5 Important AWS re:Invent Announcements

New data governance and sharing, business intelligence, supply chain management, security, AI/ML, spatial simulation tools and capabilities — this week was a busy one at AWS re: Invent, with AWS rolling out many new services.

FREMONT, CA: AWS released many new services, including new data governance and sharing tools, business intelligence, supply chain management, security, AI/ML, and spatial simulation. The most important announcements from the annual AWS conference are listed below.

Real-world Simulation: Complicated spatial simulations demand a lot of computing power, and scaling simulations with millions of interdependent objects across compute instances can be a challenging and expensive operation.

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AWS has released AWS SimSpace Weaver to assist users in creating, maintaining, and running extensive spatial simulations. Users of the fully-managed computer service can employ spatial simulations to simulate systems with numerous data points, such as city traffic patterns, population movements in public spaces, or manufacturing floor layouts. According to AWS, they can then be utilised to carry out immersive training and gather important insights.

More than a million things people, cars, traffic lights, and roads can interact in real-time during simulations that anyone can execute. Like a real metropolis, the simulation is a vast world in itself.

SimSpace Weaver automatically configures the environment, links up to 10 Amazon EC2 instances into a networked cluster, and distributes the simulation across instances when a customer is prepared to deploy. To produce a single, integrated simulation that allows for real-time user interaction and manipulation, the service controls the network and memory configurations and duplicates and synchronises the data across the instances. Customers include Duality Robotics, Epic Games, and Lockheed Martin. The latter collaborated with AWS to create a San Francisco earthquake recovery demo to show how emergency personnel might plan an aid mission.

They need to be able to simulate at a real-world scale to trust that the insights we obtain from the simulation are transferable back to reality.

They were able to simulate more than a million objects at a continental scale, providing them with practical knowledge that helped them be more prepared for various situations, including natural disasters.

Better Data Handling: Exabytes, or petabytes, of data are gathered by enterprises from a variety of services, departments, on-site databases, and outside sources.

Administrators and data stewards must make this data accessible before it can realise its full potential. They must simultaneously retain control and governance to guarantee that data can only be accessed by the appropriate parties and under the appropriate circumstances.

To aid enterprises in cataloguing, discovering, sharing, and governing data from AWS, on-premises, and outside sources, Amazon DataZone was introduced. The vice president of databases, analytics, and machine learning at AWS, stated that good governance is the foundation that makes data available to the entire organisation. But customers frequently tell us that it can be challenging to strike the correct balance between making data discoverable and keeping control.

By defining their data taxonomy, configuring governance policies, and connecting to a variety of AWS services like Amazon S3 or Amazon Redshift, partner solutions like Salesforce and ServiceNow, and on-premises systems using the new data management service's web portal, organisations can set up their own business data catalogue.

After catalogues are set up, users may search and find assets using the Amazon DataZone web interface, analyse metadata for context, and request access to datasets. ML is used to collect and suggest information for each dataset. Customers can leverage the new tool's integration with AWS analytics services, including Amazon Redshift, Amazon Athena, and Amazon QuickSight, as part of their data project.

The new tool sets data free across the enterprise, so every person can contribute and develop fresh insights to maximise its value," as Sivasubramanian put it.

Safe Data Sharing: Organisations frequently wish to supplement their data with that of their partners to gain crucial insights. But they also need to curtail or stop exchanging raw data while safeguarding private consumer data.

Sharing user-level data and relying on partners to properly uphold contractual obligations are frequent requirements for this.

Data clean rooms, which enable several parties to combine and analyse their data in a secure setting without being able to view each other's raw data, can help to solve this problem. However, creating clean rooms can be challenging and calls for sophisticated privacy measures and specific data transportation technologies.

This procedure is made simpler by AWS Clean Rooms. Now that the AWS Cloud is available, businesses may work with any other business and easily construct secure data clean rooms.

Customers select the partners they wish to work with, their datasets, and participation restrictions. Advanced cryptographic computing technologies maintain data encrypted while giving them access to configurable data access constraints, such as query controls, query output restrictions, and query logging.

According to the VP of AWS applications, customers can cooperate on a range of tasks, such as more successfully creating advertising campaign insights and analysing investment data while boosting data security.

Proactively Acting on Security Data: Organisations want to identify security concerns quickly and take appropriate action. This enables them to safeguard data and networks quickly.

However, the information they require for analysis is frequently dispersed across several sources and kept in a range of forms.

Customers of AWS can now use the Amazon Security Lake to simplify this procedure. This service automatically consolidates security data from on-premises and cloud sources into a data lake that has been specifically designed in an AWS customer's account.

To facilitate quicker threat detection, investigation, and incident response, security analysts and engineers may then consolidate, manage, and optimise enormous volumes of diverse log and event data, according to AWS.

The vice president for security services at AWS, customers tell us they want to take action on this data faster to improve their security posture, but the process of collecting, standardising, storing, and managing this data is complex and time-consuming.

Addressing Supply Chain Complexity: Supply chains have recently witnessed unheard-of supply and demand instability, which has only been sped up by severe resource shortages, geopolitics, and natural disasters.

Businesses are under pressure from such interruptions to prepare for any supply chain unpredictability and react promptly to changes in customer demand while managing expenses.

However, firms can suffer from extra inventory expenses or stockouts when they do not sufficiently plan for supply chain risks, such as component shortages, shipping port congestion, unexpected demand surges, or weather interruptions. This may therefore result in disappointing consumer experiences.

By collecting and analysing data from various supply chain systems, the new AWS Supply Chain facilitates the simplification of this process. Companies can monitor operations in real-time, spot trends faster, and produce more precise demand projections.

Customers tell them that the undifferentiated heavy lifting required to integrate data between diverse supply chain solutions has impeded their ability to swiftly witness and respond to possible supply chain problems.

According to the business, the new service is built on nearly 30 years of experience with the Amazon.com logistics network. It employs pre-trained ML models to comprehend, extract, and aggregate data from ERP and supply chain management systems. The data is then contextualised in real-time, emphasising the quantity and selection of products available at each location at the time.

Users receive alerts when issues such as inventory shortages or delays arise from ML insights. AWS Supply Chain offers suggested actions after discovering an issue, such as moving inventory between locations based on the percentage of risk resolved, the distance between facilities, and the sustainability impact.

According to the global supply chain and operations head at Accenture, while supply chain disruptions persist for the foreseeable future, firms need to stay focused on balancing cost-effectiveness, sustainability, and relevancy throughout their supply networks to enable the growth of an AWS Supply Chain customer.Agile, resilient supply chains that are sensitive to market changes and customer needs can be enabled by executing a cloud-based digital strategy.

AWS also unveiled eight new Amazon SageMaker features, five new capabilities for its business intelligence tool Amazon QuickSight, and new database and analytics capabilities this week at AWS re:Invent 2022.