AWS latest – Amplify Flutter, Lex and Macie

As ever, there’s lots happening on the AWS front, so here’s a quick snapshot of the some of the most significant announcements in recent weeks.

Firstly, AWS has announced the general availability of Amazon Flutter, a new version of AWS Amplify that provides support for the Google Flutter software development kit.
AWS Amplify is a set of tools and services for building secure, scalable mobile and web applications, and already supports cross-platforms like iOS, Android and JavaScript.

Flutter is also a toolkit for building natively compiled mobile, web and desktop applications from a single code base and is one of the fastest-growing mobile frameworks.

Amazon Flutter was originally previewed back in August 2020, but this new version comes with a number of additional features, specifically:

• The addition of a GraphQL API with GraphQL API and REST APIs backed by the AWS AppSync
• The introduction of the Amplify DataStore that enables users to work with shared and distributed data for both offline and online scenarios, without having to write more code
• Support for authenticating app users using Hosted UI. Hosted UI is a customisable OAuth 2.0 flow that allows developers to launch a login screen without embedding the SDK for Cognito or a social provider - such as Facebook and Google - in their application

Martin Beeby, a principal advocate for AWS confirmed that Amazon Flutter is intended for “…customers who have invested in the Flutter ecosystem and now want to take advantage of the power of AWS”.

There’s also a range of new features announced for Amazon Lex, including a new console experience that makes it easier to build, deploy and manage conversational experiences, together with a number of new APIs.

Amazon Lex is a service for building conversational interfaces into any application and the enhanced management console and APIs make it easier to build, deploy and manage virtual agents, conversational IVR systems, self-service chatbots or informational bots.

In particular, you can add a new language to a bot at any time and manage all the languages through the lifecycle of design, test and deployment as a single resource. The new console experience allows you to quickly move between different languages to compare and refine your conversations.

And there are additional builder productivity tools and capabilities to give you more flexibility and control of your bot design process. You can now save partially completed work whilst you develop different bot elements as you script, test and tune your configuration.

One particularly interesting development is a new streaming conversation API. Natural conversations are punctuated with pauses and interruptions, and previously you had to configure client attributes and implement Lambda code to handle these. With the streaming conversation API, you can pause a conversation and handle interruptions directly as you configure the bot. Overall, the design and implementation of the conversation is simplified and easier to manage.

Elsewhere, a series of new capabilities have been added to Amazon Macie to make it easier to configure, scope and run sensitive data discovery in AWS. Amazon Macie is a fully managed data security and data privacy service that uses machine learning and pattern matching to discover and protect sensitive data in AWS.

Perhaps the most significant of these new capabilities is the support for scanning jobs that cover Amazon S3 buckets residing across multiple accounts and the scoping of scans by object prefix. Together these mean that a single scanning job can now be configured to cover Amazon S3 buckets that span multiple AWS accounts and the evaluation of objects can be scoped down by prefix or prefixes of interest within a bucket.

In addition, there is now better cost estimation for visibility of spend before jobs are submitted. The new cost estimation takes into account supported object types, compressed files and the Macie volume discount pricing tiers.

And furthermore, sensitive data location information can now be added to Macie findings. This allows for the identification of sensitive data within objects using details such as line numbers, page numbers, record index, or column and row numbers, all contributing to an enhanced discovery service.