Database Week at the San Francisco Loft: Graph & Neptune
Graph databases are purpose-built to store and navigate relationships. They have advantages for many use cases: social networking, recommendation engines, fraud detection, and others where you need to create relationships between data and quickly query these relationships. Amazon Neptune is a fast, reliable, fully-managed graph database service that makes it easy to build and run applications that work with highly connected datasets. We’ll discuss when you should use a graph database and look at how to use Neptune.
Speaker: Paul Fryer- Enterprise Solutions Architect, AWS
Database Week at the San Francisco Loft
Graph & Neptune
Graph databases are purpose-built to store and navigate relationships. They have advantages for many use cases: social networking, recommendation engines, fraud detection, and others where you need to create relationships between data and quickly query these relationships. Amazon Neptune is a fast, reliable, fully-managed graph database service that makes it easy to build and run applications that work with highly connected datasets. We’ll discuss when you should use a graph database and look at how to use Neptune.
Speakers:
Taylor Riggan - Solutions Architect, AWS
Aditya Challa - Technical Account Manager, AWS
Graph databases are purpose-built to store and navigate relationships. They have advantages for many use cases: social networking, recommendation engines, fraud detection, and others where you need to create relationships between data and quickly query these relationships. Amazon Neptune is a fast, reliable, fully-managed graph database service that makes it easy to build and run applications that work with highly connected datasets. We’ll discuss when you should use a graph database and look at how to use Neptune.
Speaker: Taylor Riggan - Solutions Architect, AWS
Elasticsearch is a popular open-source distributed search and analytics engine, widely used for log analytics and text search – and increasingly used as a primary data store. Amazon Elasticsearch Service makes it easy to deploy, secure, operate, and scale Elasticsearch. We’ll take a look at how to use Elasticsearch Service to manage these different use cases.
Neptune is a Graph database, but what is a graph database, when should it be used and how? Come to this sessions and learn about Neptune and the different use cases for graph databases and how it can be used, including demos.
This document provides an overview of Amazon Neptune and graph databases. It discusses how graph databases are optimized for storing and querying highly connected data and provides examples of property graphs and RDF graphs. It also summarizes Amazon Neptune's key features like multi-AZ deployment, continuous backups, read replicas, and online restore capabilities.
In this presentation we will offer an overview of the fundamental concepts of graph databases and data representation and query technologies;
We will also focus on AWS Property Graph and Apache TinkerPop Gremlin. Then we'll talk about the use of Amazon Neptune: creating a cluster, loading data and running some sample queries;
Introduction to Graph Databases - a different way to see data, with endless possibilities!
Alex Barbosa Coqueiro - Head of Public Sector Solutions Architecture at AWS for Latin America, Canada & Caribbean covers Graph technology, terminology, how Graph could be applied to real-world business problems, and share a few examples of graph data model using AWS Cloud services.
Event details: https://www.meetup.com/Serverless-Toronto/events/271595147/
Event recording: https://youtu.be/p96pppoCIGo
For more exciting learning opportunities, join our #ServerlessTO community: https://www.meetup.com/Serverless-Toronto/about/
This document discusses Amazon ElastiCache, a fully managed in-memory cache and database service. It provides Redis and Memcached compatible data stores that can be used for fast databases, caches, and other use cases. The document outlines key features of ElastiCache like security, high availability, scalability, and common usage patterns. It also provides an example of how GE uses ElastiCache Redis to power its Predix platform and make it easy for developers to create Redis clusters.
Database Week at the San Francisco Loft
Graph & Neptune
Graph databases are purpose-built to store and navigate relationships. They have advantages for many use cases: social networking, recommendation engines, fraud detection, and others where you need to create relationships between data and quickly query these relationships. Amazon Neptune is a fast, reliable, fully-managed graph database service that makes it easy to build and run applications that work with highly connected datasets. We’ll discuss when you should use a graph database and look at how to use Neptune.
Speakers:
Taylor Riggan - Solutions Architect, AWS
Aditya Challa - Technical Account Manager, AWS
Graph databases are purpose-built to store and navigate relationships. They have advantages for many use cases: social networking, recommendation engines, fraud detection, and others where you need to create relationships between data and quickly query these relationships. Amazon Neptune is a fast, reliable, fully-managed graph database service that makes it easy to build and run applications that work with highly connected datasets. We’ll discuss when you should use a graph database and look at how to use Neptune.
Speaker: Taylor Riggan - Solutions Architect, AWS
Elasticsearch is a popular open-source distributed search and analytics engine, widely used for log analytics and text search – and increasingly used as a primary data store. Amazon Elasticsearch Service makes it easy to deploy, secure, operate, and scale Elasticsearch. We’ll take a look at how to use Elasticsearch Service to manage these different use cases.
Neptune is a Graph database, but what is a graph database, when should it be used and how? Come to this sessions and learn about Neptune and the different use cases for graph databases and how it can be used, including demos.
This document provides an overview of Amazon Neptune and graph databases. It discusses how graph databases are optimized for storing and querying highly connected data and provides examples of property graphs and RDF graphs. It also summarizes Amazon Neptune's key features like multi-AZ deployment, continuous backups, read replicas, and online restore capabilities.
In this presentation we will offer an overview of the fundamental concepts of graph databases and data representation and query technologies;
We will also focus on AWS Property Graph and Apache TinkerPop Gremlin. Then we'll talk about the use of Amazon Neptune: creating a cluster, loading data and running some sample queries;
Introduction to Graph Databases - a different way to see data, with endless possibilities!
Alex Barbosa Coqueiro - Head of Public Sector Solutions Architecture at AWS for Latin America, Canada & Caribbean covers Graph technology, terminology, how Graph could be applied to real-world business problems, and share a few examples of graph data model using AWS Cloud services.
Event details: https://www.meetup.com/Serverless-Toronto/events/271595147/
Event recording: https://youtu.be/p96pppoCIGo
For more exciting learning opportunities, join our #ServerlessTO community: https://www.meetup.com/Serverless-Toronto/about/
This document discusses Amazon ElastiCache, a fully managed in-memory cache and database service. It provides Redis and Memcached compatible data stores that can be used for fast databases, caches, and other use cases. The document outlines key features of ElastiCache like security, high availability, scalability, and common usage patterns. It also provides an example of how GE uses ElastiCache Redis to power its Predix platform and make it easy for developers to create Redis clusters.
Brad Bebee from Amazon discusses Amazon Neptune, a fully managed graph database service. Neptune can be used to build applications that work with highly connected data through properties like relationships and links. It allows querying billions of relationships efficiently using graph models and query languages like Gremlin and SPARQL. Neptune provides high availability, reliability, and scales to handle large datasets.
The document discusses marketing strategies for open source projects. It covers the types of content that can be created, such as documentation, blog posts, videos, and scholarly articles. It also discusses where to publish content, such as on GitHub pages or blogs, and how to optimize content for search engines. Additionally, it addresses the importance of community engagement through meetups, conferences and creating a positive community culture.
by Ganesh Shankaran, Sr. Solutions Architect, AWS
Database Week at the AWS Loft is an opportunity to learn about Amazon’s broad and deep family of managed database 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 RDS and Amazon Aurora relational databases, Amazon DynamoDB non-relational databases, Amazon Neptune graph databases, and Amazon ElastiCache managed Redis, along with options for database migration, caching, search and more. You'll will learn how to get started, how to support applications, and how to scale.
by Joseph Idziorek, Sr. Product Manager, AWS
Database Week at the AWS Loft is an opportunity to learn about Amazon’s broad and deep family of managed database 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 RDS and Amazon Aurora relational databases, Amazon DynamoDB non-relational databases, Amazon Neptune graph databases, and Amazon ElastiCache managed Redis, along with options for database migration, caching, search and more. You'll will learn how to get started, how to support applications, and how to scale.
Building Real-Time Serverless Backends with GraphQL | AWS Floor28Amazon Web Services
GraphQL is an open standard that lets you request, change, and subscribe to the exact data you need in a single network request. This makes prototyping and building data-intensive applications as simple as writing a few lines of code. In this session, we’ll introduce the core concepts of GraphQL and put that into practice with real-world implementations using tools such as AWS AppSync and the AWS Amplify toolchain.
This document discusses the rise of non-relational databases and their advantages over traditional relational databases for modern cloud applications. It outlines how characteristics like scale, data volume, and developer access have changed. It promotes the idea of using different data store technologies based on data needs, rather than relying on a single database. Examples of Amazon's non-relational database services are provided, including DynamoDB, ElastiCache, and the new Neptune graph database.
The document discusses building real-time serverless backends using GraphQL. It describes some of the challenges of building scalable data-driven apps and why GraphQL may be preferable to REST in some cases. It then introduces AWS AppSync as a managed service for application data using GraphQL with real-time capabilities and an offline programming model. It discusses how AppSync can be used with various data sources and features like subscriptions. Finally, it briefly mentions AWS Amplify, a library and tools for building fullstack serverless apps using AppSync and other AWS services.
Marketing Your Open Source Project (All Things Open 2018)Amazon Web Services
Your open source project competes with millions of others for users, contributors, and perhaps financial support. To stand out from the crowd, your project needs… marketing. If that term makes you shudder, or you simply don’t think you know how, this talk (aimed at anyone involved in open source) will help you understand the why and the how of open source marketing.
GraphQL is an open standard that lets you request, change, and subscribe to the exact data you need in a single network request. This makes prototyping and building data-intensive applications as simple as writing a few lines of code. In this session we’ll introduce the core concepts of GraphQL and put that into practice with real-world implementations using tools such as AWS Lambda and AWS AppSync to deliver real-time collaborative experiences for web and mobile apps, using multiple data sources, managing off-line users’ data, and resolving data conflicts.
GraphQL is an open standard that lets you request, change, and subscribe to the exact data you need in a single network request. This makes prototyping and building data-intensive applications as simple as writing a few lines of code. In this session we’ll introduce the core concepts of GraphQL and put that into practice with real-world implementations using tools such as AWS Lambda and AWS AppSync to deliver real-time collaborative experiences for web and mobile apps, using multiple data sources, managing off-line users’ data, and resolving data conflicts.
This document discusses the need for identifiers for things on the web to be attributable, discoverable, and allow for declarations of equivalences. It presents Subj3ct as a core model for subject declarations, equivalence statements, and resource statements with provenance that can help creators and consumers of linked data find existing and related identifiers through its ATOM/SKOS feeds, REST API, and web UI. Subj3ct aims to make mash-ups easier, applications smarter, and expand the knowledge base by helping connect linked data.
by J. Bako, Solutions Architect, AWS
Graph databases are purpose-built to store and navigate relationships. They have advantages for many use cases: social networking, recommendation engines, fraud detection, and others where you need to create relationships between data and quickly query these relationships. Amazon Neptune is a fast, reliable, fully-managed graph database service that makes it easy to build and run applications that work with highly connected datasets. We’ll discuss when you should use a graph database and look at how to use Neptune.
Graph databases are purpose-built to store and navigate relationships. They have advantages for many use cases: social networking, recommendation engines, fraud detection, and others where you need to create relationships between data and quickly query these relationships. Amazon Neptune is a fast, reliable, fully-managed graph database service that makes it easy to build and run applications that work with highly connected datasets. We’ll discuss when you should use a graph database and look at how to use Neptune.
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
Connecting the Unconnected using GraphDB - Tel Aviv Summit 2018Amazon Web Services
In this session, we will build on stage microservices that will be used to access in real time the data collected in the previous sessions. We will focus on Amazon Neptune, the new Graph database and Amazon Rekognition, the image recognition service.
Understanding Graph Databases: AWS Developer Workshop at Web SummitAmazon Web Services
Understanding Graph Databases: AWS Developer Workshop at Web Summit 2018
Speaker: Gabe Hollombe - Technical Evangelist, AWS
Different types of applications do better with different database technologies. Traditional relational databases are often our go-to choice, but if we're storing lots of relationships between data, a graph database might be a better option. In this session, we'll learn how graph databases differ from more familiar SQL and NoSQL options, and we'll review some of the use cases where graph databases really shine. I'll show how to leverage the power of the cloud to set up your own highly available, scalable, and secure graph database, and we'll run some queries on sample data to get more familiar with how to model and query data in a graph database.
NEW LAUNCH! Deep dive on Amazon Neptune - DAT318 - re:Invent 2017Amazon Web Services
Amazon Neptune is a fully managed graph database service which has been built ground up for handling rich highly connected data. Graph databases have diverse use cases across multiple industries; examples include recommendation engines, knowledge graphs, fraud detection, social networks, network management and life sciences. Amazon Neptune is open and flexible with support for Apache TinkerPop and RDF/SPARQL standards. Under the hood Neptune uses the same foundational building blocks as Amazon Aurora which gives it high performance, availability and durability. In this session, we will do a deep dive into capabilities, performance and key innovations in Amazon Neptune.
GraphQL is a query language for APIs and a runtime for fulfilling those queries. It gives clients the power to ask for exactly what they need, which makes it a great fit for modern web and mobile apps. In this talk, we explain why GraphQL was created, introduce you to the syntax and behavior, and then show how to use it to build powerful APIs for your data. We will also introduce you to AWS AppSync, a GraphQL-powered serverless backend for apps, which you can use to host GraphQL APIs and also add real-time and offline capabilities to your web and mobile apps. You can follow along if you have an AWS account – no GraphQL experience required!
Level: Beginner
Speaker: Rohan Deshpande - Sr. Software Dev Engineer, AWS Mobile Applications
Using AI for real-life data enrichment - Tel Aviv Summit 2018Amazon Web Services
In this session, we will learn how we used Amazon Machine Learning services to enrich our datasets, we will use Amazon rekognition to extract data from pictures and Amazon comprehend to get sentiments and areas of interests from posts. We'll use Amazon SageMaker built-in algorithms to easily build and train a machine learning model and deploy it into a production-ready hosted environment.
Best Practices for Designing GraphQL APIs That Scale (MOB420-R1) - AWS re:Inv...Amazon Web Services
The emergence of GraphQL over the past couple of years has changed the way people are thinking about API development and is reshaping the way engineering teams are structured by allowing front-end developers to move further up the stack and redefining traditional engineering roles. With the introduction of AWS AppSync, we can easily extend the possibilities of what GraphL is capable of, enabling a single GraphQL API to power entire applications by working with multiple data sources, microservices, and AWS Lambda functions. In this session, we show this architecture in action, walk through the design and implementation, and answer your questions as we dive deep.
Taking your Progressive Web App to the Next Level with GraphQL and AWS AppSyncAmazon Web Services
Progressive Web Apps (PWAs) are the future of web development and combine the best of web and native apps. In this session you will learn how to build PWAs on AWS then take your app to the next level with AWS AppSync. We will cover how AWS AppSync allows you to query your data using GraphQL and how it handles mutations, subscriptions, offline access, realtime communications, conflict resolution, and efficient data fetching.
by Rich Alberth, Solutions Architect, AWS
If you need to query relationships between data, you need a graph database. We’ll take a close look at Amazon Neptune, explore the differences between property graphs and RDF, then do graph data queries using Apache Tinkerpop. You’ll need a laptop with a Firefox or Chrome browser.
Brad Bebee from Amazon discusses Amazon Neptune, a fully managed graph database service. Neptune can be used to build applications that work with highly connected data through properties like relationships and links. It allows querying billions of relationships efficiently using graph models and query languages like Gremlin and SPARQL. Neptune provides high availability, reliability, and scales to handle large datasets.
The document discusses marketing strategies for open source projects. It covers the types of content that can be created, such as documentation, blog posts, videos, and scholarly articles. It also discusses where to publish content, such as on GitHub pages or blogs, and how to optimize content for search engines. Additionally, it addresses the importance of community engagement through meetups, conferences and creating a positive community culture.
by Ganesh Shankaran, Sr. Solutions Architect, AWS
Database Week at the AWS Loft is an opportunity to learn about Amazon’s broad and deep family of managed database 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 RDS and Amazon Aurora relational databases, Amazon DynamoDB non-relational databases, Amazon Neptune graph databases, and Amazon ElastiCache managed Redis, along with options for database migration, caching, search and more. You'll will learn how to get started, how to support applications, and how to scale.
by Joseph Idziorek, Sr. Product Manager, AWS
Database Week at the AWS Loft is an opportunity to learn about Amazon’s broad and deep family of managed database 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 RDS and Amazon Aurora relational databases, Amazon DynamoDB non-relational databases, Amazon Neptune graph databases, and Amazon ElastiCache managed Redis, along with options for database migration, caching, search and more. You'll will learn how to get started, how to support applications, and how to scale.
Building Real-Time Serverless Backends with GraphQL | AWS Floor28Amazon Web Services
GraphQL is an open standard that lets you request, change, and subscribe to the exact data you need in a single network request. This makes prototyping and building data-intensive applications as simple as writing a few lines of code. In this session, we’ll introduce the core concepts of GraphQL and put that into practice with real-world implementations using tools such as AWS AppSync and the AWS Amplify toolchain.
This document discusses the rise of non-relational databases and their advantages over traditional relational databases for modern cloud applications. It outlines how characteristics like scale, data volume, and developer access have changed. It promotes the idea of using different data store technologies based on data needs, rather than relying on a single database. Examples of Amazon's non-relational database services are provided, including DynamoDB, ElastiCache, and the new Neptune graph database.
The document discusses building real-time serverless backends using GraphQL. It describes some of the challenges of building scalable data-driven apps and why GraphQL may be preferable to REST in some cases. It then introduces AWS AppSync as a managed service for application data using GraphQL with real-time capabilities and an offline programming model. It discusses how AppSync can be used with various data sources and features like subscriptions. Finally, it briefly mentions AWS Amplify, a library and tools for building fullstack serverless apps using AppSync and other AWS services.
Marketing Your Open Source Project (All Things Open 2018)Amazon Web Services
Your open source project competes with millions of others for users, contributors, and perhaps financial support. To stand out from the crowd, your project needs… marketing. If that term makes you shudder, or you simply don’t think you know how, this talk (aimed at anyone involved in open source) will help you understand the why and the how of open source marketing.
GraphQL is an open standard that lets you request, change, and subscribe to the exact data you need in a single network request. This makes prototyping and building data-intensive applications as simple as writing a few lines of code. In this session we’ll introduce the core concepts of GraphQL and put that into practice with real-world implementations using tools such as AWS Lambda and AWS AppSync to deliver real-time collaborative experiences for web and mobile apps, using multiple data sources, managing off-line users’ data, and resolving data conflicts.
GraphQL is an open standard that lets you request, change, and subscribe to the exact data you need in a single network request. This makes prototyping and building data-intensive applications as simple as writing a few lines of code. In this session we’ll introduce the core concepts of GraphQL and put that into practice with real-world implementations using tools such as AWS Lambda and AWS AppSync to deliver real-time collaborative experiences for web and mobile apps, using multiple data sources, managing off-line users’ data, and resolving data conflicts.
This document discusses the need for identifiers for things on the web to be attributable, discoverable, and allow for declarations of equivalences. It presents Subj3ct as a core model for subject declarations, equivalence statements, and resource statements with provenance that can help creators and consumers of linked data find existing and related identifiers through its ATOM/SKOS feeds, REST API, and web UI. Subj3ct aims to make mash-ups easier, applications smarter, and expand the knowledge base by helping connect linked data.
by J. Bako, Solutions Architect, AWS
Graph databases are purpose-built to store and navigate relationships. They have advantages for many use cases: social networking, recommendation engines, fraud detection, and others where you need to create relationships between data and quickly query these relationships. Amazon Neptune is a fast, reliable, fully-managed graph database service that makes it easy to build and run applications that work with highly connected datasets. We’ll discuss when you should use a graph database and look at how to use Neptune.
Graph databases are purpose-built to store and navigate relationships. They have advantages for many use cases: social networking, recommendation engines, fraud detection, and others where you need to create relationships between data and quickly query these relationships. Amazon Neptune is a fast, reliable, fully-managed graph database service that makes it easy to build and run applications that work with highly connected datasets. We’ll discuss when you should use a graph database and look at how to use Neptune.
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
Connecting the Unconnected using GraphDB - Tel Aviv Summit 2018Amazon Web Services
In this session, we will build on stage microservices that will be used to access in real time the data collected in the previous sessions. We will focus on Amazon Neptune, the new Graph database and Amazon Rekognition, the image recognition service.
Understanding Graph Databases: AWS Developer Workshop at Web SummitAmazon Web Services
Understanding Graph Databases: AWS Developer Workshop at Web Summit 2018
Speaker: Gabe Hollombe - Technical Evangelist, AWS
Different types of applications do better with different database technologies. Traditional relational databases are often our go-to choice, but if we're storing lots of relationships between data, a graph database might be a better option. In this session, we'll learn how graph databases differ from more familiar SQL and NoSQL options, and we'll review some of the use cases where graph databases really shine. I'll show how to leverage the power of the cloud to set up your own highly available, scalable, and secure graph database, and we'll run some queries on sample data to get more familiar with how to model and query data in a graph database.
NEW LAUNCH! Deep dive on Amazon Neptune - DAT318 - re:Invent 2017Amazon Web Services
Amazon Neptune is a fully managed graph database service which has been built ground up for handling rich highly connected data. Graph databases have diverse use cases across multiple industries; examples include recommendation engines, knowledge graphs, fraud detection, social networks, network management and life sciences. Amazon Neptune is open and flexible with support for Apache TinkerPop and RDF/SPARQL standards. Under the hood Neptune uses the same foundational building blocks as Amazon Aurora which gives it high performance, availability and durability. In this session, we will do a deep dive into capabilities, performance and key innovations in Amazon Neptune.
GraphQL is a query language for APIs and a runtime for fulfilling those queries. It gives clients the power to ask for exactly what they need, which makes it a great fit for modern web and mobile apps. In this talk, we explain why GraphQL was created, introduce you to the syntax and behavior, and then show how to use it to build powerful APIs for your data. We will also introduce you to AWS AppSync, a GraphQL-powered serverless backend for apps, which you can use to host GraphQL APIs and also add real-time and offline capabilities to your web and mobile apps. You can follow along if you have an AWS account – no GraphQL experience required!
Level: Beginner
Speaker: Rohan Deshpande - Sr. Software Dev Engineer, AWS Mobile Applications
Using AI for real-life data enrichment - Tel Aviv Summit 2018Amazon Web Services
In this session, we will learn how we used Amazon Machine Learning services to enrich our datasets, we will use Amazon rekognition to extract data from pictures and Amazon comprehend to get sentiments and areas of interests from posts. We'll use Amazon SageMaker built-in algorithms to easily build and train a machine learning model and deploy it into a production-ready hosted environment.
Best Practices for Designing GraphQL APIs That Scale (MOB420-R1) - AWS re:Inv...Amazon Web Services
The emergence of GraphQL over the past couple of years has changed the way people are thinking about API development and is reshaping the way engineering teams are structured by allowing front-end developers to move further up the stack and redefining traditional engineering roles. With the introduction of AWS AppSync, we can easily extend the possibilities of what GraphL is capable of, enabling a single GraphQL API to power entire applications by working with multiple data sources, microservices, and AWS Lambda functions. In this session, we show this architecture in action, walk through the design and implementation, and answer your questions as we dive deep.
Taking your Progressive Web App to the Next Level with GraphQL and AWS AppSyncAmazon Web Services
Progressive Web Apps (PWAs) are the future of web development and combine the best of web and native apps. In this session you will learn how to build PWAs on AWS then take your app to the next level with AWS AppSync. We will cover how AWS AppSync allows you to query your data using GraphQL and how it handles mutations, subscriptions, offline access, realtime communications, conflict resolution, and efficient data fetching.
by Rich Alberth, Solutions Architect, AWS
If you need to query relationships between data, you need a graph database. We’ll take a close look at Amazon Neptune, explore the differences between property graphs and RDF, then do graph data queries using Apache Tinkerpop. You’ll need a laptop with a Firefox or Chrome browser.
AWS Mobile Week at the San Francisco Loft
Introduction to GraphQL
GraphQL is a query language for APIs and a runtime for fulfilling those queries. It gives clients the power to ask for exactly what they need, which makes it a great fit for modern web and mobile apps. In this talk, we explain why GraphQL was created, introduce you to the syntax and behavior, and then show how to use it to build powerful APIs for your data. We will also introduce you to AWS AppSync, a GraphQL-powered serverless backend for apps, which you can use to host GraphQL APIs and also add real-time and offline capabilities to your web and mobile apps. You can follow along if you have an AWS account – no GraphQL experience required!
Level: Beginner
Speaker: Rohan Deshpande - Sr. Software Developer Engineer, AWS Mobile Applications
Taking your Progressive Web App to the Next Level - AWS Summit Sydney 2018Amazon Web Services
Taking your Progressive Web App to the Next Level with GraphQL and AWS AppSync
Progressive Web Apps (PWAs) are the future of web development and combine the best of web and native apps. In this session you will learn how to build PWAs on AWS then take your app to the next level with AWS AppSync. We will cover how AWS AppSync allows you to query your data using GraphQL and how it handles mutations, subscriptions, offline access, realtime communications, conflict resolution, and efficient data fetching.
Ed Lima, Associate Solutions Architect, Amazon Web Services
Database Week at the San Francicso Loft
Non-Relational Revolution
A decade ago, relational databases were used for nearly every use case. Today, new technologies are enabling a revolution in databases, creating new options for document, key: value, in-memory, search, and graph capabilities that do not use relational tables. We’ll discuss this revolution in database options and who is using them.
Level: 200
Speakers:
Smitty Weygant - Solutions Architect, AWS
Karan Desai - Solutions Architect, AWS
A decade ago, relational databases were used for nearly every use case. Today, new technologies are enabling a revolution in databases, creating new options for document, key: value, in-memory, search, and graph capabilities that do not use relational tables. We’ll discuss this revolution in database options and who is using them.
Level: 200
Speaker: Samir Karande - Sr. Manager, Solutions Architecture, AWS
If you need to query relationships between data, you need a graph database. We’ll take a close look at Amazon Neptune, explore the differences between property graphs and RDF, then do graph data queries using Apache Tinkerpop. You’ll need a laptop with a Firefox or Chrome browser.
The document discusses building real-time serverless backends using GraphQL. It describes challenges with building scalable data-driven apps, and why GraphQL may be preferable to REST for certain use cases. It then introduces AWS AppSync as a managed GraphQL service that provides real-time capabilities, offline support, and integration with database services. Key features of AWS AppSync discussed include subscriptions, resolvers, authentication, and support for rich content like images. Finally, it demos AWS AppSync in action.
Connecting the dots - How Amazon Neptune and Graph Databases can transform yo...Amazon Web Services
This document discusses Amazon Neptune, a fully managed graph database service. It provides an overview of graph databases and their advantages over traditional databases for modeling connected data. It then describes Amazon Neptune's key features, like automatic scaling, high availability across Availability Zones, integration with open standards like Gremlin and SPARQL, and ease of use on AWS. Examples are given showing how to model and query graph data using Gremlin and SPARQL. Finally, it discusses Amazon Neptune's architecture and roadmap for general availability later in 2018.
AWS 인공지능 서비스와 서버리스 서비스를 이용한 동영상 분석 서비스 구축하기 (김현수/황윤상, AWS 솔루션즈 아키텍트) :: AWS D...Amazon Web Services Korea
AWS 인공지능 서비스와 서버리스 서비스를 이용한 동영상 분석 서비스 구축하기
동영상에 포함되어 있는 다양한 정보를 쉽고 빠르게 분석하는 솔루션을 구축할 수 있습니다. VOD 동영상을 S3에 업로드하면, Lambda에서 Elemental MediaConvert를 호출하여 대량의 이미지로 분할하여 S3에 저장합니다. 대량의 이미지는 AWS Lambda를 활용하여 Rekognition 서비스를 호출하여 이미지 정보를 수집합니다. 수집 결과물은 ElasticSearch에 저장하고 Kibana를 통해 시각화 할 수 있습니다.
Similar to Graph & Neptune: Database Week San Francisco (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.
1) The document discusses building a minimum viable product (MVP) using Amazon Web Services (AWS).
2) It provides an example of an MVP for an omni-channel messenger platform that was built from 2017 to connect ecommerce stores to customers via web chat, Facebook Messenger, WhatsApp, and other channels.
3) The founder discusses how they started with an MVP in 2017 with 200 ecommerce stores in Hong Kong and Taiwan, and have since expanded to over 5000 clients across Southeast Asia using AWS for scaling.
This document discusses pitch decks and fundraising materials. It explains that venture capitalists will typically spend only 3 minutes and 44 seconds reviewing a pitch deck. Therefore, the deck needs to tell a compelling story to grab their attention. It also provides tips on tailoring different types of decks for different purposes, such as creating a concise 1-2 page teaser, a presentation deck for pitching in-person, and a more detailed read-only or fundraising deck. The document stresses the importance of including key information like the problem, solution, product, traction, market size, plans, team, and ask.
This document discusses building serverless web applications using AWS services like API Gateway, Lambda, DynamoDB, S3 and Amplify. It provides an overview of each service and how they can work together to create a scalable, secure and cost-effective serverless application stack without having to manage servers or infrastructure. Key services covered include API Gateway for hosting APIs, Lambda for backend logic, DynamoDB for database needs, S3 for static content, and Amplify for frontend hosting and continuous deployment.
This document provides tips for fundraising from startup founders Roland Yau and Sze Lok Chan. It discusses generating competition to create urgency for investors, fundraising in parallel rather than sequentially, having a clear fundraising narrative focused on what you do and why it's compelling, and prioritizing relationships with people over firms. It also notes how the pandemic has changed fundraising, with examples of deals done virtually during this time. The tips emphasize being fully prepared before fundraising and cultivating connections with investors in advance.
AWS_HK_StartupDay_Building Interactive websites while automating for efficien...Amazon Web Services
This document discusses Amazon's machine learning services for building conversational interfaces and extracting insights from unstructured text and audio. It describes Amazon Lex for creating chatbots, Amazon Comprehend for natural language processing tasks like entity extraction and sentiment analysis, and how they can be used together for applications like intelligent call centers and content analysis. Pre-trained APIs simplify adding machine learning to apps without requiring ML expertise.
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.