Many things were different this year at AWS’ global customer event. It was extended from one to three weeks, with five key notes and more than 500 technical sessions. And instead of hosting 65,000 attendees onsite in Las Vegas as last year, AWS welcomed over 500,000 registered attendees at their virtual event.
But many things also felt familiar. During the key notes and breakout sessions, an impressive number of new announcements was embedded in customer presentations and thoughts from AWS managers about transformation and about ensuring resilience in the face of unexpected circumstances.
All news can be followed in detail on the AWS channels. Here, only a few announcements related to the AWS cloud platform are to be highlighted as examples. The second part of this blog post focuses more on AWS’ go-to-market and partner strategy, as well as client case studies.
AWS aims at “remaining the platform with the broadest and deepest set of tools for today’s builders, providing the right tools for the right job”.
Accordingly, the number of new announcements remains impressive, and many were, as usual at AWS re:Invent, addressing developers and administrators. To pick out just a few:
AWS brings Apple's macOS operating system to the cloud. MacOS can now also be launched in the Amazon Elastic Compute Cloud. This should enable developers to program their own iOS or Mac apps.
Amazon Location Service helps developers easily add location data to their applications, including maps, points of interest, geocoding, geofences, and tracking for applications, using data from global providers Esri and HERE.
AWS Launch Wizard offers a guided way of sizing, configuring, and deploying AWS resources for third-party applications. AWS further extended the service in November 2020 and it now allows users to install SAP application software as part of the deployment process, with support for SAP NetWeaver on HANA, SAP S/4HANA, and SAP BW/4HANA.
AWS Proton is a new managed deployment service dedicated to the automation and development and deployment of container and serverless applications.
AWS also announced a number of news related to the cloud infrastructure, such as new Amazon EC2 instances, EBS volume types and AWS-designed microchips, which all aim at providing the best price performance for specific use cases.
Price performance optimization was generally a major topic. The announced AWS Cost Anomaly Detection, e.g., is a free service that monitors users' spending patterns and leverages machine learning to detect anomalous spend and provide root cause analysis, helping to enhance cost control. Another example, which should help to significantly reduce costs is that serverless computing platform AWS Lambda will be billed per millisecond instead of per 100 milliseconds.
Management & governance remains an important area of new developments, particularly for heterogeneous environments. With AWS Systems Manager Change Manager, for instance, users can use predefined change workflows to help automate approvals and avoid unintentional results when making operational changes. AWS Audit Manager is a new service that helps users continuously audit their AWS usage to simplify how they assess risk and compliance with regulations and industry standards.
And with the introduction of APIs for the AWS Well-Architected Tool customers now can build their own integrations to support a broad range of use cases, including the ability to integrate AWS Well-Architected Tool data into centralized reporting tools, and integrate identified risks with ticketing and program management systems.
In addition, many new features were announced in the focus areas IoT, analytics and machine learning. E.g., for AWS IoT Core and AWS IoT SiteWise, for AWS’ data warehouse Amazon Redshift, as well as for the advanced preparation of data lakes on AWS. AWS also announced various new capabilities for its machine learning service Amazon SageMaker – that build on the more than 50 new capabilities that AWS has delivered only in the past year. This includes faster data preparation with Amazon SageMaker Data Wrangler, a purpose-built repository for prepared data with Amazon SageMaker Feature Store, workflow automation with Amazon SageMaker Pipelines, greater transparency into training data through Amazon SageMaker Clarify, or model monitoring on edge devices with Amazon SageMaker Edge Manager.
Machine learning for the contact center: Covid-19 has brought call centers into sharper focus. Amazon Connect is a scalable call center service that can support up to ten thousand agents simultaneously. AWS announced new machine learning-based capabilities for Amazon Connect to help contact center agents create more personalized, efficient, and effective customer interactions. These include providing real-time information for problem resolution, a unified profile of customers for more personalized service, sentiment analysis, user recognition based on voice analysis, and automated workflows.
Usually, established AWS services are further developed, and maybe supplemented by additional features, without putting version numbers to the product brands. But for some solutions, recent releases are considered so significant that the vendor decided for a versioning:
“AWS IoT Greengrass 2.0” provides an open source edge runtime, a modular set of pre-built software components, tools for local software development, and new capabilities for managing software on large fleets of devices.
Aurora Serverless is an on-demand, auto-scaling configuration for the Amazon Aurora database. The announced “Amazon Aurora Serverless v2” will provide the ability to scale database workloads from hundreds to hundreds of thousands of transactions in a fraction of a second. It addresses, e.g., enterprises with a particularly large number of applications that want to manage database capacity across the entire fleet, or Software as a Service (SaaS) vendors that have multi-tenant environments with hundreds or thousands of databases. Part two of our analysis of the event can be found here.