AWS

Security is aconcern for both customers in the cloud, and those considering cloud adoption. An increase in cyberattacks and data leaks remainstop of mind for most industry personnel. The Security Engineering on AWS course addresses these concerns by helping youbetter understand how to interact and build withAmazon Web Services (AWS)in a secure way. In this course, you will learn about managing identities and roles, managing and provisioning accounts, and monitoring API activity for anomalies. You will also learn about how to protect datastored on AWS. The course explores how you can generate, collect, and monitor logsto help identifysecurity incidents. Finally, you will review detecting and investigating security incidents with AWS services.

The AWS Certified Database – Specialty exam validates technical skills and experience in designing, deploying, and managing AWS database services. This certification & training course helps you prepare for the exam by exploring the exam’s topic areas and familiarizing you with the question style and exam approach. The course reviews sample exam questions in each topic area and teaches you how to interpret the concepts being tested so you can more easily eliminate incorrect responses.

This course is for individuals who seek an understanding of how to plan and migrate existing workloads to the AWS Cloud. You will learn about various cloud migration strategies and how to apply each step of the migration process, including portfolio discovery, application migration planning and design, conducting a migration, and post-migration validation and application optimization. Hands-on labs reinforce learning, and each lab is designed to provide you with the understanding and foundation necessary to complete migration tasks in your organization

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.

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.

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.

In this course, you will build a data analytics solution using Amazon Redshift, a cloud data warehouse service. The course focuses on the data collection, ingestion, cataloging, storage, and processing components of the analytics pipeline. You will learn to integrate Amazon Redshift with a data lake 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 Redshift.

You will learn how to solve a real-world use case with Machine Learning (ML) and produce actionable results using Amazon SageMaker. This course walks through the stages of a typical data science process for Machine Learning from analyzing and visualizing a dataset to preparing the data, and feature engineering. Individuals will also learn practical aspects of model building, training, tuning, and deployment with Amazon SageMaker. Real life use case includes customer retention analysis to inform customer loyalty programs

In this course, you’ll learn about AWS’s deep learning solutions, including scenarios where deep learning makes sense and how deep learning works. You’ll learn how to run deep learning models on the cloud using Amazon SageMaker and the MXNet framework. You’ll also learn to deploy your deep learning models using services like AWS Lambda while designing intelligent systems on AWS.

Data Warehousing on AWS introduces you to concepts, strategies, and best practices for designing a cloud-based data warehousing solution using Amazon Redshift, the petabyte-scale data warehouse in AWS. This course demonstrates how to collect, store, and prepare data for the data warehouse by using AWS services such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis, and Amazon S3. Additionally, this course demonstrates how to use Amazon Quick Sight to perform analysis on your data.

Duration: 1.0 day The AWS Certified Data Analytics – Specialty exam validates technical skills and experience in designing and implementing AWS services to derive value from data. This course is intended for Individuals with a Cloud Practitioner or Associate-level AWS certification and two or more years of experience performing complex big data analysis. The course helps you prepare for the exam by taking a deep dive into several data-driven use cases.

Duration: 3.0 days This course teaches systems operators, and anyone performing cloud operations functions how to manage and operate automatable and repeatable deployments of networks and systems on AWS. You will learn about cloud operations functions, such as installing, configuring, automating, monitoring, securing, maintaining, and troubleshooting these services, networks, and systems. The course also covers specific AWS features, tools, and best practices related to these functions.

Amazon SageMaker Studio helps data scientists prepare, build, train, deploy, and monitor machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML. This course prepares experienced data scientists to use the tools that are part of SageMaker Studio to improve productivity at every step of the ML lifecycle.

Architecting on AWS – Accelerator is designed for audiences who can learn and understand new information at a rapid pace. This course covers various aspects of how to architect for the cloud over 5 days. It covers topics from the Architecting on AWS and Advanced Architecting on AWS courses to offer a consolidated course in cloud architecture. Architectural solutions differ depending on the industry, types of applications, and business size. AWS Authorized Instructors emphasize best practices using the AWS Well-Architected Framework and guide you through the process of designing optimal IT solutions based on real-life scenarios. The modules focus on computing, storage, database, networking, security, monitoring, automation, containers, serverless architecture, edge services, and backup and recovery. They also focus on optimization, the benefits of loose coupling applications and serverless components, building resilience, and understanding costs. Using hands-on labs, you will apply knowledge from lectures to gain skills.

This course teaches systems operators and anyone performing system operations functions how to install, configure, automate, monitor, secure, maintain, and troubleshoot the services, networks, and systems on AWS necessary to support business applications. The course also covers specific AWS features, tools, and best practices related to these functions. The final day is an AWS Jam, a gamified event, with teams competing to score points by completing a series of challenges according to established best practices based on concepts covered in the course. You get to experience a wide range of AWS services in a series of real-world scenarios that represent common operational and troubleshooting tasks. The end result is developing, enhancing, and validating your skillsets in the AWS Cloud through real-world problem-solving, exploring new services, and features, and understanding how they interoperate.  

In this course, each module presents a scenario with an architectural challenge to be solved. You will examine available AWS services and features as solutions to the problem. You will gain insights by participating in problem-based discussions and learning about the AWS services that you could apply to meet the challenges. Over 3 days, the course goes beyond the basics of cloud infrastructure and covers topics to meet a variety of needs for AWS customers. Course modules focus on managing multiple AWS accounts, hybrid connectivity and devices, networking with a focus on AWS Transit Gateway connectivity, container services, automation tools for continuous integration/continuous delivery (CI/CD), security, and distributed denial of service (DDoS) protection, data lakes and data stores, edge services, migration options, and managing costs. The course concludes by presenting you with scenarios and challenging you to identify the best solutions.

This course teaches experienced developers how to programmatically interact with AWS services to build web solutions. It guides you through a high-level architectural discussion on resource selection and dives deep into using the AWS Software Development Kits (AWS SDKs) and Command Line Interface (AWS CLI) to build and deploy your cloud applications. You will build a sample application during this course, learning how to set up permissions to the development environment, adding business logic to process data using AWS core services, configure user authentications, deploy to AWS cloud, and debug to resolve application issues. The course includes code examples to help you implement the design patterns and solutions discussed in the course. The labs reinforce key course content and help you to implement solutions using the AWS SDK for Python, .Net, and Java, the AWS CLI, and the AWS Management Console.  

Architecting on AWS is for solutions architects, solution-design engineers, and developers seeking an understanding of AWS architecting. In this course, you will learn to identify services and features to build resilient, secure, and highly available IT solutions on the AWS Cloud. Architectural solutions differ depending on industry, types of applications, and business size. AWS Authorized Instructors emphasize best practices using the AWS Well-Architected Framework, and guide you through the process of designing optimal IT solutions based on real-life scenarios. The modules focus on account security, networking, compute, storage, databases, monitoring, automation, containers, serverless architecture, edge services, and backup and recovery. At the end of the course, you will practice building a solution and apply what you have learned.