AWS re:Invent 2019: Key announcements for the Advertising and Marketing Industry
With more than 77 new service launches and other announcements, AWS re:Invent left a lot to digest—even for teams inside AWS! We’ve developed this guide to ensure customers in the advertising and marketing industry know the most relevant news from this year’s event.
Of the many announcements, three stood out as game-changers for advertising and marketing industry customers:
- The Trade Desk announced four new cloud-based real-time bidding sites running on AWS in Tokyo, Singapore, Beijing and Frankfurt, with another site coming online soon in Hong Kong. Zak Stengel, SVP of Engineering at The Trade Desk, spoke in the Advertising and Marketing Industry Leadership session at re:Invent. Stengel noted the launch of AWS Global Accelerator at AWS re:Invent 2018 was a game-changer for the company’s bidding strategy, saying,“The sheer size of our bidding workload and its latency requirements, along with how dynamic it can be, posed some really significant challenges to load balancing solutions. We began testing Global Accelerator shortly after its public release [at re:Invent 2018] … and we found that it worked for our workload. This was the final development that led us to shift toward a cloud-bidding strategy.”
- Amazon Sagemaker: AWS announced the availability of Deep Graph Library, an open source library built for easy implementation of graph neural networks, on Amazon SageMaker. The library will help customers in advertising and marketing improve machine learning on identity graph-friendly workloads such as Data Management Platforms, Customer Data Platforms, and other graph workloads for cross-device and customer event mapping.
- AWS made several Amazon Redshift announcements that will improve advertising analytics and big data workloads for industry customers, including Amazon Redshift RA3 nodes—featuring high bandwidth networking and performance indistinguishable from bare metal— as well as Amazon Redshift Federated Query (Preview) to query and analyze data across operational databases, data warehouses, and data lakes.
Advertising and marketing industry customer announcements
- Nielsen Media talked about their migration of National TV Audience Measurement to AWS, which included development of a 30PB AWS data lake that helped Nielsen expand from measuring 40,000 households to over 30 million.
- Advertising technology firm Smaato shared through how they use Apache Spark and Amazon SageMaker to reduce costs on programmatic advertising workloads with machine learning.
- Annalect dove deep into their use of containers and AWS analytics tools to reduce costs from $70 per usable TB to under $5 per usable TB while increasing their total queryable data from 100 TB to over 2 PB during the same period.
- Calvin French-Owen, CTO of Segment, shared keys for personalization for marketing technology firms using big data, also as part of the Advertising and Marketing Industry Leadership Session at re:Invent.
- Zeta Global announced using Amazon Neptune for cross-device identity resolution in their Customer Intelligence platform, which handles 450 million requests per day and resolves data for 1 billion customer profiles.
Below are some relevant services and features launched at this year’s re:Invent grouped by common workloads and use-cases from industry customers.
Cost and performance optimization
Target use-cases: Optimize compute and networking costs at low-latency for scaled data collection and event streaming, machine learning inferencing, and for programmatic advertising workloads such as bidding, auctions and ad serving.
- AWS Compute Optimizer recommends optimal AWS Compute resources for your workloads to reduce costs and improve performance by using machine learning to analyze historical usage metrics.
- EC2 inf1 instances for machine learning inferencing deliver up to 3x higher throughput and up to 40% lower cost per inference than Amazon EC2 G4 instances, which were already the lowest cost instance for machine learning inference available in the cloud.
- EKS Preview: ARM-Processor EC2 A1 instances are now available in more regions with latest kubernetes versions.
- Amazon Elastic Container Services (ECS) Cluster Auto Scaling is now available. With ECS Cluster Auto Scaling, your ECS clusters running on EC2 can automatically scale as needed to meet the resource demands of all tasks and services in your cluster, including scaling to and from zero.
- AWS Wavelength: Build applications that deliver single-digit millisecond latencies to mobile devices and end-users. Deploy applications to Wavelength Zones, AWS infrastructure deployments that embed AWS compute and storage services within the telecommunications providers’ datacenters at the edge of the 5G networks, and seamlessly access the breadth of AWS services in the region. In advertising and marketing, this means workloads like ad tracking, event collection, identity matching, and ad serving are possible.
- AWS Transit Gateway Network Manager allows you to centrally manage and monitor your global network across AWS and on premises, with network manager. Transit Gateway network manager reduces the operational complexity of managing networks across AWS Regions and remote locations.
- AWS Transit Gateway now supports the ability to establish peering connections between Transit Gateways in different AWS Regions. Transit Gateway is a service that enables customers to connect thousands of Amazon Virtual Private Clouds (Amazon VPCs) and their on-premises networks using a single gateway.
Target use-cases: Contextual analysis and brand safety with Amazon Rekognition Custom Labels, and optimizing bid decisioning, auction traffic, compute and networking costs and personalization with Amazon SageMaker
- AWS released eight new features for Amazon SageMaker, including Amazon SageMaker Studio—the first-ever fully integrated development environment for machine learning—and Amazon SageMaker Notebooks, one-click Jupyter notebooks that customers can start in seconds, and Amazon SageMaker Operators for Kubernetes to make it easier for developers and data scientists using Kubernetes to train, tune, and deploy machine learning (ML) models. AWS also made Amazon SageMaker Autopilot generally available, enabling customers to automatically create the best classification and regression machine learning models, while allowing full control and visibility.
- Amazon Rekognition Custom Labels: A new feature of Amazon Rekognition that enables customers to build their own specialized machine learning (ML)-based image analysis capabilities to detect and label unique objects and scenes including brand icons, logos, or key markers important to brand safety and contextual advertising analysis. Relevant examples for the advertising and marketing industry include NFL Media, part of the National Football League, and VidMob, a marketing creative platform that uses Amazon Rekognition Custom Labels to achieve “a lift of 150% in creative performance and 30% reduction in human analyst time,” according to VidMob CEO Alex Collmer.
Advertising and customer-360 analytics
Target use-cases: Advertising analytics, supply-path optimization, identity resolution and cross-device matching
- Amazon Neptune Workbench: Amazon Neptune now offers a workbench—an in-console experience to query your graph. The workbench lets you quickly and easily query your Neptune databases with Jupyter notebooks – a fully managed, interactive development environment with live code and narrative text.
- Amazon Redshift RA3 nodes—Analyze data more cost-effectively with RA3 nodes, which are built on the AWS Nitro System and feature high bandwidth networking and performance indistinguishable from bare metal.
- AQUA (Advanced Query Accelerator for Amazon Redshift) is now available on Preview. AQUA is a new distributed and hardware-accelerated cache that enables Redshift to run up to 10x faster than any other cloud data warehouse.
- Amazon Redshift Federated Query (Preview) will enable customers to query and analyze data across operational databases, data warehouses, and data lakes.
- Amazon Redshift Data Lake Export enables customers to unload the result of an Amazon Redshift query to your Amazon S3 data lake as Apache Parquet, an efficient open columnar storage format for analytics.
Data integrations & orchestration
Target use-cases: Improve integrations with customers and SaaS providers with Amazon EventBridge, and drive cost-effectiveness with new functionality in AWS Step Functions
- Amazon EventBridge Schema Registry and Discovery (Preview): Amazon EventBridge is a serverless event bus that makes it easy to connect applications together using data from customer’s own applications, Software-as-a-Service (SaaS) applications, and AWS services. The Amazon EventBridge schema registry stores event structure or schema in a shared central location and maps those schema to code for Java, Python, and Typescript so it’s easy to use events as objects in their code. This makes it faster and easier to build event-driven applications such as those used commonly in marketing automation today.
- AWS Step Functions Express Workflows: Express Workflows are a new type of AWS Step Functions workflow type that cost-effectively orchestrate AWS compute, database, and messaging services for marketing technology workloads.