Tuesday, 2nd December 2024
Today was all about Expo and networking. The Expo is on a scale that I have never experienced anything close in comparison. I visited the Community Hub and spoke with fellow AWS Community Builders before heading to the Expo. I discussed vendor solutions with Sysdig, GitLab, did a full in-depth demo with Datadog, did a generative AI experience with Intel Ai and spoke with Hashicorp. The rest of my day was spent discussing Well-Architected, DevOps, Security, AWS Ambassadors Program, AWS Community Builders, catching up with fellow AWS Ambassadors from all over the world and some old faces from AWS. It was awesome! What an exhausting day!
Some highlights from today’s activities:
Recent Announcments
Lastly, it would not be re:Invent without some announcements. There were 31 announcements in total today. These are the highlights:
Announcing Amazon Aurora DSQL (Preview)
Today, AWS announces the preview of Amazon Aurora DSQL, a new serverless, distributed SQL database with active-active high availability. Aurora DSQL allows you to build always available applications with virtually unlimited scalability, the highest availability, and zero infrastructure management. It is designed to make scaling and resiliency effortless for your applications, and offers the fastest distributed SQL reads and writes.
Aurora DSQL provides virtually unlimited horizontal scaling with the flexibility to independently scale reads, writes, compute, and storage. It automatically scales to meet any workload demand without database sharding or instance upgrades. Its active-active distributed architecture is designed for 99.99% single-Region and 99.999% multi-Region availability with no single point of failure, and automated failure recovery. This ensures that all reads and writes to any Regional endpoint are strongly consistent and durable. Aurora DSQL is PostgreSQL compatible, offering an easy-to-use developer experience.
Aurora DSQL is now available in preview in the following AWS Regions: US East (N. Virginia), US East (Ohio), and US West (Oregon).
Announcing Amazon S3 Tables – Fully managed Apache Iceberg tables optimized for analytics workloads
Amazon S3 Tables deliver the first cloud object store with built-in Apache Iceberg support, and the easiest way to store tabular data at scale. S3 Tables are specifically optimised for analytics workloads, resulting in up to 3x faster query throughput and up to 10x higher transactions per second compared to self-managed tables. With S3 Tables support for the Apache Iceberg standard, your tabular data can be easily queried by popular AWS and third-party query engines. Additionally, S3 Tables are designed to perform continual table maintenance to automatically optimise query efficiency and storage cost over time, even as your data lake scales and evolves. S3 Tables integration with AWS Glue Data Catalog is in preview, allowing you to stream, query, and visualise data—including S3 Metadata tables—using AWS Analytics services such as Amazon Data Firehose, Athena, Redshift, EMR, and QuickSight.
Amazon EC2 Trn2 instances are generally available
Today, AWS announces the general availability of Amazon Elastic Compute Cloud (Amazon EC2) Trn2 instances and preview of Trn2 UltraServers, powered by AWS Trainium2 chips. Available via EC2 Capacity Blocks, Trn2 instances and UltraServers are the most powerful EC2 compute solutions for deep learning and generative AI training and inference.
You can use Trn2 instances to train and deploy the most demanding foundation models including large language models (LLMs), multi-modal models, diffusion transformers and more to build a broad set of AI applications. To reduce training times and deliver breakthrough response times (per-token-latency) for the most capable, state-of-the-art models you might need more compute and memory than a single instance can deliver. Trn2 UltraServers is a completely new EC2 offering that uses NeuronLink, a high-bandwidth, low-latency fabric, to connect 64 Trainium2 chips across 4 Trn2 instances into one node unlocking unparalleled performance. For inference, UltraServers help deliver industry-leading response times to create the best real-time experiences. For training, UltraServers boost model training speed and efficiency with faster collective communication for model parallelism as compared to standalone instances.
Amazon DynamoDB global tables previews multi-Region strong consistency
Starting today in preview, Amazon DynamoDB global tables now supports multi-Region strong consistency. DynamoDB global tables is a fully managed, serverless, multi-Region, and multi-active database used by tens of thousands of customers. With this new capability, you can now build highly available multi-Region applications with a Recovery Point Objective (RPO) of zero, achieving the highest level of resilience.
Multi-Region strong consistency ensures your applications can always read the latest version of data from any Region in a global table, removing the undifferentiated heavy lifting of managing consistency across multiple Regions. It is useful for building global applications with strict consistency requirements, such as user profile management, inventory tracking, and financial transaction processing.
The preview of DynamoDB global tables with multi-Region strong consistency is available in the following Regions: US East (N. Virginia), US East (Ohio), and US West (Oregon). DynamoDB global tables with multi-Region strong consistency is billed according to existing global tables pricing. To learn more about global tables multi-Region strong consistency, see the preview documentation. For information about DynamoDB global tables, see the global tables information page and the developer guide.
Announcing Amazon S3 Metadata (Preview) – Easiest and fastest way to manage your metadata
Amazon S3 Metadata is the easiest and fastest way to help you instantly discover and understand your S3 data with automated, easily-queried metadata that updates in near real-time. This helps you to curate, identify, and use your S3 data for business analytics, real-time inference applications, and more. S3 Metadata supports object metadata, which includes system-defined details like size and the source of the object, and custom metadata, which allows you to use tags to annotate your objects with information like product SKU, transaction ID, or content rating, for example.
