintain your AWS Certified status, we require you to periodically demonstrate your continued expertise though a process called recertification. Recertification helps strengthen the overall value of your AWS Certification and shows individuals and employers that your credential covers the latest AWS knowledge, skills, and best practices.
Recertification for Foundational-Certification
Take the “Cloud Practitioner” exam
You can take the current AWS Certified Cloud Practitioner exam for recertification. You can use your 50% discount voucher from the Benefits section of your AWS Certification account to re-certify, or use it for any future certification exams you want to take.
Obtain an Associate or Professional certification
You can fulfill the recertification requirement by taking any Associate or Professional exam.
current course dates can be found at the bottom of this page … company training available on request!
Course description
Are you planning to run your container workloads in a cloud-based Kubernetes environment in the future? Have you recently been tasked with operating an EKS cluster and would now like to acquire the necessary know-how? Are you an experienced DevOps engineer looking to make the transition to the AWS cloud? Then “Operations on Kubernetes” is the right choice!
This course focuses on the operation of Kubernetes clusters and the associated AWS infrastructure. Through a mixture of theory and practice, we will convey content in a lasting manner and offer you the opportunity to put what you have learned into practice immediately.
Course objectives
Brush up on basic Kubernetes concepts
Learn advanced concepts such as RBAC, Ingress Controller, etc.
Familiarize with basic Kubernetes commands
Recognize and analyze key metrics
Identify and rectify disruptions in a systematic manner
Confidently perform Kubernetes cluster upgrades on Amazon EKS
Gain familiarity with common Kubernetes tooling
INTENDED AUDIENCE
This course is intended for:
People who plan to run their Kubernetes workloads on the Elastic Kubernetes Service (EKS) in the future
DevOps Engineers
System/Operations administrators
Prerequisites
We recommend that attendees of this course have:
Basic understanding of general networking concepts and multi-tier architectures
Basic understanding of container runtimes (Docker, containerd, etc.)
Basic understanding of container orchestration (Kubernetes, Docker Swarm, etc.)
Knowledge in the administration of microservices (git, bash, YAML/JSON, common text editors)
(Optional) Basic knowledge of AWS services (VPC, EC2, S3) is an advantage
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.
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.
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:
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