Top re:Invent 2022 takeaways for the advertising and marketing technology industry

There were a large number of new service announcements as well as partner and solution announcements that came out of AWS re:Invent 2022. To make it easy for advertising and marketing technology customers, we have identified a list of top announcements specific to this industry. Session summaries and access details to content on demand is also included in this blog.

Top announcements:

1. AWS Cleanrooms for privacy enhanced data collaboration

At Amazon Web Services, Inc. (AWS) re:Invent 2022, we announced the preview of AWS Clean Rooms, a new analytics service that helps customers collaborate with their partners to more easily and securely analyze their collective datasets—without sharing or revealing underlying raw data. The service is particularly relevant for brands, media publishers, agencies, and their partners who are looking to collaborate together for advertising and marketing use cases. It can improve campaign planning, activation, and measurement, or offer better, more relevant consumer experiences. (Press release)

Post re:Invent we also announced AWS Clean Rooms solutions to help customers prepare, match, and upload output data. Customers can deploy all these solutions with a few steps from the AWS Solutions Library, and launch pre-configured AWS services and partner applications for each use case. Together, these solutions enable customers to quickly get started with clean rooms to generate insights and improve performance in their applications:

• Data Connectors for AWS Clean Rooms
• LiveRamp Identity Resolution for AWS Clean Rooms
• Neustar Identity Resolution for AWS Clean Rooms
• Audience Uploader from AWS Clean Rooms
• Amazon Marketing Cloud uploader from AWS

2. Additional EC2 instances (Including new graviton 3 based instances) for cost/performance optimizing of Ad Platforms

New General Purpose((M6in/M6idn), Compute Optimized(C6in), and Memory-Optimized((R6in/R6idn) Amazon EC2 Instances with Higher Packet-Processing Performance
M6in and M6idn are general purpose instances.

C6in instances are designed to be able to handle up to twice as many packets per second (PPS) as their predecessors. This allows them to deliver increased performance in situations where they need to handle a large number of small-ish network packets. This will accelerate many applications and use cases including network virtual appliances, telecommunications, build servers, caches, in-memory databases, and gaming hosts. With more network bandwidth and PPS on tap, heavy-duty analytics applications that retrieve and store massive amounts of data and objects from Amazon Simple Storage Service (Amazon S3) or data lakes will benefit. For workloads that benefit from low-latency local storage, the disk versions of the new instances offer twice as much instance storage compared to previous generations.

R6in and R6idn: The higher network and EBS performance of the r6in instances will allow you to scale your network-intensive SQL, NoSQL, and in-memory database workloads, with the option to use the r6idn when you need low-latency local storage.

New Amazon EC2 Instance Types In the Works – C7gn, R7iz, and Hpc7g
C7gn instances are designed for your most demanding network-intensive workloads: network virtual appliances, data analytics, and tightly-coupled cluster computing jobs. They are powered by AWS Graviton3E processors and will support up to 200 Gbps of network bandwidth, along with 50% higher packet processing performance.

R7iz instances are powered by the latest 4th generation Intel Xeon Scalable Processors and are a great match for relational databases and other commercial software that is licensed on a per-core basis.

Hpc7g instances are powered by AWS Graviton3E processors, with up to 35% higher vector instruction processing performance than the Graviton3. They are designed to give you the best price/performance for tightly coupled compute-intensive HPC and distributed computing workloads. They deliver 200 Gbps of dedicated network bandwidth that is optimized for traffic between instances in the same VPC.

New Graviton3-Based General Purpose (m7g) and Memory-Optimized (r7g) Amazon EC2 Instances
The announcement of M7g and R7g instances was made after re:Invent. Both types are powered by the latest generation AWS Graviton3 processors, and are designed to deliver up to 25% better performance than the equivalent sixth-generation (M6g and R6g) instances, making them the best performers in EC2. M7g instances are for general purpose workloads such as application servers, micro services, mid-sized data stores, and caching fleets. R7g instances are a great fit for memory-intensive workloads such as open-source databases, in-memory caches, and real-time big data analytics.

Also, while we are on the topic of ad platforms, you may want to check the recently published Guidance for Building a Real Time Bidder for Advertising on AWS

3. New SageMaker capabilities for ad intelligence workloads

New for Amazon SageMaker – Perform Shadow Tests to Compare Inference Performance Between ML Model Variants
Deploying a model in shadow mode lets you conduct a more holistic test by routing a copy of the live inference requests for a production model to the new (shadow) model.

Next Generation SageMaker Notebooks – Now with Built-in Data Preparation, Real-Time Collaboration, and Notebook Automation
You can now improve data quality in minutes with the built-in data preparation capability, edit the same notebooks with your teams in real time, and automatically convert notebook code to production-ready jobs.

New – Share ML Models and Notebooks More Easily Within Your Organization with Amazon SageMaker JumpStart
Share your models and notebooks to collaborate and increase productivity, or to put your models into production.

New — Introducing Support for Real-Time and Batch Inference in Amazon SageMaker Data Wrangler
A new feature allows you to reuse the data transformation flow, which you created in Amazon SageMaker Data Wrangler as a step in Amazon SageMaker inference pipelines.

New — Amazon SageMaker Data Wrangler Supports SaaS Applications as Data Sources
With this feature, you can use 40+ SaaS applications as data sources by using Amazon AppFlow and have the data available on Amazon SageMaker Data Wrangler.

New ML Governance Tools for Amazon SageMaker – Simplify Access Control and Enhance Transparency Over Your ML Projects
New tools let you define custom permissions for Amazon SageMaker users in minutes, document model information from conception to deployment, and monitor all your deployed models through a unified dashboard.

New – Redesigned UI for Amazon SageMaker Studio
The redesigned UI makes it easier for you to discover and get started with the machine learning tools in Amazon SageMaker Studio.

Also, while we are on the topic of ad intelligence workloads, you may want to check the recently published Guidance for Machine Learning for Near Real-Time Advertising on AWS

4. New serverless analytics services to power your data processing workloads

Amazon OpenSearch Serverless (Preview)
Amazon OpenSearch Service now offers a new serverless option, Amazon OpenSearch Serverless. This option reduces the complexity from the process of running petabyte-scale search and analytics workloads by not needing you to configure, manage, or scale OpenSearch clusters. OpenSearch Serverless automatically provisions and scales the underlying resources to deliver fast data ingestion and query responses for even the most demanding and unpredictable workloads. With OpenSearch Serverless, you pay only for the resources consumed.

Amazon Athena for Apache Spark
Get started with interactive analytics using Amazon Athena for Apache Spark in under a second to analyze petabytes of data. Interactive Spark applications start instantly and run faster with our optimized Spark runtime—so you spend more time on insights, not waiting for results. Build Spark applications using the expressiveness of Python with a simplified notebook experience in an Athena console or through Athena APIs. With the Athena Serverless, fully managed model, there are no resources to manage, provision, and configure and no minimum fee or setup cost. You only pay for the queries that you run.

AWS Aurora zero ETL integration with Amazon Redshift
Amazon Aurora now supports zero-ETL (extract, transform, and load) integration with Amazon Redshift, to enable near real-time analytics and machine learning using Amazon Redshift on petabytes of transactional data from Aurora. Within seconds of transactional data being written into Aurora, the data is available in Amazon Redshift, so you don’t have to build and maintain complex data pipelines to perform ETL operations.

New for Amazon Transcribe – Real-Time Analytics During Live Calls
Near real-time call analytics provides APIs for developers to accurately transcribe live calls and at the same time identify customer experience issues and sentiment in near real-time.

5. New capabilities for networking workloads

AWS Verified Access — VPN-less Secure Network Access to Corporate Applications (Preview)
It is a new secure connectivity service that allows enterprises to enable local or remote secure access for their corporate applications without requiring a VPN. Verified Access improves your organization’s security posture by leveraging multiple security inputs to grant access to applications.

Introducing VPC Lattice – Simplify Networking for Service-to-Service Communication (Preview)
AWS announces Amazon VPC Lattice (Preview)
It is an application layer networking service that makes it quick to connect, secure, and monitor service-to-service communication. With Amazon VPC Lattice, you can define policies for traffic management, network access, and monitoring so you can connect applications in a simple and consistent way across AWS compute services (instances, containers, and serverless functions).

Elastic Load Balancing capabilities for application availability
We announced four new Elastic Load Balancing capabilities to further improve the availability of your applications. AWS provides multiple building blocks, like Regions and Availability Zones, so that you can design your applications to isolate them from different types of failures. Starting today, we are providing additional features that allow you to define how you want your applications to behave when failures occur as well as a feature to help you recover faster. These new capabilities include:

• Application Load Balancer (ALB) Cross Zone Off
• Network Load Balancer (NLB) Health Check Improvements
• ALB and NLB Minimum Healthy Targets
• Zonal Shift for ALB and NLB [Preview]

Amazon VPC Reachability Analyzer now supports across accounts
Amazon VPC Reachability Analyzer allows you to diagnose network reachability between a source resource and a destination resource in your virtual private clouds (VPCs) by analyzing your network configurations. It now supports network reachability analysis between AWS resources across different AWS accounts in your AWS Organization. This enables you to trace and troubleshoot the network reachability across your AWS Organization.

AWS Network Manager introduces real-time performance monitoring
Using AWS Network Manager, you can now monitor the near real-time and historical performance of the AWS Global Network for operational and planning purposes. AWS Network Manager provides aggregate network latency between AWS Regions, Availability Zones and within each Availability Zone, allowing you to better understand how your application performance relates to the performance of the underlying AWS network. You can monitor the network latency for the AWS Global Network in up to 5 minute intervals, as well as view the 45 day historical trend from AWS Network Manager. In addition, you can also publish these latency metrics to Amazon CloudWatch, to further monitor, analyze, and alert on them.

6. New capabilities for database workloads

Announcing Amazon DocumentDB Elastic Clusters
Elastic Clusters simplifies how customers interact with Amazon DocumentDB by automatically managing the underlying infrastructure and removing the need to create, remove, upgrade, or scale instances.

New – Amazon RDS Optimized Reads and Optimized Writes
Two new features will accelerate your Amazon Relational Database Service (Amazon RDS) for MySQL workloads.

New – Fully Managed Blue/Green Deployments in Amazon Aurora and Amazon RDS
The new feature for Amazon Aurora with MySQL compatibility, Amazon RDS for MySQL, and Amazon RDS for MariaDB, enables you to make database updates safer and faster.

7. Announcing the general availability of two ML-powered capabilities for Amazon Connect as well as two previews of new capabilities that support contact center agents

Amazon Connect – New ML-Powered Capabilities for Forecasting, Capacity Planning, Scheduling, and Agent Empowerment

Amazon Connect is a cloud contact center that helps companies of any size deliver superior customer service at a lower cost. New capabilities that help marketers and media companies reduce heavy lifting to deliver personalized consumer experiences:

• Amazon Connect forecasting, capacity planning, and scheduling, which was announced in March 2022 at Enterprise Connect, is now generally available!
• Contact Lens for Amazon Connect adds conversational analytics for chat, extending the ML-powered analytics from voice-based customer interactions to chat interactions.

In addition, there are previews of the following new capabilities:

• Contact Lens for Amazon Connect adds evaluation forms for agent performance, helping managers to create evaluation forms that can be automatically scored by Contact Lens’s ML-powered conversational analytics.
• Amazon Connect agent workspace adds a new step-by-step experience that guides agents to resolve customer issues.

8. Management tools

New – Amazon CloudWatch Cross-Account Observability
A new capability lets you search, analyze, and correlate cross-account telemetry data stored in Amazon CloudWatch such as metrics, logs, and traces.

Amazon CloudWatch Internet Monitor Provides End-to-End Visibility into Internet Performance for your Applications (Preview)
A new capability gives visibility into how an internet issue might impact the performance and availability of your applications. It allows you to reduce the time it takes to diagnose internet issues from days to minutes.

9. New capabilities for securing the advertising and marketing tech workloads

Automated Data Discovery for Amazon Macie
A new capability allows you to gain visibility into where your sensitive data resides on Amazon Simple Storage Service (Amazon S3) at a fraction of the cost of running a full data inspection across all your S3 buckets.

AWS announces Amazon Verified Permissions (Preview)
This central fine-grained permissions management system makes it straightforward to change and update permission rules in a single place without needing to change the code.

10. New Storage capabilities

New – Failover Controls for Amazon S3 Multi-Region Access Points
New controls let you shift S3 data access request traffic routed through an Amazon S3 Multi-Region Access Point to an alternate AWS Region within minutes to test and build highly available applications for business continuity.

New – Amazon Redshift Support in AWS Backup
AWS Backup allows you to define a central backup policy to manage data protection of your applications and can now also protect your Amazon Redshift clusters.

Announcing Automated in-AWS Failback for AWS Elastic Disaster Recovery
The new automated support provides a simplified and expedited experience to fail back Amazon Elastic Compute Cloud (Amazon EC2) instances to the original Region. Both failover and fail back processes (for on-premises or in-AWS recovery) can be conveniently started from the AWS Management Console.

Sessions:

Amazon Ads – Ultra-low latency machine learning at Amazon Ads
Learn how Amazon Ads runs high-throughput and low-latency machine learning (ML) workloads in the AWS Cloud to improve ad performance and shopper experience. Learn how they built an ML feature store with multi-tenant scaling, versioning, and near real-time insights which serves streaming features and deep learning embeddings handling millions of requests per second with one-millisecond retrieval latency. Discover how they use Amazon Rekognition for content moderation across millions of ads to ensure a positive ad experience. You can watch the recording on demand.

Ad technology innovation with NBCUniversal and FreeWheel
Hear how one of the largest media companies in the world used AWS to reinvent their TV and video advertising workloads. Learn how NBCUniversal built a first-party data platform in the AWS Cloud to create unique audiences and expand addressability and developed privacy-enhanced solutions to collaborate with partners more effectively. Then see how FreeWheel, a Comcast company, improved performance for data and analytics workloads running at massive scale using AWS Graviton–based instances and other cloud capabilities. Learn about best practices, architectures, and lessons learned building scaled publisher-side ad technology workloads. You can watch the recording on demand.

Deploying a complete ML framework for real-time bidding (Chalk Talk)
Learn tips, tricks, and best practices for how to run machine learning for high-throughput, ultra-low-latency advertising workloads in the cloud. Discover a new framework using Amazon SageMaker Studio and Amazon EMR that can help organizations reduce heavy lifting and deploy a complete purpose-built ML pipeline for OpenRTB protocols and near real-time inference. Learn how to deploy an ML code kit and train models that are preconfigured for near real-time advertising use cases including bid filtering and deduplication. Determine how to adapt the kit for other low-latency use cases. Chalk Talks are not recorded, but you can download the slides.

[NEW LAUNCH!] Privacy-enhanced collaboration with AWS Clean Rooms (with comScore)
Get a first look at how AWS Clean Rooms can help you collaborate with your partners without sharing raw data with each other. Hear from AWS experts and customers on how you can use AWS Clean Rooms to create your own clean rooms in minutes, add participants, and start analyzing your collective datasets. You’ll learn how AWS Clean Rooms helps you protect consumer data and add restrictions on queries run by each AWS Clean Rooms participant with built-in, customizable analysis rules and privacy-enhancing controls. You can watch the recording on demand.

Running high-throughput, real-time ad platforms in the cloud (AWS + AppsFlyer)
Explore best practices and learnings from industry users for how AWS can reduce costs and improve performance for scaled real-time advertising workloads. Learn how AppsFlyer moved their real-time platform that handles more than 800 billion events per day into a scalable, cloud-native architecture using Amazon EKS and AWS Graviton-based instances to improve cost performance and reduce their carbon footprint. Then, hear AWS ad technology experts share customer stories and pitfalls to avoid when running ad platforms at scale. Learn from purpose-built solutions that you can apply to your own applications. You can watch the recording on demand.

Accelerating innovation in advertising and marketing technology (Leadership Session)
AWS helps brands, agencies, media publishers, and technology providers innovate faster, optimize cost performance, and protect consumer data in the privacy-first era of advertising and marketing. In this leadership session, hear directly from customers about how AWS helps them reinvent using solutions, services, and AWS Partner offerings across areas such as audience and customer data management, advertising platforms, advertising intelligence, collaboration and interoperability, measurement, and customer experience. Explore new solutions and discover how you can use them to quickly build on the most comprehensive cloud for advertising and marketing technology. You can watch the recording on demand.

Contact your AWS account team if you want to better understand any of these announcements or have questions.