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Building Data Lakes on AWS

Written by wamrhein on . Posted in AWS Training, Training.

Building Data Lakes 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 how to build an operational data lake that supports analysis of both structured and unstructured data. You will learn the components and functionality of the services involved in creating a data lake. You will use AWS Lake Formation to build a data lake, AWS Glue to build a data catalog, and Amazon Athena to analyze data. The course lectures and labs further your learning with the exploration of several common data lake architectures.

Course objectives

In this course, you will learn to:

  • Apply data lake methodologies in planning and designing a data lake
  • Articulate the components and services required for building an AWS data lake
  • Secure a data lake with appropriate permission
  • Ingest, store, and transform data in a data lake
  • Query, analyze, and visualize data within a data lake

Intended audience

This course is intended for:

  • Data platform engineers
  • Solutions architects
  • IT professionals

Prerequisites

We recommend that attendees of this course have:

  • Completed thes AWS Technical Essentials training
  • One year of experience building data analytics pipelines or have completed the Data Analytics Fundamentals course

Activities

This course includes:

  •  presentations
  • lecture
  • hands-on labs,
  • group exercises

Course duration / Price

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

Course outline

Module 1: Introduction to data lakes

  • Describe the value of data lakes
  • Compare data lakes and data warehouses
  • Describe the components of a data lake
  • Recognize common architectures built on data lakes

Module 2: Data ingestion, cataloging, and preparation

  • Describe the relationship between data lake storage and data ingestion
  • Describe AWS Glue crawlers and how they are used to create a data catalog
  • Identify data formatting, partitioning, and compression for efficient storage and query
  • Lab 1: Set up a simple data lake

Module 3: Data processing and analytics

  • Recognize how data processing applies to a data lake
  • Use AWS Glue to process data within a data lake
  • Describe how to use Amazon Athena to analyze data in a data lake

Module 4: Building a data lake with AWS Lake Formation

  • Describe the features and benefits of AWS Lake Formation
  • Use AWS Lake Formation to create a data lake
  • Understand the AWS Lake Formation security model
  • Lab 2: Build a data lake using AWS Lake Formation

Module 5: Additional Lake Formation configurations

  • Automate AWS Lake Formation using blueprints and workflows
  • Apply security and access controls to AWS Lake Formation
  • Match records with AWS Lake Formation FindMatches
  • Visualize data with Amazon QuickSight
  • Lab 3: Automate data lake creation using AWS Lake Formation blueprints
  • Lab 4: Data visualization using Amazon QuickSight

Module 6: Architecture and course review

  • Post course knowledge check
  • Architecture review
  • Course review

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.

Developing Serverless Solutions on AWS

Written by wamrhein on . Posted in AWS Training, Training.

Developing Serverless Solutions on AWS

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

Course description

This course gives developers exposure to and practice with best practices for building serverless applications using AWS Lambda and other services in the AWS serverless platform. You’ll use AWS frameworks to deploy a serverless application in hands-on labs that progress from simpler to more complex topics. You will use AWS documentation throughout the course to develop authentic methods for learning and problem-solving beyond the classroom.

Course objectives

In this course, you will learn to:

  • Apply event-driven best practices to a serverless application design using appropriate AWS services
  • Identify the challenges and trade-offs of transitioning to serverless development, and make recommendations that suit your development organization and environment
  • Build serverless applications using patterns that connect AWS managed services together, and account for service characteristics, including service quotas, available integrations, invocation model, error handling, and event source payload
  • Compare and contrast available options for writing infrastructure as code, including AWS CloudFormation, AWS Amplify, AWS Serverless Application Model (AWS SAM), and AWS Cloud Development Kit (AWS CDK)
  • Apply best practices to writing Lambda functions inclusive of error handling, logging, environmen re-use, using layers, statelessness, idempotency, and configuring concurrency and memory
  • Apply best practices for building observability and monitoring into your serverless application
  • Apply security best practices to serverless applications
  • Identify key scaling considerations in a serverless application, and match each consideration to the methods, tools, or best practices to manage it
  • Use AWS SAM, AWS CDK, and AWS developer tools to configure a CI/CD workflow, and automate deployment of a serverless application
  • Create and actively maintain a list of serverless resources that will assist in your ongoing serverless development and engagement with the serverless community

Intended audience

This course is intended for:

  • Developers who have some familiarity with serverless and experience with development in the AWS Cloud

Prerequisites

We recommend that attendees of this course have:

  • Familiarity with the basics of AWS Cloud architecture
  • An understanding of developing applications on AWS equivalent to completing the “Developing on AWS“
  • Knowledge equivalent to completing the following serverless digital trainings: AWS Lambda Foundations and Amazon API Gateway for Serverless Applications

Activities

This course includes:

  • presentations
  • hands-on labs
  • demonstrations
  • videos
  • knowledge checks
  • group exercises

Course duration / Price

  • 3 days / € 1,995.00 (excl. tax) per person (DE)

Course outline

Day 1

Module 0: Introduction

Module 1: Thinking Serverless

Module 2: API-Driven Development and Synchronous Event Sources

Module 3: Introduction to Authentication, Authorization, and Access Control

Module 4: Serverless Deployment Frameworks

Module 5: Using Amazon EventBridge and Amazon SNS to Decouple Components

Module 6: Event-Driven Development Using Queues and Streams

Hands-On Labs

Day 2

Module 7: Writing Good Lambda Functions

Module 8: Step Functions for Orchestration

Module 9: Observability and Monitoring

Hands-On Labs

Day 3

Module 10: Serverless Application Security

Module 11: Handling Scale in Serverless Applications

Module 12: Automating the Deployment Pipeline

Hands-On Labs

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.

Running Containers on Amazon Elastic Kubernetes Service (Amazon EKS)

Written by wamrhein on . Posted in AWS Training, Training.

Running Containers on Amazon Elastic Kubernetes Service (Amazon EKS)

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

Course description

Amazon EKS makes it easy for you to run Kubernetes on AWS without needing to install, operate, and maintain your own Kubernetes control plane. In this course, you will learn container management and orchestration for Kubernetes using Amazon EKS.

You will build an Amazon EKS cluster, configure the environment, deploy the cluster, and then add applications to your cluster. You will manage container images using Amazon Elastic Container Registry (ECR) and learn how to automate application deployment. You will deploy applications using CI/CD tools. You will learn how to monitor and scale your environment by using metrics, logging, tracing, and horizontal/vertical scaling. You will learn how to design and manage a large container environment by designing for efficiency, cost, and resiliency. You will configure AWS networking services to support the cluster and learn how to secure your Amazon EKS environment.

Course objectives

In this course, you will learn to:

  • Describe Kubernetes and Amazon EKS fundamentals and the impact of containers on workflows.
  • Build an Amazon EKS cluster by selecting the correct compute resources to support worker nodes.
  • Secure your environment with AWS Identity and Access Management (IAM) authentication and Kubernetes Role Based Access Control (RBAC) authorization.
  • Deploy an application on the cluster. Publish container images to Amazon ECR and secure access via IAM policy.
  • Deploy applications using automated tools and pipelines. Create a GitOps pipeline using WeaveFlux.
  • Collect monitoring data through metrics, logs, and tracing with AWS X-Ray and identify metrics for performance tuning. Review scenarios where bottlenecks require the best scaling approach using horizontal or vertical scaling.
  • Assess the tradeoffs between efficiency, resiliency, and cost and the impact of tuning for one over the others. Describe and outline a holistic, iterative approach to optimizing your environment. Design for cost, efficiency, and resiliency.
  • Configure AWS networking services to support the cluster. Describe how Amazon Virtual Private Cloud (VPC) supports Amazon EKS clusters and simplifies inter-node communications. Describe the function of the VPC Container Network Interface (CNI). Review the benefits of a service mesh.
  • Upgrade your Kubernetes, Amazon EKS, and third party tools.

Intended audience

This course is intended for:

  • people who provide container orchestration management in the AWS Cloud including:
    • DevOps engineers
    • Systems administrators

Prerequisites

We recommend that attendees of this course have:

  • Completed Amazon Elastic Kubernetes Service (EKS) Primer
  • Completed AWS Cloud Practitioner Essentials (or equivalent real-world experience)
  • Basic Linux administration experience
  • Basic network administration experience
  • Basic knowledge of containers and microservices

Activities

This course includes:

  • Training with instructor
  • Practical exercises

Course duration / Price

  • 3 days
  • € 2,685.00 (excl. tax) per person (DE)
  • CHF 2,500.00 (excl. tax) per person (CH)

Course outline

Day 1:

  • Module 0: Course Introduction
  • Module 1: Kubernetes Fundamentals
  • Hands-On Lab 1: Deploying Kubernetes Pods
  • Module 2: Amazon EKS Fundamentals
  • Module 3: Building an Amazon EKS Cluster
  • Hands-On Lab 2: Building an Amazon EKS cluster

Day 2:

  • Module 4: Deploying Applications to Your Amazon EKS Cluster
  • Hands-On Lab 3: Deploying applications
  • Module 5: Architecting on Amazon EKS Part 1: Observe and Optimize
  • Hands-On Lab 4: Monitoring Amazon EKS
  • Module 6: Architecting on Amazon EKS Part 2: Balancing Efficiency, Resiliency, and Cost

Day 3:

  • Module 7: Managing Networking in Amazon EKS
  • Hands-On Lab 5: Exploring Amazon EKS Communication
  • Module 8: Securing Amazon EKS Clusters
  • Hands-On Lab 6: Securing Amazon EKS
  • Module 9: Managing Upgrades in Amazon EKS

 

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.

Practical Data Science with Amazon SageMaker

Written by wamrhein on . Posted in AWS Training, Training.

Practical Data Science with Amazon SageMaker

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

Course description

In this course, learn how to solve a real-world use case with machine learning and produce actionable results using Amazon SageMaker. This course teaches you how to use Amazon SageMaker to cover the different stages of the typical data science process, from analyzing and visualizing a data set, to preparing the data and feature engineering, down to the practical aspects of model building, training, tuning and deployment.

Course objectives

In this course, you will learn to:

  • Prepare a dataset for training.
  • Train and evaluate a machine learning model.
  • Automatically tune a machine learning model.
  • Prepare a machine learning model for production.
  • Think critically about machine learning model results.

Intended audience

This course is intended for:

  • A technical audience at an intermediate level

Prerequisites

We recommend that attendees of this course have:

  • Working knowledge of a programming language

Activities

This course includes:

  • Training with instructor
  • Practical exercises

Course duration / Price

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

Course outline

  • Business problem: Churn prediction
  • Load and display the dataset
  • Assess features and determine which Amazon SageMaker algorithm to use
  • Use Amazon Sagemaker to train, evaluate, and automatically tune the model
  • Deploy the model
  • Assess relative cost of errors

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 Cloud Practitioner Essentials Training

Written by wamrhein on . Posted in AWS Training, Training.

AWS Cloud Practitioner Essentials

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

Course description

This fundamental-level, one-day, instructor-led classroom course is intended for individuals who seek an overall understanding of the AWS Cloud, independent of specific technical roles. It provides a detailed overview of cloud concepts, AWS services, security, architecture, pricing, and support to enable you to build your cloud skills and grow your professional credibility. It includes hands-on lab exercises reinforcing some of the core concepts of the course’s lecture. This course also helps you prepare you for the AWS Certified Cloud Practitioner exam.

Course objectives

In this course, you will learn to:

  • Define what the cloud is and how it works
  • Differentiate between cloud computing and deployment models
  • Describe the AWS Cloud value proposition
  • Describe the basic global infrastructure of the cloud
  • Compare the different methods of interacting with AWS
  • Describe and differentiate between AWS service domains
  • Given a scenario, identify an appropriate solution using AWS Cloud services
  • Describe the Well-Architected Framework
  • Describe basic AWS Cloud architectural principles
  • Explain the Shared Responsibility model
  • Describe security services with the AWS cloud
  • Define the billing, account management, and pricing models for the AWS platform
  • Identify future services and developments built on the cloud

Intended audience

This course is intended for:

  • Sales
  • Legal
  • Marketing
  • Business analysts
  • Project managers
  • AWS Academy Students
  • IT-related professionals

Course duration / Price

  • 1 day
  • € 750.00 (excl. tax) per person (DE)
  • CHF 900.00 (excl. tax) per person (CH)

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.