2022 AWS Re:Invent Day 2; Top Highlights From Adam Selipsky’s Keynote
Day 2 has AWS CEO Adam Selipsky present and in grand fashion, a tonne of exciting announcements have been revealed. The underlying theme of the presentation has been building products/services on top of their already leading-edge cloud with added focus on Sustainability.
Unfortunately, services with the tag ‘Preview’ are not yet available in our beloved Sydney region, but hopefully, they’ll show up soon.
Amazon OpenSearch Serverless (Preview)
OpenSearch Serverless decouples compute and storage and separates the indexing (ingest) components from the search components, with AWS S3 as the primary data storage for indexes. With this decoupled architecture, OpenSearch Serverless can scale search and indexing functions independently of each other, and independently of the indexed data in Amazon S3. Ideally, when an application-monitoring workload receives a sudden burst of logging activities during an availability event, OpenSearch Serverless instantly scales the resources to ingest and store the data without impacting query response times.
Removing the need to run OpenSearch 24/7 would be a huge cost saving long term, and with the serverless nature of it, automating OpenSearch as part of a dev stack allows us for quicker deployment times.
AWS Supply Chain (Preview)
AWS Supply Chain provides a real-time visual map feature showing the level and health of inventory in each location, ML-powered insights, and targeted watchlists to alert you to potential risks. When a risk is uncovered, AWS Supply Chain provides inventory rebalancing recommendations and built-in, contextual collaboration tools that make it easier to coordinate across teams to implement solutions. AWS Supply Chain connects to your existing enterprise resource planning (ERP) and supply chain management systems, without re-platforming, upfront licensing fees, or long-term contracts.
The pandemic has created a chance for AWS and Amazon to leverage their logistics expertise and provide an offering that will visualise any potential disruption.
Amazon Security Lake (Preview)
Security Lake automatically creates a security data lake in a Region that you select for rolling up your global data. AWS log and security data sources are automatically collected in your selected Amazon Simple Storage Service (Amazon S3) bucket for existing and new accounts. They are normalised into the
Open Cybersecurity Schema Framework (OCSF) format, including AWS CloudTrail management events, Amazon Virtual Private Cloud (Amazon VPC) Flow Logs, Amazon Route 53 Resolver query logs, and security findings from over 50 solutions integrated through AWS Security Hub. Security Lake also provides integrations to third-party security solutions and your custom data that you have converted into OCSF.
Security Lake comes across as a standardisation platform for all logs and simplifies analysing security data to get a more complete understanding of your security across the entire organisation. It improves the protection of workloads, applications, and data, while automatically gathering and managing all security data across accounts and regions.
AWS SimSpace Weaver
With SimSpace Weaver, you can break down the simulation world into smaller, discrete spatial areas and distribute the task of running the simulation code across multiple Amazon Elastic Compute Cloud (Amazon EC2) instances. SimSpace Weaver automatically provisions the requested number of EC2 resources, networks them together, and maintains synchronised simulation time across the distributed cluster. It manages the complexities of data replication and object transfer across Amazon EC2 instances so that you can spend more time developing simulation code and content. You can use your own custom simulation engine or popular third-party tools such as Unity and Unreal Engine 5 with SimSpace Weaver.
AWS Redshift Integration For Apache Spark
Amazon Redshift integration for Apache Spark minimises the cumbersome and often manual process of setting up a spark-redshift open-source connector and reduces the time needed to prepare for analytics and ML tasks. You only need to specify the connection to your data warehouse and can start working with Amazon Redshift data from your Apache Spark-based applications in seconds. You can use several pushdown capabilities for operations such as sort, aggregate, limit, join, and scalar functions so that only the relevant data is moved from your Amazon Redshift data warehouse to the consuming Spark application. This allows you to improve the performance of your applications. You can also help make your applications more secure by using AWS Identity Access and Management (IAM) credentials to connect to Amazon Redshift.
Amazon Aurora Zero-ETL integration with Amazon RedShift (Preview)
Along with Redshift integration for Apache Spark, AWS also announced a new feature of Amazon Aurora that allows for near real-time analytics and machine learning processing using Amazon RedShift. 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 extract, transform, and load (ETL) operations.
This is going to drastically reduce the time and effort organisations need to invest in gaining insights into their data and allow for the leveraging of Amazon Redshift’s analytics capabilities as well as other features such as built-in ML, materialised views, data sharing, and federated access.
The only catch is that for the time being at least, it’s only available in a limited preview for Amazon Aurora MySQL 3 with MySQL 8.0 compatibility in the US East (N. Virginia) region.
While this is a really interesting announcement (and I’ll definitely be doing some testing of it over the christmas break) there are a couple of key limitation outlined in the AWS documentation (available here for reference) including:
- Currently only supports MariaDB and MySQL
- Isn’t currently available in the Beijing and Ningxia regions
- AWS CloudFormation, RDS Proxy, Cross-Region Read Replication and Multi-AZ DB clusters are not supported