Курсы Облачные технологии

This fundamental-level course is intended for individuals who seek an overall understanding of the AWS Cloud, independent of specific technical roles. You will learn about AWS cloud concepts, AWS services, security, architecture, pricing, and support to build your AWS Cloud knowledge. Throughout the day there are hands-on lab exercises to reinforce some of the core concepts of the class. It also helps you prepare for the AWS Certified Cloud Practitioner exam.

In this course, you’ll learn about cloud-based big data solutions like Amazon EMR, Amazon Redshift, Amazon Kinesis, and the rest of the AWS big data platform. Learn to use Amazon EMR to process data using the broad ecosystem of Hadoop tools like Hive and Hue, create big data environments, work with Amazon DynamoDB, Amazon Redshift, Amazon QuickSight, Amazon Athena and Amazon Kinesis, and design big data environments for security and cost-effectiveness.

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.

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 QuickSight to perform analysis on your data.

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.

2-day Specialist course which provides an understanding of the basic concepts and skills necessary to configure and deploy Check Point CloudGuard IaaS products in public cloud platforms such as Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform.

Learn how to use the AWS Software Development Kit to develop secure and scalable cloud applications. You will be able to setup AWS SDK and developer
credentials for Java, C#/.NET, Python, and JavaScript. Explore how to interact with AWS services, and develop solutions by using the AWS SDK. Also learn about key concepts, best practices, and troubleshooting tips.

DevOps Engineering on AWS teaches you how to use the combination of DevOps cultural philosophies, practices, and tools to increase your organization’s ability to develop, deliver, and maintain applications and services at high velocity on AWS. This course covers Continuous Integration (CI), Continuous Delivery (CD), infrastructure as code, microservices, monitoring and logging, and communication and collaboration. Hands-on labs give you experience building and deploying AWS CloudFormation templates and CI/CD pipelines that build and deploy applications on Amazon Elastic Compute Cloud (Amazon EC2), serverless applications, and container-based applications. Labs for multi-pipeline workflows and pipelines that deploy to multiple environments are also included.

This three-day instructor-led course is intended for data professionals who want to design and implement database solutions in SQL Server and on Microsoft Azure.  Security, Availability, Disaster Recovery, Scalability, Monitoring and Troubleshooting options will also be demonstrated.