Learning Objectives:
-Learn how to automatically discover, catalog, and prepare your data for analytics
-Understand how to query data in your data lake without having to transform or load the data into your data warehouse
-See how to analyze data in both your data lake and data warehouse
SRV205 Architectures and Strategies for Building Modern Applications on AWSAmazon Web Services
Rapid growth of technology and tooling in the cloud has enabled us to build modern applications that are more secure, scalable, and focused on our business. In this session, we cover the key compute primitives that enable us to accelerate towards building and running modern, cloud-native applications. We highlight what we’ve learned from customers running applications with AWS Lambda and AWS Fargate, two modern compute technologies for running applications in the cloud. In addition, we cover architecture patterns of modern application, key primitives required for building modern systems, steps you can take to start building and monitoring modern applications today, and secrets to fearlessly going faster and farther in the cloud.
SRV327 Replicate, Analyze, and Visualize Data Using Managed Database and Ser...Amazon Web Services
If you have disparate datasets within your data center and on AWS, it can be challenging to manage all of them while you extract and analyze data to drive positive business outcomes. In this workshop, we use AWS managed database services, migration tools, and serverless technologies to replicate, analyze, and visualize data. We replicate an on-premises database to Amazon RDS and Amazon S3 using AWS Database Migration Service and the AWS Schema Conversion Tool. We then use Amazon Athena to interactively analyze data using SQL, and finally, we use Amazon QuickSight to visualize the data to enable better business decisions. For this session, ensure that you have an AWS account set up, and familiarize yourself with the AWS Management Console at least a day before the workshop. You don't need any credit on the account.
SRV309 AWS Purpose-Built Database Strategy: The Right Tool for the Right JobAmazon Web Services
In this session, Shawn Bice, VP of NoSQL and QuickSight, covers the AWS purpose-built strategy for databases and explains why your application should drive the requirements of a database, not the other way around. We introduce AWS databases that are purpose-built for your application use cases. Learn why you should select different data services to solve different aspects of an application, and watch a demonstration on which application use cases lend themselves well to which data services. If you’re a developer building modern applications that require flexibility and consistent millisecond performance, and you’re trying to determine what relational and non-relational data services to use, this session is for you.
Achieving Business Value with AWS - AWS Online Tech TalksAmazon Web Services
Learning Objectives:
- Learn about the business benefits of moving to the AWS platform
- Learn what the commercial levers are that can help you lower your TCO on AWS
- Discover how other enterprises have used these levers
How to Build a Data Lake in Amazon S3 & Amazon Glacier - AWS Online Tech TalksAmazon Web Services
Learning Objectives:
- Understand the options for building an analytics platform that leverages Amazon S3 & Amazon Glacier
- Learn about the key considerations for ETL and other core analytics functions
- Determine if query-in-place capabilities like Amazon S3 Select, Amazon Glacier Select, Amazon Athena, and Amazon Redshift Spectrum are a good fit for your use case
Building Serverless Analytics Solutions with Amazon QuickSight (ANT391) - AWS...Amazon Web Services
Querying and analyzing big data can be complicated and expensive. It requires you to setup and manage databases, data warehouses, and business intelligence (BI) applications—all of which require time, effort, and resources. Using Amazon Athena and Amazon QuickSight, you can avoid the cost and complexity by creating a fast, scalable, and serverless cloud analytics solution without the need to invest in databases, data warehouses, complex ETL solutions, and BI applications. In this session, we demonstrate how you can build a serverless big data analytics solution using Amazon Athena and Amazon QuickSight.
Architecting ASP.NET Core Microservices Applications on AWS (WIN401) - AWS re...Amazon Web Services
In this session, learn how to architect, configure, and deploy an ASP.NET Core microservices application running in containerized AWS Fargate tasks. We cover how to use Amazon DynamoDB for session state and how to use Amazon Cognito for identity management. We also discuss using Amazon ECS for service discovery and AWS CodePipeline to create CI/CD pipelines for each microservice so that each one is individually deployed when an AWS CodeCommit repository is updated. Join us, and learn everything you need to know to start designing and deploying containerized ASP.NET Core applications on AWS.
SRV205 Architectures and Strategies for Building Modern Applications on AWSAmazon Web Services
Rapid growth of technology and tooling in the cloud has enabled us to build modern applications that are more secure, scalable, and focused on our business. In this session, we cover the key compute primitives that enable us to accelerate towards building and running modern, cloud-native applications. We highlight what we’ve learned from customers running applications with AWS Lambda and AWS Fargate, two modern compute technologies for running applications in the cloud. In addition, we cover architecture patterns of modern application, key primitives required for building modern systems, steps you can take to start building and monitoring modern applications today, and secrets to fearlessly going faster and farther in the cloud.
SRV327 Replicate, Analyze, and Visualize Data Using Managed Database and Ser...Amazon Web Services
If you have disparate datasets within your data center and on AWS, it can be challenging to manage all of them while you extract and analyze data to drive positive business outcomes. In this workshop, we use AWS managed database services, migration tools, and serverless technologies to replicate, analyze, and visualize data. We replicate an on-premises database to Amazon RDS and Amazon S3 using AWS Database Migration Service and the AWS Schema Conversion Tool. We then use Amazon Athena to interactively analyze data using SQL, and finally, we use Amazon QuickSight to visualize the data to enable better business decisions. For this session, ensure that you have an AWS account set up, and familiarize yourself with the AWS Management Console at least a day before the workshop. You don't need any credit on the account.
SRV309 AWS Purpose-Built Database Strategy: The Right Tool for the Right JobAmazon Web Services
In this session, Shawn Bice, VP of NoSQL and QuickSight, covers the AWS purpose-built strategy for databases and explains why your application should drive the requirements of a database, not the other way around. We introduce AWS databases that are purpose-built for your application use cases. Learn why you should select different data services to solve different aspects of an application, and watch a demonstration on which application use cases lend themselves well to which data services. If you’re a developer building modern applications that require flexibility and consistent millisecond performance, and you’re trying to determine what relational and non-relational data services to use, this session is for you.
Achieving Business Value with AWS - AWS Online Tech TalksAmazon Web Services
Learning Objectives:
- Learn about the business benefits of moving to the AWS platform
- Learn what the commercial levers are that can help you lower your TCO on AWS
- Discover how other enterprises have used these levers
How to Build a Data Lake in Amazon S3 & Amazon Glacier - AWS Online Tech TalksAmazon Web Services
Learning Objectives:
- Understand the options for building an analytics platform that leverages Amazon S3 & Amazon Glacier
- Learn about the key considerations for ETL and other core analytics functions
- Determine if query-in-place capabilities like Amazon S3 Select, Amazon Glacier Select, Amazon Athena, and Amazon Redshift Spectrum are a good fit for your use case
Building Serverless Analytics Solutions with Amazon QuickSight (ANT391) - AWS...Amazon Web Services
Querying and analyzing big data can be complicated and expensive. It requires you to setup and manage databases, data warehouses, and business intelligence (BI) applications—all of which require time, effort, and resources. Using Amazon Athena and Amazon QuickSight, you can avoid the cost and complexity by creating a fast, scalable, and serverless cloud analytics solution without the need to invest in databases, data warehouses, complex ETL solutions, and BI applications. In this session, we demonstrate how you can build a serverless big data analytics solution using Amazon Athena and Amazon QuickSight.
Architecting ASP.NET Core Microservices Applications on AWS (WIN401) - AWS re...Amazon Web Services
In this session, learn how to architect, configure, and deploy an ASP.NET Core microservices application running in containerized AWS Fargate tasks. We cover how to use Amazon DynamoDB for session state and how to use Amazon Cognito for identity management. We also discuss using Amazon ECS for service discovery and AWS CodePipeline to create CI/CD pipelines for each microservice so that each one is individually deployed when an AWS CodeCommit repository is updated. Join us, and learn everything you need to know to start designing and deploying containerized ASP.NET Core applications on AWS.
Simplifying Microsoft Architectures with AWS Services (WIN306) - AWS re:Inven...Amazon Web Services
In this session, learn how to architect Microsoft solutions on AWS for both high availability and scalability. Discover how Microsoft solutions can leverage AWS services to achieve more resiliency, replace unnecessary complexity, and provide scalability. We explore hybrid architecture scenarios and common architecture patterns for Microsoft Active Directory and productivity solutions, such as Dynamics AX, CRM, and SharePoint. We also cover common design patterns for .NET applications, including approaches to CI/CD, DevOps, and containerizing .NET applications.
SID304 Threat Detection and Remediation with Amazon GuardDutyAmazon Web Services
Join us for this hands-on lab where you learn about Amazon GuardDuty by walking through some real-world threat scenarios. Learn about the threat detection capabilities of GuardDuty and the available remediation options. We first go through a scenario where an Amazon EC2 instance is compromised, followed by one where IAM credentials are compromised. In each scenario, we look at a method to remediate the threat. We use the following services: AWS CloudFormation, AWS CloudTrail, Amazon VPC flow logs, Amazon CloudWatch events, Amazon SNS, Amazon S3, AWS Lambda, and, of course, Amazon GuardDuty. Be sure you have an AWS account to do the lab. This should be your own personal account and not an account through your company. We provide AWS credits to help cover any costs incurred during the lab.
Building Your First Serverless Data Lake (ANT356-R1) - AWS re:Invent 2018Amazon Web Services
In this session, you have the opportunity to learn the fundamental building blocks of a data lake on AWS. You design and build a serverless pipeline to ingest, process, optimize and query data in your very own data lake. We discuss different optimizations and best practices to tune your architecture for future growth.
Hands-On: Building a Migration Strategy for SQL Server on AWS (WIN310) - AWS ...Amazon Web Services
In this workshop, we discuss how to migrate SQL Server databases to AWS. Following a short presentation, attendees have the opportunity to choose a hands-on walkthrough to migrate a SQL Server database from an on-premises environment to SQL Server on Amazon EC2 or SQL Server on Amazon RDS. Bring a laptop for the hands-on exercises.
The Future of Enterprise Applications is Serverless (ENT314-R1) - AWS re:Inve...Amazon Web Services
At re:Invent 2014, we announced AWS Lambda and ushered in a whole new world of application design, one without the need to manage or think about traditional server infrastructure. Since then, serverless has become one of the hottest topics in the industry. Customers like Capital One and Coca Cola talk about how serverless saved them time and money, helped them reduce their operational burden, and drove developer agility and innovation. What is serverless, and what are the key trends you should be aware of? Where does one start on the journey of building serverless applications? We cover all of this and more in this session.
SRV307 Applying AWS Purpose-Built Database Strategy: Match Your Workload to ...Amazon Web Services
In this session, Tony Petrossian, director of engineering, AWS Database Services, dives deep into what databases to use for which components of your application. Learn how to evaluate a new workload for the best managed database option based on specific application needs related to data shape, data size at limit, computational requirements, programmability, throughput and latency needs, etc. This session explains the ideal use cases for relational and non-relational database services, including Amazon Aurora, Amazon DynamoDB, Amazon ElastiCache for Redis, Amazon Neptune, and Amazon Redshift.
VMware Cloud on AWS enables you to migrate existing workloads to the AWS Cloud quickly by using tools you are already familiar with. VMware Cloud on AWS brings VMware’s enterprise class Software-Defined Data Center software to Amazon’s public cloud, delivered as an on-demand, elastically scalable, cloud-based solution. Sold and operated by VMware, the solution enables customers to use a common set of software and tools to manage both their AWS-based and on-premises vSphere resources consistently. This session uses practical, real-world customer deployment examples to dive deep on hybrid cloud network connectivity, data protection best practices, and AWS native service integrations.
Optimize Your SaaS Offering with Serverless Microservices (GPSTEC405) - AWS r...Amazon Web Services
In this hands-on session, we crack open the IDE and transform a SaaS web app comprised of several monolithic single-tenant environments into an efficient, scalable, and secure multi-tenant SaaS platform using ReactJS and NodeJS serverless microservices. We use Amazon API Gateway and Amazon Cognito to simplify the operation and security of the service’s API and identity functionality. We enforce tenant isolation and data partitioning with OIDC’s JWT tokens. We leverage AWS SAM and AWS Amplify to simplify authoring, testing, debugging, and deploying serverless microservices, keeping operational burden to a minimum, maximizing developer productivity, and maintaining a great developer experience.
AWS Fargate makes running containerized workloads on AWS easier than ever before. In this session, we provide a technical foundation for using AWS Fargate with your existing containerized services, including best practices for building images, configuring task definitions, task networking, secrets management, and monitoring.
In this popular session, discover how Amazon EBS can take your application deployments on Amazon EC2 to the next level. Learn about Amazon EBS features and benefits, how to identify applications that are appropriate for use with Amazon EBS, best practices, and details about its performance and volume types. The target audience is storage administrators, application developers, applications owners, and anyone who wants to understand how to optimize performance for Amazon EC2 using the power of Amazon EBS.
Learning Objectives:
-Understand how to use a graph model and query languages to build applications over highly connected data
-Understand how the features of Amazon Neptune enable you to build production ready graph applications -Learn how to get started
Authentication & Authorization in GraphQL with AWS AppSync (MOB402) - AWS re:...Amazon Web Services
Modern apps require special consideration for the security and privacy of user data, especially in today’s compliance-driven world. In this session, we provide some of the common use cases and design patterns to secure user data in a globally available GraphQL API, and discuss best practices for authentication and authorization in AWS AppSync.
AWS Fargate makes running containerized workloads on AWS easier than ever. In this session, we provide a technical foundation for using AWS Fargate with your existing containerized services. We also provide best practices for building images, configuring task definitions, task networking, secrets management, and monitoring.
Building Serverless Applications with Amazon DynamoDB & AWS Lambda - Workshop...Amazon Web Services
Join us for this first-ever advanced design and best practices workshop, designed to demonstrate the breadth of AWS serverless offerings and how the components work together. In this interactive workshop, we review the evolution of an e-commerce company that starts with a low-effort serverless product catalog, scales to a million daily users, and then adds analytics and near real-time monitoring. As we progress through the workshop, we dive deeply into AWS serverless services, such as Amazon DynamoDB, AWS Lambda, and Amazon Kinesis. We also use Amazon S3, Amazon API Gateway, Amazon Cognito, and other services that enable you to optimize costs and improve performance. Basic knowledge of DynamoDB, Lambda, and Kinesis is required. Bring your laptop and power supply to this session.
BDA308 Deep Dive: Log Analytics with Amazon Elasticsearch ServiceAmazon Web Services
Amazon Elasticsearch Service makes it easy to deploy, secure, operate, and scale Elasticsearch for log analytics, full text search, application monitoring, and more. In this session you learn how to configure a secure, petabyte-scale Amazon Elasticsearch Service cluster and build Kibana dashboards to analyze your data. In addition, we discuss best practices to make your cluster reliable, take backups, and debug slow-running queries and indexing operations.
Best Practices to Secure Data Lake on AWS (ANT327) - AWS re:Invent 2018Amazon Web Services
As customers are looking to build Data lakes to AWS, managing security, catalog and data quality becomes a challenge. Once data is put on Amazon S3, there are multiple processing engines to access it. This could be either through a SQL interface, programmatic, or using API. Customers require federated access to their data with strong controls around Authentication, Authorization, Encryption, and Audit. In this session, we explore the major AWS analytics services and platforms that customers can use to access data in the data Lake and provide best practices on securing them.
This session provides IT pros and application owners an overview of AWS options for building hybrid storage architectures or even entirely migrating datacenter storage to the AWS cloud. The AWS Storage Gateway connects existing on-premises block, file or tape storage systems to AWS cloud storage over the WAN in a hybrid model. The AWS Snow family of physical devices can capture, pre-process and migrate data into and out of AWS without any network connection at all. Join us to learn how you can close down datacenters, reduce storage footprints, and build solutions for tiering, data lakes, backup, disaster recovery, and migration.
Build a Multi-Region Serverless Application for Resilience & High Availabilit...Amazon Web Services
Do you have a mission-critical serverless app that requires maximum uptime? Come learn how to build and deploy a multi-region serverless application to maximize application availability and resilience. In this workshop, you enter a scenario in which you help a fictional unicorn ridesharing company, Wild Rydes (www.wildrydes.com), deploy a critical customer support application using a serverless architecture. When a passenger completes a ride, they can use the app to inform the company if they had any issues with their trip—perhaps a lost wallet or a misbehaving unicorn. Since Wild Rydes is global, this support application takes advantage of a multi-region, highly available architecture using services such as AWS Lambda, Amazon API Gateway, Amazon DynamoDB, Amazon Route 53, Amazon CloudFront, and Amazon S3 to maximize availability. It also uses Amazon Cognito federated identities for user authentication. Attendees should bring a laptop and be familiar with the AWS Management Console and the AWS CLI.
Simplifying Microsoft Architectures with AWS Services (WIN306) - AWS re:Inven...Amazon Web Services
In this session, learn how to architect Microsoft solutions on AWS for both high availability and scalability. Discover how Microsoft solutions can leverage AWS services to achieve more resiliency, replace unnecessary complexity, and provide scalability. We explore hybrid architecture scenarios and common architecture patterns for Microsoft Active Directory and productivity solutions, such as Dynamics AX, CRM, and SharePoint. We also cover common design patterns for .NET applications, including approaches to CI/CD, DevOps, and containerizing .NET applications.
SID304 Threat Detection and Remediation with Amazon GuardDutyAmazon Web Services
Join us for this hands-on lab where you learn about Amazon GuardDuty by walking through some real-world threat scenarios. Learn about the threat detection capabilities of GuardDuty and the available remediation options. We first go through a scenario where an Amazon EC2 instance is compromised, followed by one where IAM credentials are compromised. In each scenario, we look at a method to remediate the threat. We use the following services: AWS CloudFormation, AWS CloudTrail, Amazon VPC flow logs, Amazon CloudWatch events, Amazon SNS, Amazon S3, AWS Lambda, and, of course, Amazon GuardDuty. Be sure you have an AWS account to do the lab. This should be your own personal account and not an account through your company. We provide AWS credits to help cover any costs incurred during the lab.
Building Your First Serverless Data Lake (ANT356-R1) - AWS re:Invent 2018Amazon Web Services
In this session, you have the opportunity to learn the fundamental building blocks of a data lake on AWS. You design and build a serverless pipeline to ingest, process, optimize and query data in your very own data lake. We discuss different optimizations and best practices to tune your architecture for future growth.
Hands-On: Building a Migration Strategy for SQL Server on AWS (WIN310) - AWS ...Amazon Web Services
In this workshop, we discuss how to migrate SQL Server databases to AWS. Following a short presentation, attendees have the opportunity to choose a hands-on walkthrough to migrate a SQL Server database from an on-premises environment to SQL Server on Amazon EC2 or SQL Server on Amazon RDS. Bring a laptop for the hands-on exercises.
The Future of Enterprise Applications is Serverless (ENT314-R1) - AWS re:Inve...Amazon Web Services
At re:Invent 2014, we announced AWS Lambda and ushered in a whole new world of application design, one without the need to manage or think about traditional server infrastructure. Since then, serverless has become one of the hottest topics in the industry. Customers like Capital One and Coca Cola talk about how serverless saved them time and money, helped them reduce their operational burden, and drove developer agility and innovation. What is serverless, and what are the key trends you should be aware of? Where does one start on the journey of building serverless applications? We cover all of this and more in this session.
SRV307 Applying AWS Purpose-Built Database Strategy: Match Your Workload to ...Amazon Web Services
In this session, Tony Petrossian, director of engineering, AWS Database Services, dives deep into what databases to use for which components of your application. Learn how to evaluate a new workload for the best managed database option based on specific application needs related to data shape, data size at limit, computational requirements, programmability, throughput and latency needs, etc. This session explains the ideal use cases for relational and non-relational database services, including Amazon Aurora, Amazon DynamoDB, Amazon ElastiCache for Redis, Amazon Neptune, and Amazon Redshift.
VMware Cloud on AWS enables you to migrate existing workloads to the AWS Cloud quickly by using tools you are already familiar with. VMware Cloud on AWS brings VMware’s enterprise class Software-Defined Data Center software to Amazon’s public cloud, delivered as an on-demand, elastically scalable, cloud-based solution. Sold and operated by VMware, the solution enables customers to use a common set of software and tools to manage both their AWS-based and on-premises vSphere resources consistently. This session uses practical, real-world customer deployment examples to dive deep on hybrid cloud network connectivity, data protection best practices, and AWS native service integrations.
Optimize Your SaaS Offering with Serverless Microservices (GPSTEC405) - AWS r...Amazon Web Services
In this hands-on session, we crack open the IDE and transform a SaaS web app comprised of several monolithic single-tenant environments into an efficient, scalable, and secure multi-tenant SaaS platform using ReactJS and NodeJS serverless microservices. We use Amazon API Gateway and Amazon Cognito to simplify the operation and security of the service’s API and identity functionality. We enforce tenant isolation and data partitioning with OIDC’s JWT tokens. We leverage AWS SAM and AWS Amplify to simplify authoring, testing, debugging, and deploying serverless microservices, keeping operational burden to a minimum, maximizing developer productivity, and maintaining a great developer experience.
AWS Fargate makes running containerized workloads on AWS easier than ever before. In this session, we provide a technical foundation for using AWS Fargate with your existing containerized services, including best practices for building images, configuring task definitions, task networking, secrets management, and monitoring.
In this popular session, discover how Amazon EBS can take your application deployments on Amazon EC2 to the next level. Learn about Amazon EBS features and benefits, how to identify applications that are appropriate for use with Amazon EBS, best practices, and details about its performance and volume types. The target audience is storage administrators, application developers, applications owners, and anyone who wants to understand how to optimize performance for Amazon EC2 using the power of Amazon EBS.
Learning Objectives:
-Understand how to use a graph model and query languages to build applications over highly connected data
-Understand how the features of Amazon Neptune enable you to build production ready graph applications -Learn how to get started
Authentication & Authorization in GraphQL with AWS AppSync (MOB402) - AWS re:...Amazon Web Services
Modern apps require special consideration for the security and privacy of user data, especially in today’s compliance-driven world. In this session, we provide some of the common use cases and design patterns to secure user data in a globally available GraphQL API, and discuss best practices for authentication and authorization in AWS AppSync.
AWS Fargate makes running containerized workloads on AWS easier than ever. In this session, we provide a technical foundation for using AWS Fargate with your existing containerized services. We also provide best practices for building images, configuring task definitions, task networking, secrets management, and monitoring.
Building Serverless Applications with Amazon DynamoDB & AWS Lambda - Workshop...Amazon Web Services
Join us for this first-ever advanced design and best practices workshop, designed to demonstrate the breadth of AWS serverless offerings and how the components work together. In this interactive workshop, we review the evolution of an e-commerce company that starts with a low-effort serverless product catalog, scales to a million daily users, and then adds analytics and near real-time monitoring. As we progress through the workshop, we dive deeply into AWS serverless services, such as Amazon DynamoDB, AWS Lambda, and Amazon Kinesis. We also use Amazon S3, Amazon API Gateway, Amazon Cognito, and other services that enable you to optimize costs and improve performance. Basic knowledge of DynamoDB, Lambda, and Kinesis is required. Bring your laptop and power supply to this session.
BDA308 Deep Dive: Log Analytics with Amazon Elasticsearch ServiceAmazon Web Services
Amazon Elasticsearch Service makes it easy to deploy, secure, operate, and scale Elasticsearch for log analytics, full text search, application monitoring, and more. In this session you learn how to configure a secure, petabyte-scale Amazon Elasticsearch Service cluster and build Kibana dashboards to analyze your data. In addition, we discuss best practices to make your cluster reliable, take backups, and debug slow-running queries and indexing operations.
Best Practices to Secure Data Lake on AWS (ANT327) - AWS re:Invent 2018Amazon Web Services
As customers are looking to build Data lakes to AWS, managing security, catalog and data quality becomes a challenge. Once data is put on Amazon S3, there are multiple processing engines to access it. This could be either through a SQL interface, programmatic, or using API. Customers require federated access to their data with strong controls around Authentication, Authorization, Encryption, and Audit. In this session, we explore the major AWS analytics services and platforms that customers can use to access data in the data Lake and provide best practices on securing them.
This session provides IT pros and application owners an overview of AWS options for building hybrid storage architectures or even entirely migrating datacenter storage to the AWS cloud. The AWS Storage Gateway connects existing on-premises block, file or tape storage systems to AWS cloud storage over the WAN in a hybrid model. The AWS Snow family of physical devices can capture, pre-process and migrate data into and out of AWS without any network connection at all. Join us to learn how you can close down datacenters, reduce storage footprints, and build solutions for tiering, data lakes, backup, disaster recovery, and migration.
Build a Multi-Region Serverless Application for Resilience & High Availabilit...Amazon Web Services
Do you have a mission-critical serverless app that requires maximum uptime? Come learn how to build and deploy a multi-region serverless application to maximize application availability and resilience. In this workshop, you enter a scenario in which you help a fictional unicorn ridesharing company, Wild Rydes (www.wildrydes.com), deploy a critical customer support application using a serverless architecture. When a passenger completes a ride, they can use the app to inform the company if they had any issues with their trip—perhaps a lost wallet or a misbehaving unicorn. Since Wild Rydes is global, this support application takes advantage of a multi-region, highly available architecture using services such as AWS Lambda, Amazon API Gateway, Amazon DynamoDB, Amazon Route 53, Amazon CloudFront, and Amazon S3 to maximize availability. It also uses Amazon Cognito federated identities for user authentication. Attendees should bring a laptop and be familiar with the AWS Management Console and the AWS CLI.
Build Data Lakes and Analytics on AWS: Patterns & Best PracticesAmazon Web Services
With over 90% of today’s data generated in the last two years, the rate of data growth is showing no sign of slowing down. In this session, we step through the challenges and best practices for capturing data, understanding what data you own, driving insights, and predicting the future using AWS services. We frame the session and demonstrations around common pitfalls of building data lakes and how to successfully drive analytics and insights from data. We also discuss the architecture patterns brought together key AWS services, including Amazon S3, AWS Glue, Amazon Athena, Amazon Kinesis, and Amazon Machine Learning. Discover the real-world application of data lakes for roles including data scientists and business users.
Stephen Moon, Sr. Solutions Architect, Amazon Web Services
James Juniper, Solution Architect for the Geo-Community Cloud, Natural Resources Canada
Build Data Lakes & Analytics on AWS: Patterns & Best PracticesAmazon Web Services
With over 90% of today’s data generated in the last two years, the rate of data growth is showing no sign of slowing down. In this session, we step through the challenges and best practices for capturing data, understanding what data you own, driving insights, and predicting the future using AWS services. We frame the session and demonstrations around common pitfalls of building data lakes and how to successfully drive analytics and insights from data. We also discuss the architecture patterns brought together key AWS services, including Amazon S3, AWS Glue, Amazon Athena, Amazon Kinesis, and Amazon Machine Learning. Discover the real-world application of data lakes for roles including data scientists and business users.
Stephen Moon, Sr. Solutions Architect, Amazon Web Services
James Juniper, Solution Architect for the Geo-Community Cloud, Natural Resources Canada
Big Data Analytics Architectural Patterns and Best Practices (ANT201-R1) - AW...Amazon Web Services
In this session, we discuss architectural principles that helps simplify big data analytics.
We'll apply principles to various stages of big data processing: collect, store, process, analyze, and visualize. We'll disucss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on.
Finally, we provide reference architectures, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
Data Lake Implementation: Processing and Querying Data in Place (STG204-R1) -...Amazon Web Services
Flexibility is key when building and scaling a data lake. The analytics solutions you use in the future will almost certainly be different from the ones you use today, and choosing the right storage architecture gives you the agility to quickly experiment and migrate with the latest analytics solutions. In this session, we explore best practices for building a data lake in Amazon S3 and Amazon Glacier for leveraging an entire array of AWS, open source, and third-party analytics tools. We explore use cases for traditional analytics tools, including Amazon EMR and AWS Glue, as well as query-in-place tools like Amazon Athena, Amazon Redshift Spectrum, Amazon S3 Select, and Amazon Glacier Select.
Building a Modern Data Warehouse - Deep Dive on Amazon RedshiftAmazon Web Services
Osemeke Isibor, Solutions Architect, AWS
In this session, we take a deep dive on Amazon Redshift architecture and the latest performance enhancements that give you faster insights into your data. We also cover Redshift Spectrum, a feature of Redshift that enables you to analyze data across Redshift and your Amazon S3 data lake to deliver unique insights not possible by analyzing independent data silos.
A data lake is an architectural approach that allows you to store massive amounts of data into a central location, so it's readily available to be categorized, processed, analyzed and consumed by diverse groups within an organization.In this session, we will introduce the Data Lake concept and its implementation on AWS.We will explain the different roles our services play and how they fit into the Data Lake picture.
AWS Floor 28 - Building Data lake on AWSAdir Sharabi
AWS makes it easy to build and operate a highly scalable and flexible data platforms to collect, process, and analyze data so you can get timely insights and react quickly to new information. In this session we will talk about how to improve over time using your data. How do you take your everyday data and build relevant business insights, to help and continuously improve your business processes, and keep your innovation going based on your data.
Leadership Session: AWS Database and Analytics (DAT206-L) - AWS re:Invent 2018Amazon Web Services
We’re witnessing an unprecedented growth in the amount of data collected and stored in the cloud. Getting insights from this data requires database and analytics services that scale and perform in ways not possible before. AWS offers the broadest set of database and analytics services to process, store, manage, and analyze all your data. In this session, we provide an overview of the database and analytics services at AWS, new services and features we launched this year, how customers are using these services, and our vision for continued innovation in this space.
by Ben Willett, Solutions Architect, AWS
Organizations use reports, dashboards, and analytics tools to extract insights from their data, monitor performance, and support decision making. To support these tools, data must be collected and prepared for use. We'll look at two approaches: a structured centralized data repository as a Data Warehouse the less-structured repository of a Data Lake. We'll compare these approaches, examine the services that support each, and explore how they work together.
Building a Modern Data Warehouse: Deep Dive on Amazon Redshift - SRV337 - Chi...Amazon Web Services
In this chalk talk, we take a deep dive on Amazon Redshift architecture and the latest performance enhancements that give you faster insights into your data. We also cover Amazon Redshift Spectrum, a feature of Amazon Redshift that enables you to analyze data across Amazon Redshift and your Amazon S3 data lake to deliver unique insights not possible by analyzing independent data silos.
Using data lakes to quench your analytics fire - AWS Summit Cape Town 2018Amazon Web Services
Speaker: Shafreen Sayyed, AWS
Level: 200
Traditional data storage and analytic tools no longer provide the agility and flexibility required to deliver relevant business insights. We are seeing more and more organisations shift to a data lake solution. This approach allows you to store massive amounts of data in a central location so its readily available to be categorized, processed, analyzed, and consumed by diverse organizational groups. In this session, we’ll assemble a data lake using services such as Amazon S3, Amazon Kinesis, Amazon Athena, Amazon EMR, AWS Glue and integration with Amazon Redshift Spectrum.
by Amy Che, Sr Solutions Delivery Manager AWS and Marie Yap, Technical Account Manager AWS
AWS Data & Analytics Week is an opportunity to learn about Amazon’s family of managed analytics services. These services provide easy, scalable, reliable, and cost-effective ways to manage your data in the cloud. We explain the fundamentals and take a technical deep dive into Amazon Redshift data warehouse; Data Lake services including Amazon EMR, Amazon Athena, & Amazon Redshift Spectrum; Log Analytics with Amazon Elasticsearch Service; and data preparation and placement services with AWS Glue and Amazon Kinesis. You'll will learn how to get started, how to support applications, and how to scale.
by Andre Hass, Specialist Technical Account Manager, AWS
Organizations use reports, dashboards, and analytics tools to extract insights from their data, monitor performance, and support decision making. To support these tools, data must be collected and prepared for use. We'll look at two approaches: a structured centralized data repository as a Data Warehouse the less-structured repository of a Data Lake. We'll compare these approaches, examine the services that support each, and explore how they work together.
Modernise your Data Warehouse with Amazon Redshift and Amazon Redshift SpectrumAmazon Web Services
We will walk through how to migrate and modernise your legacy data warehouse, moving from an on-premises server or application, to the cloud. You will learn how to easily migrate your data by leveraging serverless ETL, data cataloging as well as the techniques needed to successfully modernise your data warehouse, reduce costs, and increase performance and scalability.
Speaker: Paul Macey, Specialist Solutions Architect, AWS
Building Data Lakes That Cost Less and Deliver Results Faster - AWS Online Te...Amazon Web Services
Learning Objectives:
- Get an inside look at Amazon S3 Select and how it helps to accelerate application performance
- Learn about how Amazon Glacier Select helps you extend your data lake to archival storage
- Understand how different applications can leverage these features
Build Data Lakes and Analytics on AWS: Patterns & Best Practices - BDA305 - A...Amazon Web Services
In this session, we show you how to understand what data you have, how to drive insights, and how to make predictions using purpose-built AWS services. Learn about the common pitfalls of building data lakes and discover how to successfully drive analytics and insights from your data. Also learn how services such as Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR, Amazon Kinesis, and Amazon ML services work together to build a successful data lake for various roles, including data scientists and business users.
Similar to Analyze your Data Lake, Fast @ Any Scale - AWS Online Tech Talks (20)
Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...Amazon Web Services
Il Forecasting è un processo importante per tantissime aziende e viene utilizzato in vari ambiti per cercare di prevedere in modo accurato la crescita e distribuzione di un prodotto, l’utilizzo delle risorse necessarie nelle linee produttive, presentazioni finanziarie e tanto altro. Amazon utilizza delle tecniche avanzate di forecasting, in parte questi servizi sono stati messi a disposizione di tutti i clienti AWS.
In questa sessione illustreremo come pre-processare i dati che contengono una componente temporale e successivamente utilizzare un algoritmo che a partire dal tipo di dato analizzato produce un forecasting accurato.
Big Data per le Startup: come creare applicazioni Big Data in modalità Server...Amazon Web Services
La varietà e la quantità di dati che si crea ogni giorno accelera sempre più velocemente e rappresenta una opportunità irripetibile per innovare e creare nuove startup.
Tuttavia gestire grandi quantità di dati può apparire complesso: creare cluster Big Data su larga scala sembra essere un investimento accessibile solo ad aziende consolidate. Ma l’elasticità del Cloud e, in particolare, i servizi Serverless ci permettono di rompere questi limiti.
Vediamo quindi come è possibile sviluppare applicazioni Big Data rapidamente, senza preoccuparci dell’infrastruttura, ma dedicando tutte le risorse allo sviluppo delle nostre le nostre idee per creare prodotti innovativi.
Ora puoi utilizzare Amazon Elastic Kubernetes Service (EKS) per eseguire pod Kubernetes su AWS Fargate, il motore di elaborazione serverless creato per container su AWS. Questo rende più semplice che mai costruire ed eseguire le tue applicazioni Kubernetes nel cloud AWS.In questa sessione presenteremo le caratteristiche principali del servizio e come distribuire la tua applicazione in pochi passaggi
Vent'anni fa Amazon ha attraversato una trasformazione radicale con l'obiettivo di aumentare il ritmo dell'innovazione. In questo periodo abbiamo imparato come cambiare il nostro approccio allo sviluppo delle applicazioni ci ha permesso di aumentare notevolmente l'agilità, la velocità di rilascio e, in definitiva, ci ha consentito di creare applicazioni più affidabili e scalabili. In questa sessione illustreremo come definiamo le applicazioni moderne e come la creazione di app moderne influisce non solo sull'architettura dell'applicazione, ma sulla struttura organizzativa, sulle pipeline di rilascio dello sviluppo e persino sul modello operativo. Descriveremo anche approcci comuni alla modernizzazione, compreso l'approccio utilizzato dalla stessa Amazon.com.
Come spendere fino al 90% in meno con i container e le istanze spot Amazon Web Services
L’utilizzo dei container è in continua crescita.
Se correttamente disegnate, le applicazioni basate su Container sono molto spesso stateless e flessibili.
I servizi AWS ECS, EKS e Kubernetes su EC2 possono sfruttare le istanze Spot, portando ad un risparmio medio del 70% rispetto alle istanze On Demand. In questa sessione scopriremo insieme quali sono le caratteristiche delle istanze Spot e come possono essere utilizzate facilmente su AWS. Impareremo inoltre come Spreaker sfrutta le istanze spot per eseguire applicazioni di diverso tipo, in produzione, ad una frazione del costo on-demand!
In recent months, many customers have been asking us the question – how to monetise Open APIs, simplify Fintech integrations and accelerate adoption of various Open Banking business models. Therefore, AWS and FinConecta would like to invite you to Open Finance marketplace presentation on October 20th.
Event Agenda :
Open banking so far (short recap)
• PSD2, OB UK, OB Australia, OB LATAM, OB Israel
Intro to Open Finance marketplace
• Scope
• Features
• Tech overview and Demo
The role of the Cloud
The Future of APIs
• Complying with regulation
• Monetizing data / APIs
• Business models
• Time to market
One platform for all: a Strategic approach
Q&A
Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...Amazon Web Services
Per creare valore e costruire una propria offerta differenziante e riconoscibile, le startup di successo sanno come combinare tecnologie consolidate con componenti innovativi creati ad hoc.
AWS fornisce servizi pronti all'utilizzo e, allo stesso tempo, permette di personalizzare e creare gli elementi differenzianti della propria offerta.
Concentrandoci sulle tecnologie di Machine Learning, vedremo come selezionare i servizi di intelligenza artificiale offerti da AWS e, anche attraverso una demo, come costruire modelli di Machine Learning personalizzati utilizzando SageMaker Studio.
OpsWorks Configuration Management: automatizza la gestione e i deployment del...Amazon Web Services
Con l'approccio tradizionale al mondo IT per molti anni è stato difficile implementare tecniche di DevOps, che finora spesso hanno previsto attività manuali portando di tanto in tanto a dei downtime degli applicativi interrompendo l'operatività dell'utente. Con l'avvento del cloud, le tecniche di DevOps sono ormai a portata di tutti a basso costo per qualsiasi genere di workload, garantendo maggiore affidabilità del sistema e risultando in dei significativi miglioramenti della business continuity.
AWS mette a disposizione AWS OpsWork come strumento di Configuration Management che mira ad automatizzare e semplificare la gestione e i deployment delle istanze EC2 per mezzo di workload Chef e Puppet.
Scopri come sfruttare AWS OpsWork a garanzia e affidabilità del tuo applicativo installato su Instanze EC2.
Microsoft Active Directory su AWS per supportare i tuoi Windows WorkloadsAmazon Web Services
Vuoi conoscere le opzioni per eseguire Microsoft Active Directory su AWS? Quando si spostano carichi di lavoro Microsoft in AWS, è importante considerare come distribuire Microsoft Active Directory per supportare la gestione, l'autenticazione e l'autorizzazione dei criteri di gruppo. In questa sessione, discuteremo le opzioni per la distribuzione di Microsoft Active Directory su AWS, incluso AWS Directory Service per Microsoft Active Directory e la distribuzione di Active Directory su Windows su Amazon Elastic Compute Cloud (Amazon EC2). Trattiamo argomenti quali l'integrazione del tuo ambiente Microsoft Active Directory locale nel cloud e l'utilizzo di applicazioni SaaS, come Office 365, con AWS Single Sign-On.
Dal riconoscimento facciale al riconoscimento di frodi o difetti di fabbricazione, l'analisi di immagini e video che sfruttano tecniche di intelligenza artificiale, si stanno evolvendo e raffinando a ritmi elevati. In questo webinar esploreremo le possibilità messe a disposizione dai servizi AWS per applicare lo stato dell'arte delle tecniche di computer vision a scenari reali.
Amazon Web Services e VMware organizzano un evento virtuale gratuito il prossimo mercoledì 14 Ottobre dalle 12:00 alle 13:00 dedicato a VMware Cloud ™ on AWS, il servizio on demand che consente di eseguire applicazioni in ambienti cloud basati su VMware vSphere® e di accedere ad una vasta gamma di servizi AWS, sfruttando a pieno le potenzialità del cloud AWS e tutelando gli investimenti VMware esistenti.
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
Crea la tua prima serverless ledger-based app con QLDB e NodeJSAmazon Web Services
Molte aziende oggi, costruiscono applicazioni con funzionalità di tipo ledger ad esempio per verificare lo storico di accrediti o addebiti nelle transazioni bancarie o ancora per tenere traccia del flusso supply chain dei propri prodotti.
Alla base di queste soluzioni ci sono i database ledger che permettono di avere un log delle transazioni trasparente, immutabile e crittograficamente verificabile, ma sono strumenti complessi e onerosi da gestire.
Amazon QLDB elimina la necessità di costruire sistemi personalizzati e complessi fornendo un database ledger serverless completamente gestito.
In questa sessione scopriremo come realizzare un'applicazione serverless completa che utilizzi le funzionalità di QLDB.
Con l’ascesa delle architetture di microservizi e delle ricche applicazioni mobili e Web, le API sono più importanti che mai per offrire agli utenti finali una user experience eccezionale. In questa sessione impareremo come affrontare le moderne sfide di progettazione delle API con GraphQL, un linguaggio di query API open source utilizzato da Facebook, Amazon e altro e come utilizzare AWS AppSync, un servizio GraphQL serverless gestito su AWS. Approfondiremo diversi scenari, comprendendo come AppSync può aiutare a risolvere questi casi d’uso creando API moderne con funzionalità di aggiornamento dati in tempo reale e offline.
Inoltre, impareremo come Sky Italia utilizza AWS AppSync per fornire aggiornamenti sportivi in tempo reale agli utenti del proprio portale web.
Database Oracle e VMware Cloud™ on AWS: i miti da sfatareAmazon Web Services
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
In queste slide, gli esperti AWS e VMware presentano semplici e pratici accorgimenti per facilitare e semplificare la migrazione dei carichi di lavoro Oracle accelerando la trasformazione verso il cloud, approfondiranno l’architettura e dimostreranno come sfruttare a pieno le potenzialità di VMware Cloud ™ on AWS.
Amazon Elastic Container Service (Amazon ECS) è un servizio di gestione dei container altamente scalabile, che semplifica la gestione dei contenitori Docker attraverso un layer di orchestrazione per il controllo del deployment e del relativo lifecycle. In questa sessione presenteremo le principali caratteristiche del servizio, le architetture di riferimento per i differenti carichi di lavoro e i semplici passi necessari per poter velocemente migrare uno o più dei tuo container.