[판교에서 만나는 아마존웹서비스] Obama for America를 통해서 본 AWS에서의 데이터 분석

Building a real time data analysis infrastructure is a challenging task that requires experienced engineers. With AWS services, you can do it in a matter of minutes, scale it easily to handle almost unlimited load, and keep it as a low cost infrastructure. This session is an opportunity to see a live demo on building an infrastructure using a combination of Amazon Kinesis, Redshift, DynamoDB, EMR and CloudSearch, to collect, process and share data.

The document discusses Comcast's journey towards continuous delivery. It describes how Comcast has transitioned from a process where deployments were done by many humans over much time, to one where a single human and many machines can deploy changes much faster. It introduces "Gumby", a tool developed by Comcast to automate deployments across various cloud platforms like vSphere, Openstack, and EC2 using technologies like Puppet, Git, and Cloud-Init. Gumby started as an experiment using Play and Akka but was rewritten using Spray and Akka to scale deployments. It is now used to deploy around 60% of Comcast's X1 backend infrastructure.

In this talk from the Dublin Websummit 2014 AWS Technical Evangelist Danilo Poccia discusses using the AWS cloud to support Sensor-based Internet of Things applications. Includes a discussion of the architectural patterns for creating event processing applications and techniques for ensuring the security of IoT applications, using the AWS SDKs to integrate sensors with the cloud and techniques for building Geo-spacial applications on the AWS cloud.

L'histoire de l'informatique jeune: En moins de 50 ans l'informatique a révolutionné l'industrie et la société. En conséquence, la façon dont nous, informaticiens, travaillons est encore "artisanale" par endroit. Si l'on considère les processus de développement aujourd'hui, nous pouvons parler d'usine logicielle. Les tâches fastidieuses peuvent être automatisées et ce du démarage d'un projet à l'intégration continue du service. Mais aujourd'hui tout le secteur de l'hébergement n'a pas encore complètement automatisé de façon similaire: industrialisé de bout en bout. La perte de productivité est phénoménale. Et c'est que ce Quentin ADAM va aborder au cours de cette conférence.

Learn how Officeworks leverages NetApp’s Cloud Data Services to simplify storage and radically reduce costs. Greg Rose, Principal Systems Engineer at Officeworks, will share first-hand experience using Amazon EC2, Amazon EBS and Amazon S3 with NetApp Cloud Volumes. See how Officeworks instantly creates multi-protocol persistent storage volumes, clones data for easy Dev & Test, utilizes de-duplication to reduce volume sizes, and automatically tiers their data to Amazon S3. Leveraging Officeworks’ techniques with NetApp’s Cloud Volumes, you too will get the most from your cloud investments.

The rise of open-source electronics platforms has enabled makers and developers to build devices capable of interacting with our environment. The processing power of those devices is limited, but they are often equipped with internet access which allows them to use AWS to provide more features or data processing capabilities to their users. Companies such as Dropcam for video capture, or Illumina for DNA sequencing, already produce devices that directly use AWS to offer much richer services. This session shows how to use an Intel Galileo board to interact with the AWS API. The board will collect sensor data that will be sent to the real-time data analytics backend built during part 1.

This document discusses deploying Docker containers on Amazon Web Services. It covers using AWS services like EC2, OpsWorks and Elastic Beanstalk that support Docker. It describes using the EC2 Container Service for container management and deploying containers across a cluster of EC2 instances. It also discusses the immutable server pattern of deploying to new infrastructure with each release rather than changing existing servers.

This document discusses using Docker on AWS. It describes using Docker to deploy highly scalable applications across multiple AWS regions and availability zones. It also discusses using a private Docker registry hosted on EC2 and S3 to store custom Docker images. Finally, it summarizes using Amazon EC2 Container Service (ECS) for container management on AWS, including concepts like clusters, tasks, and container instances.

This document contains information about machine learning and artificial intelligence technologies on AWS including: - Amazon SageMaker for building, training, and deploying machine learning models. - NVIDIA GPU instances for deep learning workloads on AWS. - Greengrass for deploying machine learning models on edge devices. - Conferences and events related to AI/ML and AWS in Japan.

A brief introduction to Amazon Web Services from a web developer's perspective. You'll find out what EC2, VPC and S3 stand for, you'll also learn the purpose of security groups, resource isolation and why it all matters for your application. This is a part of Mirumee Talks — an engineering meetup that is free for everyone to come and enjoy. We love sharing what we know and what we are currently up to our in the techy trenches. Talk. Share. Learn.

This document summarizes a presentation about designing applications for elasticity on AWS. It discusses key AWS concepts like scalability, security, and elasticity. It emphasizes designing applications according to service-oriented architecture principles like loose coupling, abstraction, and reusability. It provides recommendations for implementing elasticity on AWS using services like Elastic Load Balancing, Auto Scaling, and CloudWatch. The presenter advocates automating configurations and leveraging free tier services like Route53, CloudFront, and different instance types to optimize costs.

Netflix is a large streaming company with over 75 million members and 42.5 billion hours watched in 2015. The company has thousands of microservices and many tens of thousands of virtual machines across 3 regions worldwide. Netflix open sources much of its cloud platform technologies to get feedback, collaborate with others, and improve proven open source projects for its scale and availability. Open sourcing also helps with recruiting and retention by allowing candidates and engineers to work on the same projects they could at Netflix. Netflix's open source offerings like Spring Cloud and container technologies are widely used both publicly and internally at other large companies.

How to monitor unknown third party code? One of the hardest challenges we face running Clever Cloud, apart from the impressive scale we face with hundreds of new applications per week, is the monitoring of unknown tech stacks. The first goal of rebuilding the monitoring platform was to accommodate the immutable infrastructure pattern that generates lots of ephemeral hosts every minute. The traditional approach is to focus on VMs or hosts, not applications. We needed to shift this into an approach of auto-discovery of metrics to monitor, allowing third party code to publish new items. This talk explains our journey in building Clever Cloud Metrics stack, heavily based on Warp10 (Kafka/Hadoop/Storm based) to deliver developer efficiency and trustability to our clients applications.

Cloud Native Night Mai 2019, Mainz: Vortrag von Alex Krause (@alex0ptr, Senior Softwareingenieur bei QAware) Join our Meetup: www.meetup.com/cloud-native-night == Dokument bitte herunterladen, falls unscharf! Please download slides if blurred! == Abstract: Eine solide Cloud Infrastruktur ist die Basis für Cloud-Native Applikationen. Diese muss genau wie die Anwendung einfach zu ändern, dynamisch skalierbar, hochverfügbar und sicher sein. Diese Anforderungen führen zu komplexen Strukturen, die selten von einzelnen Personen verwaltet werden. Zusätzlich ist es wünschenswert die Änderungen und die Erfüllung der Anforderungen nachvollziehbar über unterschiedliche Umgebungen hinweg zu dokumentieren. Glücklicherweise ist Cloud-Infrastruktur hochgradig automatisierbar. In diesem technisch orientierten Vortrag kombinieren wir Infrastructure as Code und Immutable Infrastructure um eine produktionsreife Cloud-Infrastruktur aufzubauen. Insbesondere Cloud Einsteigern geben wir hierdurch Tools wie cloud-init, Packer und Terraform in die Hand um Standard-Architekturen auf AWS den eigenen Anstrich zu verpassen. Code: https://github.com/alex0ptr/cloud-101

This document provides an overview of Amazon Web Services (AWS) and the services it offers for building serverless applications. It discusses AWS Lambda, API Gateway, DynamoDB and other core services. It also summarizes approaches for structuring applications using these serverless computing services and development best practices like testing and deployment.

Developing and experimenting with machine learning models in Python is easy and well supported by robust and agile libraries such as scikit-learn, although efficiently deploying multi-model systems at scale is still a challenge in the data science field. This talk will focus on the main issues related to deploying machine learning models and how to make scikit-learn production-ready with minimal operational efforts, by means of Cloud Computing services, in particular Amazon Web Services. Prerequisites: basic Machine Learning understanding (modeling and training), minimal knowledge about scikit-learn and Python utilities such as Pandas and boto.

We, as KKStream / KKTV / KKBOX, just kicked off the 1st sharing session inside our organization, introducing the event, the new services and potentially some of our insights and opinions. Let's keep fingers crossed for the following deeper sessions.

Is Multi-Cloud good or bad? How about Serverless? The answer to all these questions is Yes, sometimes. Whether you're new to all this or a long-time industry veteran, you'll surely come away from this approachable talk with a new understanding of cutting edge technology and actionable insights on how to make smart trade offs. Vancouver Cloud Summit 2024 (2024-04-22)

This document discusses how various companies scale their services and applications on AWS to handle large user loads and data volumes. It provides examples of Animoto handling over 1 billion files saved per day and Airbnb having over 9 million guests. It then outlines an approach for scaling an application from 1 user to millions by starting with EC2 instances, adding services like S3, DynamoDB, ElastiCache and auto-scaling groups. The document emphasizes using AWS managed services to avoid re-inventing solutions for tasks like queuing, storage and databases.

Customer Presentation by Conde Nast at the AWS Cloud for the Enterprise Event in NYC on October 19, 2009

This document provides an overview of a workshop on cloud native, capacity, performance and cost optimization tools and techniques. It begins with introducing the difference between a presentation and workshop. It then discusses introducing attendees, presenting on various cloud native topics like migration paths and operations tools, and benchmarking Cassandra performance at scale across AWS regions. The goal is to explore cloud native techniques while discussing specific problems attendees face.

Same basic flow as the keynote, but with a lot more detail, and we had a lot more interactive discussion rather than a presentation format. See part 2 for some more specific detail and links to other presentations.

The document discusses 10 tips for startups and developers to scale their applications from 0 to 10 million users on AWS. It provides examples of startups like Airbnb and Foursquare that were able to scale significantly using AWS services for computing, storage, databases, analytics and more. The tips include using AWS services to solve problems instead of doing it yourself, focusing on product over infrastructure, using auto-scaling and reserved instances to optimize costs as user base grows.

This document discusses building a social network using serverless architecture. It describes how the company moved from monolithic architecture to microservices, events, and serverless functions. This reduced costs by 95% compared to EC2 and allowed 15x more production releases per month. It also discusses challenges of testing, monitoring, security and other aspects of building serverless systems at scale.

Amazon Web Services provides startups with the low cost, easy to use infrastructure needed to scale and grow any size business. Attend this session and learn how to migrate your startup to AWS and make the most out of the platform.

Traditional data warehouses become expensive and slow down as the volume of your data grows. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it easy to analyze all of your data using existing business intelligence tools for 1/10th the traditional cost. This session will provide an introduction to Amazon Redshift and cover the essentials you need to deploy your data warehouse in the cloud so that you can achieve faster analytics and save costs. We’ll also cover the recently announced Redshift Spectrum, which allows you to query unstructured data directly from Amazon S3.

I presented to the Georgia Southern Computer Science ACM group. Rather than one topic for 90 minutes, I decided to do an UnConference. I presented them a list of 8-9 topics, let them vote on what to talk about, then repeated. Each presentation was ~8 minutes, (Except Career) and was by no means an attempt to explain the full concept or technology. Only to wake up their interest.

TechNet Events Presents – for the IT Professional In this session, we will discuss: Azure architecture from the IT professional’s point of view Why an IT operations team would want to pursue Azure as an extension to the data center Configuration, deployment and scaling Azure-based applications The Azure roles (web, web service and worker) Azure storage options Azure security and identity options How Azure-based applications can be integrated with on-premises applications How operations teams can manage and monitor Azure-based applications

Leveraging big data and high performance computing (HPC) solutions enables your organization to make smarter and faster decisions that influence strategy, increase productivity, and ultimately grow your business. We kick off the Big Data and HPC track with the latest advancements in data analytics, databases, storage, and HPC at AWS. Hear customer success stories and discover how to put data to work in your own organization.

This document discusses cloud computing costs and analytics. It begins by providing background on cloud infrastructure and operations. It then discusses challenges in understanding and managing cloud costs. The document outlines the history and services of RightScale, a company that provides cloud cost analytics. It concludes by discussing RightScale's customers and culture.

- The document outlines strategies for scaling applications on Amazon Web Services (AWS) from a single instance to support millions of users. - It describes starting with a single EC2 instance and database and scaling out by adding more instances, load balancers, and managed database services. - The document recommends leveraging serverless architectures using services like AWS Lambda and managed services to build highly scalable and available applications without having to manage servers.

This document provides an overview and agenda for a workshop on patterns for continuous delivery, high availability, DevOps and cloud native development using NetflixOSS open source tools and frameworks. The presenter introduces himself and his background. The content covers Netflix's architecture evolution from monolithic to microservices, how Netflix scales on AWS, and principles and outcomes that enable cloud native development. The workshop then dives into specific NetflixOSS projects like Eureka, Cassandra, Zuul and Hystrix that help with service discovery, data storage, routing and availability. Tools for deployment, configuration, cost analysis and developer productivity are also discussed.

This document discusses how Japanese startups are using Amazon Web Services (AWS). It provides examples of architectures that startups are using on AWS to build scalable and reliable applications. It also describes some events for startup CTOs hosted by AWS to facilitate knowledge sharing. Finally, it shares real use cases of Japanese startups leveraging different AWS services like EC2, RDS, S3, CloudFront, and CloudSearch to build their applications and handle traffic bursts.

This document provides information about various AWS services for machine learning, analytics, databases, and data lakes. It discusses Amazon SageMaker as a fully managed service that allows developers and data scientists to build, train, and deploy machine learning models at scale. It also mentions Amazon Redshift as a data warehousing service for complex queries on large datasets and Amazon S3 as the most popular choice for data lakes with unmatched scalability, availability, and security capabilities.

This document provides an overview of Amazon Web Services (AWS) and its machine learning and artificial intelligence capabilities. It discusses how AWS offers a full suite of AI and ML services including tools for computer vision, natural language processing, forecasting, and more. It also outlines AWS's machine learning infrastructure which includes optimized hardware, frameworks, and SageMaker for building, training, and deploying models. AWS aims to put machine learning in the hands of every developer and data scientist.

AWS Lambda has changed the way we deploy and run software, but this new serverless paradigm has created new challenges to old problems - how do you test a cloud-hosted function locally? How do you monitor them? What about logging and config management? And how do we start migrating from existing architectures? In this talk Yan and Domas will discuss solutions to these challenges by drawing from real-world experience running Lambda in production and migrating from an existing monolithic architecture.

The document summarizes the 2015 Amazon Web Services re:Invent conference. It highlights the growth in attendance from 9,000 to 19,000. It outlines new computing and database services announced as well as analytics, security, and management tools. Examples are given of how Netflix and a content management system benefited from migrating to AWS. Lessons learned focused on not all features transferring directly and the learning curve involved. The document encourages hands-on learning with AWS free services and attending next year's conference.

DBA들이 Aurora MySQL과 Amazon Bedrock서비스를 연동한 생성형 AI를 어떻게 업무에 활용할지에 대해서 예제를 통해서 살펴보고, Aurora PostgreSQL의 pgVector를 Vector DB로써 어떻게 활용할수 있는지에 대해서 알아봅니다

최근 관심이 많은 GenAI RAG 를 위한 Vector Similarity Search를 2023년 re:invent에서 발표한 Neptune Analytics 를 통해 구현하여 Graph Query를 함께 할 수 있는 구성을 예제와 함께 설명합니다.