AWS IoT is a managed cloud platform that lets connected devices easily and securely interact with cloud applications and other devices. As an IoT developer, you will interact with AWS services like Kinesis, Lambda, and Amazon Machine Learning to get the most from your IoT application. In this session, we will do a deep dive on how to define rules in the Rules Engine, or retrieve the last known and desired state of device using Device Shadows, routing data from devices to AWS services to leverage the entire cloud for your Internet of Things application.

There are different methods for managing databases with AWS. Amazon RDS is a managed relational database service that supports MySQL, MariaDB, Oracle, Microsoft SQL Server, and PostgreSQL. This includes Amazon Aurora which, while maintaining compatibility with MySQL, combines the performance and reliability of commercial databases with the simplicity and lower costs of open-source databases. AWS Database Migration Service helps you migrate to the cloud while keeping the source database operational during the migration. With Amazon DynamoDB you have a completely managed NoSQL database that supports documents and key-value stores. Its flexible data model and performance make it ideal for mobile and web applications, gaming, advertising and IoT.

How can we create a scalable system to improve and automate the security of our infrastructure, benefiting from the scalability and simplicity of use of Amazon Web Services? In this session we'll discuss Amazon CloudFront, our global content distribution network, and how it integrates with AWS WAF, a Web Application Firewall, and AWS Lambda to create dynamic rules that automatically protect your infrastructure from malicious attacks. During this session you will also learn how to increase scalability and deal with occasional traffic spikes in a simple and integrated fashion.

Launch your application into production without having to configure and manage servers. Design the backend considering how to react to the interactions your users have with the app and its data. Focus on the important features for your users and the relationships between used resources. Your application is broken down into scalable microservers without having to manage their infrastructure. The resulting architecture is efficient and lowers execution costs by using AWS Lambda and Amazon API Gateway, so that availability, scalability and security are part of the implementation itself.

In this session we'll gain deeper knowledge of how to use AWS Mobile Services to implement scalable and robust multi-platform mobile applications. We'll take a deeper look at fundamental features such as user authentication and authorization, sychronizing data across devices, and managing a serverless backend, understanding user behavior and how to engage them with your app. We'll also see how to test your iOS, Android or Web applications on physical smartphones and tablets using AWS Device Farm. Unlike virtual ones, physical devices allow you to understand more accurately the performance and visual impact that users have from your app.

Launch your application in production without having to configure and manage servers. Design the backend keeping in mind how to react to the interactions your users have with the app and its data. Focus on the key features for your users and the relationships between used resources. Your application is broken down into scalable microservers without having to manage their infrastructure. The resultant architecture is efficient and reduces execution costs by using AWS Lambda and Amazon API Gateway, so that availability, scalability and security are part of the implementation itself.

The expression 'digital transformation' refers to a series of technological, cultural, organizational and social changes. The cloud is one of the engines driving this change and produces tangible benefits for organizations of all sizes. Cloud adoption is a journey that requires a precise strategy to optimize benefits. This session is for anyone interested in key considerations in the technological environment and business processes, providing organizations with the basic tools to build an effective cloud strategy.

In this session we will focus on 'Secure by Design' architectural principles, demonstrating the possible configurations and services available in AWS environments to design and monitor applications securely, from virtual environments to arrive at new serverless paradigms. The session will also demonstrate the principles of complying with the AWS cloud security standards that let customers know the solid control systems put in place by AWS to protect data in the cloud.

In this session, we will illustrate the fundamentals of Amazon Virtual Private Cloud (VPC). First, we will cover the design fundamentals for VPC, for example choosing an IP space, subnetting, routing, security, NAT and much more. We will then illustrate several approaches and use cases for connecting your virtual private cloud to a physical data center via VPN or AWS Direct Connect. This architecture discussion is aimed at architects, network administrators and business technology decision-makers interested in understanding the foundational services offered by AWS to create virtual private clouds and how to connect them to offices and data centers in use.

Many businesses have started to migrate the majority of their existing applications to AWS. These are programs in which customers need to achieve tangible results in a relatively short time. Based on several case studies we will discuss how to lead the transition and focus on the importance of working with multiple parties inside the organization and how to create value with the AWS ecosystem.

Building on the experience of Amazon.com, we have made available the tools that allow for rapid and robust continuous delivery to quickly update your applications. AWS CodePipeline tests and deploys your code every time there is a code change, based on the release process models you define. AWS CodeDeploy automates software deployments, avoiding the risks associated with manual operations and scaling together with your infrastructure. With AWS CodeCommit you can have private, secure, scalable Git repositories for your applications. If you prefer an even simpler solution, upload your code and AWS Elastic Beanstalk will handle the deployment, infrastructure resources, load balancing, auto scaling and application monitoring. With an 'infrastructure as code' approach you can apply software development best practices to managing infrastructure.

Ever more heterogenuous big data are being produced in the world. Beyond the usual batch processing, users and customers all over the world are demanding analysis almost in real time, even at the speed of milliseconds, for a flow of information in constant and rapid movement. AWS offers multiple technologies that can help solve problems related to big data. But what services should be used? When, how, and why? In this session we will simplify the processing of big data as a data bus divided into different stages: acquisition, storage, processing and visualization. Thus we will explain how to choose the most suitable technology for each stage, based on criteria such as data structure, query latency, costs, speed of requests, size of elements, volume of data, durability, etc. Finally we will provide reference architectures, design patterns and best practices for bringing these technologies together and solving problems related to big data at a reasonable cost.

Best practices allow us to get the most out of technology based on columns and parallel processing features to improve query response and overall database performance. In this session we will explain how to migrate from existing data warehouses, create an optimized schema, load data efficiently, manage workloads, and refine queries, using Amazon Redshift's interleaved sorting feature. Finally we will explain how to apply best practices to provide access to the entire company for strategic analysis.  

In this session we will demonstrate best practices for creating a real-time streaming data architecture on AWS with Amazon Kinesis, integrating other tools available on the AWS platform. We will also examine how to introduce data into the stream in a durable, scalable manner, from sources such as mobile devices, servers and web browsers. Using Amazon Kinesis Firehose, you can load streaming data continuously and efficiently in Amazon S3 and Amazon Redshift without implementing custom procedures.

In this presentation, learn how an end-to-end smart application can be built in the AWS cloud. We will demonstrate how to use Amazon Machine Learning (Amazon ML) to create machine learning models, deploy them to production, and obtain predictions in real-time. We will then demonstrate how to build a complete smart application using Amazon ML, Amazon Kinesis, and AWS Lambda. We will walk you through the process flow and architecture, demonstrate outcomes, and then dive into the code for implementation. In this session, you will learn how to use Amazon ML as well as how to integrate Amazon ML into your applications to take advantage of predictive analysis in the cloud.  

Amazon QuickSight is a very fast, cloud-powered business intelligence (BI) service that makes it easy to build visualizations, perform ad hoc analysis and get strategic insights from data. In this session, we will demonstrate how to direct Amazon QuickSight to AWS datastores, flat files and data origins from third parties to begin rendering information in just a few minutes. We'll present the new engine called SPICE (Super-Fast, Parallel, In-memory Calculation Engine) to perform advanced calculations and render visualizations rapidly without requiring infrastructure management, SQL programming or additional dimensional modelling, so that you can resize resources to accomodate hundreds of thousands of users and sustain the load of several petabytes of data. Finally, we will demonstrate how Amazon QuickSight provides intelligent visualization and optimized graphics for different types of data, in order to always ensure the most appropriate visualization for analysis, then sharing this view as a storyboard through interactive collaboration tools.