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differenttimemachinecrusade · 3 months ago

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FIDO Authentication Market Size, Share, and Business Outlook 2032

The FIDO Authentication Market Size was USD 1.5 billion in 2023 and is expected to Reach USD 9.90 billion by 2032 and grow at a CAGR of 23.33% over the forecast period of 2024-2032.

FIDO Authentication Market is expanding rapidly as organizations shift toward passwordless authentication solutions to enhance security and user experience. The rise in cyber threats, phishing attacks, and data breaches has accelerated the demand for FIDO (Fast Identity Online) standards. Leading companies and governments are adopting FIDO-based authentication to eliminate reliance on traditional passwords.

FIDO Authentication Market continues to gain traction as businesses prioritize secure and frictionless authentication methods. With biometric authentication, hardware security keys, and mobile-based authentication becoming mainstream, FIDO protocols provide a robust framework for ensuring password-free security. The adoption of FIDO2 and WebAuthn standards is driving the market forward, reducing authentication vulnerabilities and improving compliance with regulatory requirements.

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Market Keyplayers:

Yubico (YubiKey)

Auth0 (Identity Platform)

Okta (Okta Adaptive Authentication)

Gemalto (Thales) (SafeNet Trusted Access)

Microsoft (Windows Hello)

Google (Google Authenticator)

Apple (Apple Face ID, Touch ID)

Mastercard (ID Check Mobile)

Amazon Web Services (AWS) (AWS Cognito)

Ping Identity (PingOne)

SecureAuth (SecureAuth Identity Platform)

Ubisoft (Ubisoft Connect Authentication)

Duo Security (Duo Push)

(RSA SecurID)

BioConnect (BioConnect Authentication Platform)

Vasco Data Security (Digipass for FIDO)

OneLogin (OneLogin Adaptive Authentication)

Trustonic (Trustonic Secure User Authentication)

Deep Identity (Deep Identity Authentication Services)

HID Global (HID Approve, FIDO2 Authentication)

Market Trends Driving Growth

1. Increasing Adoption of Passwordless Authentication

Organizations are moving away from traditional passwords due to security concerns. FIDO authentication leverages biometrics, public key cryptography, and hardware-based security to provide a seamless login experience.

2. Expansion of Multi-Factor Authentication (MFA)

With cyberattacks on the rise, businesses are integrating FIDO-based authentication into their MFA strategies. This approach enhances security by combining biometric authentication with cryptographic keys.

3. Government Regulations and Compliance

Regulatory bodies are pushing for stronger authentication frameworks. FIDO standards align with global compliance requirements such as PSD2, GDPR, and Zero Trust security models.

4. Growth in Enterprise and Consumer Adoption

Enterprises, financial institutions, and tech companies are implementing FIDO authentication to enhance security while improving user convenience. Consumers are also embracing passwordless authentication through mobile devices and security keys.

Enquiry of This Report: https://www.snsinsider.com/enquiry/3779

Market Segmentation:

By Component

FIDO Authentication Devices

FIDO Authentication SDKs

FIDO Client SDK

FIDO Server SDK

Services

Technology Consulting

Integration & Deployment

FIDO Certification Services

Support Services

By Application

Payment Processing

PKI/Credential Management

Document Signing

User Authentication

Others

By Industry Vertical

BFSI

Healthcare & Life Sciences

IT & Telecom

Retail & CPG

Government & Defense

Energy & Utilities

Market Analysis and Current Landscape

Key factors influencing market growth include:

Rise in Cyber Threats: As phishing and credential theft increase, businesses are seeking more secure authentication solutions.

Advancements in Biometrics and Hardware Security: Fingerprint, facial recognition, and hardware-based authentication (e.g., YubiKey) are gaining widespread adoption.

Tech Giants Driving Adoption: Companies like Google, Microsoft, and Apple are integrating FIDO authentication into their platforms, making it a global standard.

Regulatory Push for Strong Authentication: Governments worldwide are enforcing stronger authentication measures to mitigate cyber risks.

Despite the positive outlook, challenges such as interoperability issues, integration complexities, and user adoption barriers remain. However, ongoing innovations in authentication technology are addressing these concerns.

Future Prospects: The Road Ahead

1. Expansion Across Industries

FIDO authentication is expected to witness significant adoption in banking, healthcare, e-commerce, and enterprise security as businesses prioritize cybersecurity.

2. Growth of Decentralized Authentication

The rise of decentralized identity solutions and blockchain-based authentication will further enhance the security and privacy of FIDO-based authentication systems.

3. Enhanced User Experience with AI and ML

AI-driven authentication and behavioral biometrics will refine FIDO implementations, reducing friction for users while maintaining high security.

4. Adoption of WebAuthn and FIDO2 in IoT Security

With the growing number of IoT devices, FIDO authentication will play a key role in securing connected ecosystems, preventing unauthorized access.

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Conclusion

The FIDO Authentication Market is set for exponential growth as businesses, governments, and consumers embrace passwordless authentication. With rising cyber threats and stricter regulations, FIDO standards are becoming the preferred choice for secure and user-friendly authentication. As technology advances, FIDO authentication will continue to evolve, providing a robust foundation for the future of digital security.

About Us:

SNS Insider is one of the leading market research and consulting agencies that dominates the market research industry globally. Our company's aim is to give clients the knowledge they require in order to function in changing circumstances. In order to give you current, accurate market data, consumer insights, and opinions so that you can make decisions with confidence, we employ a variety of techniques, including surveys, video talks, and focus groups around the world.

Jagney Dave - Vice President of Client Engagement

Phone: +1-315 636 4242 (US) | +44- 20 3290 5010 (UK)

#FIDO Authentication Market #FIDO Authentication Market Scope #FIDO Authentication Market Growth #FIDO Authentication Market Trends

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allenbalif · 1 year ago

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Looking to learn about AWS?

Of course! Amazon Web Services (AWS) is a comprehensive, evolving cloud computing platform provided by Amazon. It offers a wide range of services including computing power, storage solutions, networking, databases, machine learning, and more. Here's a brief overview of some key aspects of AWS:

Compute:

EC2 (Elastic Compute Cloud): Provides scalable virtual servers in the cloud, allowing you to run applications.

Lambda: A serverless computing service where you can run code in response to events without provisioning or managing servers.

Storage:

S3 (Simple Storage Service): Offers object storage that is highly scalable, secure, and durable. It's commonly used for hosting static websites, storing backups, and hosting media files.

EBS (Elastic Block Store): Provides persistent block storage volumes for use with EC2 instances.

Databases:

RDS (Relational Database Service): Managed relational databases including MySQL, PostgreSQL, Oracle, and SQL Server.

DynamoDB: A fully managed NoSQL database service, offering low-latency access to data at any scale.

Networking:

VPC (Virtual Private Cloud): Lets you provision a logically isolated section of the AWS Cloud where you can launch AWS resources in a virtual network.

Route 53: A scalable domain name system (DNS) web service designed to route end users to internet applications.

Security:

IAM (Identity and Access Management): Allows you to manage access to AWS services and resources securely.

Cognito: Provides authentication, authorization, and user management for web and mobile apps.

Machine Learning and AI:

SageMaker: A fully managed service for building, training, and deploying machine learning models.

Rekognition: A deep learning-based image and video analysis service for analyzing images and videos for content and metadata.

Developer Tools:

CodeCommit: A fully managed source control service that makes it easy for teams to host secure and highly scalable private Git repositories.

CodeDeploy: Automates software deployments to a variety of compute services such as EC2, Lambda, and ECS.

These are just a few highlights of the vast array of services AWS offers. Learning AWS involves understanding how these services work, how they integrate with each other, and best practices for deploying and managing applications in the cloud. AWS provides extensive documentation, tutorials, and training resources to help you get started. Additionally, there are many online courses, certifications, and communities where you can learn from others and deepen your understanding of AWS.

Watch Now:;- https://www.youtube.com/watch?v=bYYAejIfcNE

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seotechpearl · 5 years ago

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Integrating Okta as a SAML identity provider in an Amazon Cognito user pool

Posted on March 12, 2020, by Sandeep

Amazon Cognito user pool allows users to sign-in through an external identity provider (federation), such as Okta. A user pool integrated with Okta allows users in your Okta app to get user pool tokens from Amazon Cognito and authenticate apps that use Cognito.

Prerequisites 1. Cognito user pool created in the AWS account 2. Okta developer account

Steps 1. Create an app client in your user pool

a. When adding an app client, clear the Generate client secret checkbox. b. Select the required auth flows Select at least ALLOW_USER_SRP_AUTH and ALLOW_REFRESH_TOKEN_AUTH

Note: We are using the “Authorization code grant” authentication flow which does not require client secrets.

2. Create your Cognito user pool domain

a. Under the App Integration tab in the user pool console side menu, choose Domain name b. Enter your choice of unique domain prefix which is used to build Cognito login url for your SAML app.

3. Create a SAML app in Okta

a. Sign in to okta with admin access credentials and click on the “Admin” button on top right corner b. On the admin, menu chooses Applications and then choose Add Application. c. On the Add Application page, choose to Create a New App. d. In the Create a New Application Integration dialog, confirm that Platform is set to Web. e. For the Sign-on method, choose SAML 2.0. f. Choose to Create, this will open a General Settings page for saml app.

(i) Enter a name for your app. (ii) (Optional) Upload a logo and choose the visibility settings for your app. (iii) Choose Next.

(iv) Under GENERAL, for Single sign-on URL, enter https://yourDomainPrefix.auth.region.amazoncognito.com/saml2/idpresponse.

Note: Replace yourDomainPrefix and region with the values for your user pool. Find these values in the Amazon Cognito console on the Domain name page for your user pool.

(v) For Audience URI (SP Entity ID), enter urn:amazon:Cognito:sp:yourUserPoolId.

Note: Replace yourUserPoolId with your Amazon Cognito user pool ID. Find it in the Amazon Cognito console on the General settings page for your user pool.

(vi) Under ATTRIBUTE STATEMENTS (OPTIONAL- specifies which user attributes you want to export to Cognito), add a statement with the following information:

For Name, enter the SAML attribute name (attribute name by which you want to user attributes to be passed to Cognito in SAML response).

eg: if you want to pass user email in SAML response and you want to call it ‘Email Id’ enter ‘Email id’ in Name field and enter ‘user.email’ in Value field.

(vii) For all other settings on the page, leave them as their default values or set them according to your preferences. (viii) Choose Next. (ix) Choose a feedback response for Okta Support. (x) Choose Finish.

4. Assign a user to your Okta application

a. On the Assignments tab for your Okta app, for Assign, choose Assign to People. b. Next to the user that you want to assign, choose Assign.

Note: If this is a new account, the only option available is to choose yourself (the admin) as the user.

c. (Optional) For User Name, enter a user name, or leave it as the user’s email address, if you want. d. Choose Save and Go Back. Your user is assigned. e. Choose Done.

5. Get the IdP metadata for your Okta application

a. On Okta developer console, navigate to the Applications tab and select your application. b. On the Sign-On tab, find the Identity Provider metadata hyperlink (Look for text ‘Identity Provider metadata is available if this application supports dynamic configuration.’). Right-click the hyperlink (Identity Provider metadata), and then copy the URL.

6. Configure Okta as a SAML IdP in your user pool

a. In the Amazon Cognito console, choose to Manage user pools, and then choose your user pool. b. In the left navigation pane, under the Federation, choose Identity providers. c. Choose SAML. d. Under the Metadata document, paste the Identity Provider metadata URL that you copied. e. For Provider name, enter Okta. f. (Optional) Enter any SAML identifiers (Identifiers (Optional)) and enable sign-out from the IdP (Okta) when your users sign out from your user pool (Enable IdP sign out flow). g. Choose Create provider.

7. Map SAML attributes to user pool attributes

a. In the Amazon Cognito console, choose Manage user pools, and then choose your user pool. b. In the left navigation pane, under the Federation, choose Attribute mapping. c. On the attribute mapping page, choose the SAML tab. d. Choose Add SAML attribute. e. For the SAML attribute, enter the SAML attribute name (attribute name which you have mentioned in ATTRIBUTE STATEMENTS while creating the SAML app in Okta.

eg: we had specified ‘Email Id’ in the example above so mention ‘Email Id’ under SAML attribute and select Email under User pool attribute, continue and map all other attributes if you had mentioned any other in ATTRIBUTE STATEMENTS and Choose Save changes.

Note: Make sure all mapped attributes in cognito are readable and writable, you can set attribute permissions in General settings->App clients->Set read and write permissions.

8. Change app client settings for your user pool

a. In the Amazon Cognito console, choose Manage user pools, and then choose your user pool. b. In the left navigation pane, under App integration, choose App client settings. c. On the app client page, do the following: Under Enabled Identity Providers, select the Okta and Cognito User Pool checkboxes. For Callback URL(s), enter a URL where you want your users to be redirected after they log in. For testing, you can enter any valid URL, such as https://www.example.com/. For Sign out URL(s), enter a URL where you want your users to be redirected after they log out. For testing, you can enter any valid URL, such as https://www.example.com/. Under Allowed OAuth Flows, be sure to select Authorization code grant checkbox. Under Allowed OAuth Scopes, be sure to select the email and profile checkboxes. d. Choose Save changes.

9. Construct the endpoint URL

a. Using values from your user pool, construct this login endpoint URL: https://yourDomainPrefix.auth.region.amazoncognito.com/login?response_type=code&client_id=yourClientId&redirect_uri=redirectUrl b. Be sure to do the following:

(i) Replace yourDomainPrefix and region with the values for your user pool. Find these values in the Amazon Cognito console on the Domain name page for your user pool. (ii) Replace yourClientId with your app client’s ID, and replace redirectUrl with your app client’s callback URL. Find these in the Amazon Cognito console on the App client settings page for your user pool.

10. Test the endpoint URL

a. Enter the constructed login endpoint URL in your web browser. b. On your login endpoint webpage, choose Okta.

Note: If you’re redirected to your app client’s callback URL, you’re already logged in to your Okta account in your browser. The user pool tokens appear in the URL in your web browser’s address bar.

c. On the Okta Sign In page, enter the username and password for the user that you assigned to your app.

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nearmesblog · 4 years ago

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AWS Cognito is one of the offerings that had at the Amazon cloud app development platform. Using this provider without any difficulty can join it with different to be had offerings on AWS along with API Gateway, AppSync, or Lambdas. It can keep flutter development personal information like given name, own circle of relatives name, electronic mail, username, password, and some other information which your customers generally preserve within the web development or mobile software development.

📷

AWS cognito

If you need to permit customers check in and log into your app developers page, keep their information private, and a number of the capabilities like two-step authentication or password procedure, this app development provider is something well worth the use. Not best does it will let you keep away from writing a backend service, however, it additionally calls for less code at the frontend.

Getting started

Connection with AWS Cognito provider at the software development aspect may be accomplished with the aid of using uploading the AWS Amplify module, to be had to download as an NPM module. It offers your software development the cap potential to connect to the AWS Cognito provider and put in force the local person interface paperwork for authentication, password healing, etc.

AWS cognito pros

Fully configurable through the AWS manage panel.

Easy to connect to your software developers thru furnished AWS Amplify module.

No want for a further worldwide nation control answer on your app development. AWS Amplify will take a look at if the person is already logged for you. It presents its very own worldwide nation which may be used throughout the complete flutter development.

Out-of-the-field, UI paperwork for logging in, registration, password healing, password alternate, federated authentication, MFA.

All the information may be mechanically saved in the cloud AWS Cognito provider.

Confirmation emails may be mechanically despatched to the person in addition to textual content messages to confirm the phone quantity.

You can keep custom attributes for customers like address, phone quantity, town, and any custom discipline that you need.

Data despatched from the app developers is already encrypted and secured with the aid of using the AWS Amplify module.

Easy to connect to different AWS offerings like AWS AppSync.

UI Form validations controlled with the aid of using AWS manage panel.

Integration with Social identification vendors eg, “Log in with Google”.

Error messages already furnished with the aid of using web development provider.

Several approaches to deal with paperwork at the software development aspect.

AWS Amplify permits for making HTTP requests.

Out of the field protection capabilities like throttling or refresh tokens.

AWS cognito cons

It’s a paid solution.

Documentation is hardly ever up to date and now no longer a great deal detail.

Some of the alternatives may be best set for the duration of the introduction of an AWS Cognito person pool within the AWS management panel. After that, the alternatives are disabled, and in case you need to alternate them you want to delete the complete example and create a brand.

Aligning furnished UI paperwork with a number of the web designers can occasionally be problematic, then probable the quickest manner is to create your very own shape components.

Error messages furnished by using AWS Cognito aren’t very person-pleasant. Sometimes they’re too technical so that you want to offer a few types of a mistakes mapper within the flutter development, to reveal greater person-pleasant messages. For instance whilst a person attempts to log in with the incorrect password.

There aren’t any mistakes messages for precise shape fields, the best well-known mistakes messages.

Confirmation emails are very limited. You want to create custom HTML electronic mail templates in case you need greater than only undeniable textual content electronic mail with a verification link.

There are obstacles withinside the number of custom discipline attributes. You can’t create greater than 25 custom attributes.

AWS Amplify module is a touch heavy (minified + gzipped model is round a hundred and eighty kB).

Conclusion

Using AWS Cognito for authentication at the side of AWS amplify at the software development might be a solution than developing the complete authentication service on your very own. In addition, you don’t want to attend to the backend, due to the fact this can be already furnished by using AWS Cognito. But in case your software development no longer uses some other provider from AWS Cognito, connecting it with third-party offerings will make an effort and require custom web development solutions.

#sataware #softwaredevelopers

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aws101training · 4 years ago

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My Experience, Feedback, And Tips for the AWS Developer Associate Exam

I passed the AWS Certified Developer Associate test on my first try a few weeks ago. My second attempt at the trial was having previously obtained AWS Developer Associate certification around four years ago. Since then, the test has undoubtedly altered. In the past, you could easily pass the test using knowledge from the AWS Certified Solutions Architect Associate and a few extra facts about Amazon DynamoDB. The latest exam is unquestionably more developer-focused, with questions evaluating your grasp of Developer Tools, development best practices, and code examples.

I still don't think you need a solid background in development to pass this exam. Indeed, my experience is in Enterprise Solutions Architecture rather than programming. You will need to comprehend some basic code examples, such as JSON code, configuration files, and IAM policy documents, although these are pretty simple. This is a credential that anybody, regardless of background, may obtain with the proper training.

In this post, I'll go through the test blueprint, provide some input from my exam experience, and offer some advice on effectively preparing for this exam. Of course, all exam takers must sign a non-disclosure agreement (NDA) that precludes me from discussing exam subject specifics.

General Information About the Exam:

· AWS Certified Developer Associate is an abbreviation for Amazon Web Services Certified Developer Associate

· The length of the questionnaire is 65 questions.

· Duration: 130 minutes

· Passing grade: 720 points out of a possible 1000 points.

· On the test, there are two sorts of questions:

· Multiple-choice: There is one correct answer and three wrong answers (distractors)

· Multiple response: Has two or more accurate replies from a set of five or more possibilities. (I only saw four of them out of 65 questions.)

· Exam Guide for the AWS Certified Developer Associate

What should I study?

Here is a list of services and some crucial subjects I believe you should learn before taking the exam. There is a strong emphasis on serverless application development, so make sure you are well-versed in the first five services listed below.

Listed in ascending order of significance (the first five services made up over 50 percent of my exam)

AWS LAMBDA (10–15 % OF THE EXAM)

Types of invocation

· Using event source mappings and notifications

· X-Ray concurrency and throttling, as well as Amazon SQS DLLs

· Aliases and variants

· The use of blue and green deployment

· VPC connection packaging and deployment (with Internet/NAT GW)

· ELB target Lambda

· Dependencies

DYNAMODB AMAZON (10 percent OF THE EXAM)

· Query vs. Scans (and the APIs, parameters you can use)

· Secondary indices at the local and global levels

· Calculating RCUs (Read Capacity Units) and WCUs (Write Capacity Units) (WC)

· Best techniques for performance and optimization

· Case studies (for example, session state, key/value data storage, and scalability)

· Streams in DynamoDB

· Use with Lambda and API Gateway in a serverless app.

· Use cases for DynamoDB Accelerator (DAX)

AMAZON API GATEWAY (8%–10% OF THE EXAM)

· Authorizers for Lambda, IAM, and Cognito

· Cache invalidation

· Types of integration: proxy vs. bespoke / AWS vs. HTTP Caching

· importing and exporting Swagger standards for OpenAPI

· Variables at each stage

· Metrics of performance

AWS COGNITO (7%–8% OF THE EXAM)

· Identity pools vs. User pools

· An identity that has not been authenticated

· AWS Cognito Sync with Cognito Web Identity Federation Using MFA

AMAZON S3 (7%–8% OF THE EXAM)

· Encryption – needs to unmake sure you understand S3 encryption well before taking the exam!

· Versioning Data Copying Data Lifecycle Rules S3 Transfer Acceleration

IDENTITY AND ACCESS MANAGEMENT (IAM) ON AMAZON IAM POLICIES AND ROLES

· Access to multiple accounts

· API calls for multi-factor authentication (MFA)

· IAM Roles in EC2 (instance profiles)

· Roles vs. access keys

· Federation of IAM Best Practices

ELASTIC CONTAINER SERVICE ON AMAZON (ECS)

· Containers share storage space.

· Single-docker environments vs. multi-docker environments

· Using ECR to upload and download pictures

· Methods of placement (e.g., spread, binpack, random etc.)

· Mappings of ports

· Task definitions must be defined.

· Task Roles in IAM

ELASTIC BEANSTALK ON AMAZON

· Blue/green deployment policies

· extensions and configuration files

· Deployment updates

· Worker tier vs. web tier

· Deployment, packaging, and files, as well as code and commands, are all utilized.

· Case studies

CLOUD FORMATION ON AWS

· Anatomy of a CloudFormation template (e.g., mappings, outputs, parameters, etc.)

· AWS Serverless Application Model was utilized for packaging and deployment, including instructions (SAM)

#AWS Developer Associate Exam #AWS Certified Solutions Architect Associate #aws certified developer associate

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globalmediacampaign · 4 years ago

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Supercharge your knowledge graph using Amazon Neptune, Amazon Comprehend, and Amazon Lex

Knowledge graph applications are one of the most popular graph use cases being built on Amazon Neptune today. Knowledge graphs consolidate and integrate an organization’s information into a single location by relating data stored from structured systems (e.g., e-commerce, sales records, CRM systems) and unstructured systems (e.g., text documents, email, news articles) together in a way that makes it readily available to users and applications. In reality, data rarely exists in a format that allows us to easily extract and connect relevant elements. In this post we’ll build a full-stack knowledge graph application that demonstrates how to provide structure to unstructured and semi-structured data, and how to expose this structure in a way that’s easy for users to consume. We’ll use Amazon Neptune to store our knowledge graph, Amazon Comprehend to provide structure to semi-structured data from the AWS Database Blog, and Amazon Lex to provide a chatbot interface to answer natural language questions as illustrated below. Deploy the application Let’s discuss the overall architecture and implementation steps used to build this application. If you want to experiment, all the code is available on GitHub. We begin by deploying our application and taking a look at how it works. Our sample solution includes the following: A Neptune cluster Multiple AWS Lambda functions and layers that handle the reading and writing of data to and from our knowledge graph An Amazon API Gateway that our web application uses to fetch data via REST An Amazon Lex chatbot, configured with the appropriate intents, which interacts with via our web application An Amazon Cognito identity pool required for our web application to connect to the chatbot Code that scrapes posts from the AWS Database blog for Neptune, enhances the data, and loads it into our knowledge graph A React-based web application with an AWS Amplify chatbot component Before you deploy our application, make sure you have the following: A machine running Docker, either a laptop or a server, for running our web interface An AWS account with the ability to create resources With these prerequisites satisfied, let’s deploy our application: Launch our solution using the provided AWS CloudFormation template in your desired Region: us-east-1 us-west-2 Costs to run this solution depend on the Neptune instance size chosen with a minimal cost for the other services used. Provide the desired stack name and instance size. Acknowledge the capabilities and choose Create stack. This process may take 10–15 minutes. When it’s complete, the CloudFormation stack’s Outputs tab lists the following values, which you need to run the web front end: ApiGatewayInvokeURL IdentityPoolId Run the following command to create the web interface, providing the appropriate parameters: docker run -td -p 3000:3000 -e IDENTITY_POOL_ID= -e API_GATEWAY_INVOKE_URL= -e REGION= public.ecr.aws/a8u6m715/neptune-chatbot:latest After this container has started, you can access the web application on port 3000 of your Docker server (http://localhost:3000/). If port 3000 is in use on your current server, you can alter the port by changing -p :3000. Use the application With the application started, let’s try out the chatbot integration using the following phrases: Show me all posts by Ian Robinson What has Dave Bechberger written on Amazon Neptune? Have Ian Robinson and Kelvin Lawrence worked together Show me posts by Taylor Rigg (This should prompt for Taylor Riggan; answer “Yes”.) Show me all posts on Amazon Neptune Refreshing the browser clears the canvas and chatbox. Each of these phrases provides a visual representation of the contextually relevant connections with our knowledge graph. Build the application Now that we know what our application is capable of doing, let’s look at how the AWS services are integrated to build a full-stack application. We built this knowledge graph application using a common paradigm for developing these types of applications known as ingest, curate, and discover. This paradigm begins by first ingesting data from one or more sources and creating semi-structured entities from it. In our application, we use Python and Beautiful Soup to scrape the AWS Database blog website to generate and store semi-structured data, which is stored in our Neptune-powered knowledge graph. After we extract these semi-structured entities, we curate and enhance them with additional meaning and connections. We do so using Amazon Comprehend to extract named entities from the blog post text. We connect these extracted entities within our knowledge graph to provide more contextually relevant connections within our blog data. Finally, we create an interface to allow easy discovery of our newly connected information. For our application, we use a React application, powered by Amazon Lex and Amplify, to provide a web-based chatbot interface to provide contextually relevant answers to the questions asked. Putting these aspects together gives the following application architecture. Ingest the AWS Database blog The ingest portion of our application uses Beautiful Soup to scrape the AWS Database blog. We don’t examine it in detail, but knowing the structure of the webpage allows us to create semi-structured data from the unstructured text. We use Beautiful Soup to extract several key pieces of information from each post, such as author, title, tags, and images: { "url": "https://aws.amazon.com/blogs/database/configure-amazon-vpc-for-sparql-1-1-federated-query-with-amazon-neptune/", "img_src": "https://d2908q01vomqb2.cloudfront.net/887309d048beef83ad3eabf2a79a64a389ab1c9f/2020/10/02/VPCforSPARQL1.1.FederatedQueryNeptune2.png", "img_height": "395", "img_width": "800", "title": "Configure Amazon VPC for SPARQL 1.1 Federated Query with Amazon Neptune", "authors": [ { "name": "Charles Ivie", "img_src": "https://d2908q01vomqb2.cloudfront.net/887309d048beef83ad3eabf2a79a64a389ab1c9f/2020/08/06/charles-ivie-100.jpg", "img_height": "132", "img_width": "100" } ], "date": "12 OCT 2020", "tags": [ "Amazon Neptune", "Database" ], "post": "" } After we extract this information for all the posts in the blog, we store it in our knowledge graph. The following figure shows the what this looks like for the first five posts. Although this begins to show connections between the entities in our graph, we can extract more context by examining the data stored within each post. To increase the connectedness of the data in our knowledge graph, let’s look at additional methods to curate this semi-structured data. Curate our semi-structured data To extract additional information from our semi-structured data, we use the DetectEntity functionality in Amazon Comprehend. This feature takes a text input and looks for unique names of real-world items such as people, places, items, or references to measures such as quantities or dates. By default, the types of entities returned are provided a label of COMMERCIAL_ITEM, DATE, EVENT, LOCATION, ORGANIZATION, OTHER, PERSON, QUANTITY, or TITLE. To enhance our data, the input is required to be UTF-encoded strings of up to 5,000-byte chunks. We do this by dividing each post by paragraph and running each paragraph through the batch_detect_entities method in batches of 25. For each entity that’s detected, the score, type, text, as well as begin and end offsets are returned, as in the following example code: { "Score": 0.9177741408348083, "Type": "TITLE", "Text": "SPARQL 1.1", "BeginOffset": 13, "EndOffset": 23 } Associating each of these detected entities with our semi-structured data in our knowledge graph shows even more connections, as seen in a subset in the following graph. When we compare this to our previous graph, we see that a significant number of additional connections have been added. These connections not only link posts together, they allow us to provide additional relevant answers by linking posts based on contextual relevant information stored within the post. This brings us to the final portion of this sample application: creating a web-based chatbot to interact with our new knowledge graph. Discover information in our knowledge graph Creating a web application to discover information in our knowledge graph has two steps: defining our chatbot and integrating the chatbot into a web application. Defining our chatbot Our application’s chatbot is powered by Amazon Lex, which makes it easy to build conversational interfaces for applications. The building block for building any bot with Amazon Lex is an intent. An intent is an action that responds to natural language user input. Our bot has four different intents specified: CoAuthorPosts PostByAuthor PostByAuthorAndTag PostByTag Each intent is identified by defining a variety of short training phrases for that intent, known as utterances. Each utterance is unique and when a user speaks or types that phrase, the associated intent is invoked. The phrases act as training data for the Lex bot to identify user input and map it to the appropriate intent. For example, the PostsByAuthor intent has a few different utterances that can invoke it, as shown in the following screenshot. The best practice is to use 15–20 sample utterances to provide the necessary variations for the model to perform with optimum accuracy. One or more slot values are within each utterance, identified by the curly brackets, which represent input data that is needed for the intent. Each slot value can be required or not, has a slot type associated with it, and has a prompt that used by the chatbot for eliciting the slot value if it’s not present. In addition to configuring the chatbot to prompt the user for missing slot values, you can specify a Lambda function to provide a more thorough validation of the inputs (against values available in the database) or return potential options back to the user to allow them to choose. For our PostsByAuthor intent, we configure a validation check to ensure that the author entered a valid author in our knowledge graph. The final piece is to define the fulfillment action for the intents. This is the action that occurs after the intent is invoked and all required slots are filled or validation checks have occurred. When defining the fulfillment action, you can choose from either invoking a Lambda function or returning the parameters back to the client. After you define the intent, you build and test it on the provided console. Now that the chatbot is functioning as expected, let’s integrate it into our web application. Integrate our chatbot Integration of our chatbot into our React application is relatively straightforward. We use the familiar React paradigms of components and REST calls, so let’s highlight how to configure the integration between React and Amazon Lex. Amazon Lex supports a variety of different deployment options natively, including Facebook, Slack, Twilio, or Amplify. Our application uses Amplify, which is an end-to-end toolkit that allows us to construct full-stack applications powered by AWS. Amplify offers a set of component libraries for a variety of front-end frameworks, including React, which we use in our application. Amplify provides an interaction component that makes it simple to integrate a React front end to our Amazon Lex chatbot. To accomplish this, we need some configuration information, such as the chatbot name and a configured Amazon Cognito identity pool ID. After we provide these configuration values, the component handles the work of wiring up the integration with our chatbot. In addition to the configuration, we need an additional piece of code (available on GitHub) to handle the search parameters returned as the fulfillment action from our Amazon Lex intent: useEffect(() => { const handleChatComplete = (event) => { const { data, err } = event.detail; if (data) { props.setSearchParameter(data["slots"]); } if (err) console.error("Chat failed:", err); }; const chatbotElement = document.querySelector("amplify-chatbot"); chatbotElement.addEventListener("chatCompleted", handleChatComplete); return function cleanup() { chatbotElement.removeEventListener("chatCompleted", handleChatComplete); }; }, [props]); With the search parameters for our intent, we can now call our API Gateway as you would for other REST based calls. Clean up your resources To clean up the resources used in this post, use either the AWS Command Line Interface (AWS CLI) or the AWS Management Console. Delete the CloudFormation template that you used to configure the remaining resources generated as part of this application. Conclusion Knowledge graphs—especially enterprise knowledge graphs—are efficient ways to access vast arrays of information within an organization. However, doing this effectively requires the ability to extract connections from within a large amount of unstructured and semi-structured data. This information then needs to be accessible via a simple and user-friendly interface. NLP and natural language search techniques such as those demonstrated in this post are just one of the many ways that AWS powers an intelligent data extraction and insight platform within organizations. If you have any questions or want a deeper dive into how to leverage Amazon Neptune for your own knowledge graph use case, we suggest looking at our Neptune Workbench notebook (01-Building-a-Knowledge-Graph-Application). This workbook uses the same AWS Database Blog data used here but provides additional details on the methodology, data model, and queries required to build a knowledge graph based search solution. As with other AWS services, we’re always available through your Amazon account manager or via the Amazon Neptune Discussion Forums. About the author Dave Bechberger is a Sr. Graph Architect with the Amazon Neptune team. He used his years of experience working with customers to build graph database-backed applications as inspiration to co-author “Graph Databases in Action” by Manning. https://aws.amazon.com/blogs/database/supercharge-your-knowledge-graph-using-amazon-neptune-amazon-comprehend-and-amazon-lex/

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prevajconsultants · 5 years ago

Text

RT @albertgao: Listening to the recent @syntaxfm learned that @MongoDB stitch added GraphQL support. Exciting. Just tried it. Awesome, love to be back finally. And TBH, its auth model is by far the easiest compare to AWS Cognito, Auth0. Especially for the documentation. DB + Serverless FTW! https://t.co/iXlQAsNMuQ

Listening to the recent @syntaxfm learned that @MongoDB stitch added GraphQL support. Exciting. Just tried it. Awesome, love to be back finally. And TBH, its auth model is by far the easiest compare to AWS Cognito, Auth0. Especially for the documentation. DB + Serverless FTW! pic.twitter.com/iXlQAsNMuQ

— Albert Gao (@albertgao) March 11, 2020

MongoDB

#MongoDB #MongoDB Developer #MongoDB Development #MongoDB Application Development #MongoDB Developmen

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intellect-minds-pte-ltd · 6 years ago

Text

Urgent Requirement Head of Development and Cloud Lead in Singapore

Company Overview:

Intellect Minds is a Singapore-based company since 2008, specializing in talent acquisition, application development, and training. We are the Best Job Recruitment Agency and consultancy in Singapore serve BIG MNCs and well-known clients in talent acquisition, application development, and training needs for Singapore, Malaysia, Brunei, Vietnam, and Thailand.

Role overview:

This is a hybrid role in a leading company that combines responsibility as Lead Project Manager overseeing the development of the company’s web and app software while assuming full responsibility for the Cloud and Application infrastructure to support business and IT development needs.

Your primary responsibilities will be the following duties. Specifically:

• Technology team leader (project management, communication with senior management team, consult with potential B2B clients) • Cloud Architecture (High Availability, DR) • Cloud security (Network/Application/Database) • Cloud compliance (MAS TRM) • Ensure that the Company’s IT set up is fully compliant with the MAS Guidelines and Notice on Technology Risk Management, implementing new processes as required. IT Compliance consists of conducting and reviewing policies on business continuity and back up, and improvements to hardware and software as required, with a focus on security, data protection and scalability, as required. • Change Management • Continuous Integration/Deployment (CI/CD) • Automation • Backup & Recovery • Auditing Changes • Database design • Deploy application middleware (Apache/Tomcat) • Cloud Infrastructure monitoring & tuning • Cloud Cost optimization • Monitoring & Operations • Documenting policies and procedures around the IT systems in place and introducing best practice security measures and user controls etc.

Person Specification:

Education and Professional Qualifications: • Excellent interpersonal, communication and organizational skills • Strong attention to detail • Ability to work in a fast-paced, collaborative environment • Expert AWS Architect • Experience with Microservices, AWS Cognito, Postgres, MySQL, Apache, Tomcat Work Experience/ Skills and Knowledge • Candidates should ideally have 8 to 10 years previous experience in a similar role, preferably within a financial institution or Multi-National Corporation

All successful candidates can expect a very competitive remuneration package and a comprehensive range of benefits.

Interested Candidates, please submit your detailed resume online.

To your success!

The Recruitment Team

Intellect Minds Pte Ltd (Singapore)

#job

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awsexchage · 6 years ago

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AWS SDK for Python(boto3)でAmazon Managed Blockchainのブロックチェーンネットワークを作成してみた http://bit.ly/2QO67Y5

Amazon Managed Blockchainでブロックチェーンネットワークを構築するのにAWS CloudFormationを利用したいなぁとドキュメントを読んでみたらリソースがありませんでした。oh… AWS CloudFormationのカスタムリソースを利用すれば管理できるので、まずはAWS SDKでAmazon Managed Blockchainが取り扱えるのか��認してみました。

AWS CloudFormationのカスタムリソースについては下記が参考になります。

カスタムリソース – AWS CloudFormation https://docs.aws.amazon.com/ja_jp/AWSCloudFormation/latest/UserGuide/template-custom-resources.html

CloudFormationで提供されていない処理をカスタムリソースで作ってみた。｜ DevelopersIO https://dev.classmethod.jp/cloud/aws/cfn-api-custom/

AWS CloudFormationでCognitoユーザープールをMFAのTOTPを有効にして作成する – Qiita https://cloudpack.media/44573

Amazon Managed Blockchainってなんぞ？

Amazon Managed Blockchainってなんぞ？という方は下記をご参考ください。

Amazon Managed BlockchainがリリースされたのでHyperledger Fabricも合わせて情報をまとめてみた – Qiita https://cloudpack.media/46950

前提

今回はAmazon Managed Blockchainのネットワーク、メンバー、ノードがAWS SDKで作成できるかの確認のみとなります。セキュリティグループ、インタフェースVPCエンドポイントやHyperledger Fabricの設定などは行いません。

AWSアカウントがある

AWSアカウントに以下の作成権限がある

Managed Blockchainネットワーク

AWS CLIのaws configure コマンドでアカウント設定済み

ネットワーク構築して動作確認するまでの手順は下記が参考になります。

Amazon Managed BlockchainでHyperledger Fabricのブロックチェーンネットワークを構築してみた – Qiita https://cloudpack.media/46963

AWS SDK for Python(boto3)でAmazon Managed BlockchainのAPIが提供されてる

ドキュメントを確認してみたらAWS SDKでAmazon Managed BlockchainのAPIが提供されていました。

boto/boto3: AWS SDK for Python https://github.com/boto/boto3

ManagedBlockchain — Boto 3 Docs 1.9.152 documentation https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/managedblockchain.html

Python環境の用意

Pipenvを利用したことがなかったので、ついでに初利用します(趣味)。仮想環境じゃなくてもOKです。

Pipenvを使ったPython開発まとめ – Qiita https://qiita.com/y-tsutsu/items/54c10e0b2c6b565c887a

# pipenvがインストールされてなかったら > pip install pipenv (略) Installing collected packages: virtualenv, virtualenv-clone, pipenv Successfully installed pipenv-2018.11.26 virtualenv-16.6.0 virtualenv-clone-0.5.3 # 仮想環境の作成 > pipenv --python 3.7 Creating a virtualenv for this project… Pipfile: /Users/xxx/dev/aws/managed-blockchain-use-sdk/Pipfile Using /Users/xxx/.anyenv/envs/pyenv/versions/3.7.0/bin/python3 (3.7.0) to create virtualenv… ⠴ Creating virtual environment... (略) ✓ Successfully created virtual environment! Virtualenv location: /Users/xxx/.local/share/virtualenvs/managed-blockchain-use-sdk-ijMKrYxz Creating a Pipfile for this project… # boto3のインストール > pipenv install boto3 Installing boto3… ⠴ Installing... (略) # 仮想環境へ入る > pipenv shell Launching subshell in virtual environment… Welcome to fish, the friendly interactive shell

AWS SDK for Python(boto3)を利用して実装

ドキュメントを参考にネットワークなどを作成、取得できるか確認します。

ManagedBlockchain — Boto 3 Docs 1.9.152 documentation https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/managedblockchain.html

ネットワークとメンバーの作成

create_network でネットワーク作成できます。パラメータはAWS CLIとほぼ同じなので下記が参考になります。 AWS CLIと同じく最初のメンバーも合わせて作成する必要があります。

Amazon Managed BlockchainでHyperledger Fabricのブロックチェーンネットワークを構築してみた – Qiita https://cloudpack.media/46963

create_network.py

import boto3 client = boto3.client("managedblockchain") def create_network(): new_network = client.create_network( ClientRequestToken="string", Name="TestNetwork", Description="TestNetworkDescription", Framework="HYPERLEDGER_FABRIC", FrameworkVersion="1.2", FrameworkConfiguration={ "Fabric": { "Edition": "STARTER" } }, VotingPolicy={ "ApprovalThresholdPolicy": { "ThresholdPercentage": 50, "ProposalDurationInHours": 24, "ThresholdComparator": "GREATER_THAN" } }, MemberConfiguration={ "Name": "org1", "Description": "Org1 first member of network", "FrameworkConfiguration": { "Fabric": { "AdminUsername": "AdminUser", "AdminPassword": "Password123" } } } ) print(new_network)

実行するとネットワークとメンバーの作成が開始されてNetworkId とMemberId が得られます。

> python create_network.py {'ResponseMetadata': {'RequestId': 'xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx', 'HTTPStatusCode': 200, 'HTTPHeaders': {'date': 'Tue, 21 May 2019 03:24:32 GMT', 'content-type': 'application/json', 'content-length': '86', 'connection': 'keep-alive', 'x-amzn-requestid': 'xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx', 'x-amz-apigw-id': 'xxxxxxxxxxxxxxx=', 'x-amzn-trace-id': 'Root=1-xxxxxxxx-xxxxxxxxxxxxxxxxxxxxxxxx; Sampled=0'}, 'RetryAttempts': 0}, 'NetworkId': 'n-XXXXXXXXXXXXXXXXXXXXXXXXXX', 'MemberId': 'm-XXXXXXXXXXXXXXXXXXXXXXXXXX'}

ネットワーク情報の取得

list_networks でネットワーク情報が取得できます。パラメータ指定することで検索もできるみたいです。

list_network.py

import boto3 client = boto3.client("managedblockchain") def list_network(): networks = client.list_networks( # Name="Test" # Name="string", # Framework="HYPERLEDGER_FABRIC", # Status="CREATING"|"AVAILABLE"|"CREATE_FAILED"|"DELETING"|"DELETED", # MaxResults=123, # NextToken="string" ) print(networks) list_networks()

> python list_network.py [{'Id': 'n-XXXXXXXXXXXXXXXXXXXXXXXXXX', 'Name': 'TestNetwork', 'Description': 'TestNetworkDescription', 'Framework': 'HYPERLEDGER_FABRIC', 'FrameworkVersion': '1.2', 'Status': 'CREATING', 'CreationDate': datetime.datetime(2019, 5, 21, 3, 24, 31, 929000, tzinfo=tzutc())}]

ノードの作成

create_node でノード作成ができます。ClientRequestToken に関しては指定しない場合自動生成してくれるとのことでした。手動での生成方法は未調査です。

create_node.py

import boto3 client = boto3.client("managedblockchain") def create_node(): new_node = client.create_node( # ClientRequestToken='string', NetworkId='n-5QBOS5ULTVEZZG6EMIPLPRSSQA', MemberId='m-OMLBOQIAMJDDPJV3FVGIAVUGBE', NodeConfiguration={ 'InstanceType': 'bc.t3.small', 'AvailabilityZone': 'us-east-1a' } ) print(new_node) create_node()

> python create_node.py {'ResponseMetadata': {'RequestId': 'xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx', 'HTTPStatusCode': 200, 'HTTPHeaders': {'date': 'Tue, 21 May 2019 04:04:44 GMT', 'content-type': 'application/json', 'content-length': '42', 'connection': 'keep-alive', 'x-amzn-requestid': 'xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx', 'x-amz-apigw-id': 'xxxxxxxxxxxxxxx=', 'x-amzn-trace-id': 'Root=1-xxxxxxxx-xxxxxxxxxxxxxxxxxxxxxxxx;Sampled=0'}, 'RetryAttempts': 0}, 'NodeId': 'nd-XXXXXXXXXXXXXXXXXXXXXXXXXX'}

ネットワーク、メンバーが作成中(Status がCREATING )の場合は作成できませんでした。

ネットワーク、メンバー作成中に実行

> python create_node.py Traceback (most recent call last): File "main.py", line 43, in <module> 'AvailabilityZone': 'us-east-1a' File "/Users/xxx/.local/share/virtualenvs/managed-blockchain-use-sdk-ijMKrYxz/lib/python3.7/site-packages/botocore/client.py", line 357, in _api_call return self._make_api_call(operation_name, kwargs) File "/Users/xxx/.local/share/virtualenvs/managed-blockchain-use-sdk-ijMKrYxz/lib/python3.7/site-packages/botocore/client.py", line 661, in _make_api_call raise error_class(parsed_response, operation_name) botocore.errorfactory.ResourceNotReadyException: An error occurred (ResourceNotReadyException) when calling the CreateNode operation: Member m-XXXXXXXXXXXXXXXXXXXXXXXXXX is in CREATING. you cannot create nodes at this time..

メンバー情報の取得

list_members でメンバーが取得できます。NetworkId が必須となります。

list_members.py

import boto3 client = boto3.client("managedblockchain") def list_members(): members = client.list_members( NetworkId="n-XXXXXXXXXXXXXXXXXXXXXXXXXX" # Name="string", # Status="CREATING"|"AVAILABLE"|"CREATE_FAILED"|"DELETING"|"DELETED", # IsOwned=True|False, # MaxResults=123, # NextToken="string" ) print(members["Members"]) list_members()

> pyhton list_members.py [{'Id': 'm-XXXXXXXXXXXXXXXXXXXXXXXXXX', 'Name': 'demo', 'Description': 'demo member', 'Status': 'AVAILABLE', 'CreationDate': datetime.datetime(2019, 5, 21, 3, 24, 32, 271000, tzinfo=tzutc()), 'IsOwned': True}]

ノード情報の取得

list_nodes でノードが取得できます。NetworkId 、MemberId が必須となります。

list_nodes.py

import boto3 client = boto3.client("managedblockchain") def list_nodes(): nodes = client.list_nodes( NetworkId="n-XXXXXXXXXXXXXXXXXXXXXXXXXX", MemberId="m-XXXXXXXXXXXXXXXXXXXXXXXXXX" # Status="CREATING"|"AVAILABLE"|"CREATE_FAILED"|"DELETING"|"DELETED"|"FAILED", # MaxResults=123, # NextToken="string" ) print(nodes["Nodes"]) list_nodes()

> pyhton list_nodes.py [{'Id': 'nd-XXXXXXXXXXXXXXXXXXXXXXXXXX', 'Status': 'AVAILABLE', 'CreationDate': datetime.datetime(2019, 5, 21, 4, 4, 44, 468000, tzinfo=tzutc()), 'AvailabilityZone': 'us-east-1a', 'InstanceType': 'bc.t3.small'}]

特にハマることもなくboto3を利用してネットワーク、メンバー、ノードを作成することができました。これで、AWS CloudFormationのカスタムリソースを利用してリソース管理することができそうです。ただ、ネットワークとメンバー作成に20分程度かかるので、その待受処理などがAWS CloudFormationで実現できるのか、ちょっと心配です。(未調査)

参考