AWS CEO Adam Selipsky kicked off the first full day ofAWS re:Invent 2021 by delivering a 2+ hour keynote covering the history of AWS, his vision for expanding the usefulness and accessibility of the cloud tomorrow . . . and some detours into the history of basketball and Florence Nightingale.
It sounds a little weird, but it actually worked in the way good “appeal to everyone” keynotes typically do.
Here’s a rundown of all the big news announced this morning from Las Vegas, including Graviton3, AWS SageMaker Canvas, Private 5G, serverless and on-demand analytics, new security features forAWS Lake Formation, and more.
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1. 15 years in, the state of AWS is strong
As AWS re:Invent celebrates 10 years and Amazon Web Services (AWS) 15 years as a service that is almost synonymous with “cloud,” Selipsky opened by looking back at the past and discussing where the company is heading.
The news of the day included a big focus on purpose-built services for various industries and making complicated technology more accessible than ever to people with little or no technical experience.
“The cloud is an opportunity to reimagine anything,”Selipsky said.” It provides a pathway to true transformation.”.
The history portion served to remind everyone how recently cloud skeptics were still writing off cloud technology, from thinking it was a fad to just something for enterprise organizations to something useful but certainly not for mission-critical workloads.
“You’ve proved them wrong,” Selipsky said.
Today, S3 stores more than 100 trillion objects, and AWS offers more than 200 fully featured services and 81 AZ and 25 regions (with more sure to come).
“Despite what feels like massive adoption, we’re just getting started”, Selipsky said. “There’s no industry that hasn’t been touched and no industry that can’t be radically disrupted. And everyone here today is part of that movement.”
The day marked Selipsky’s first re:Invent opening keynote since filling Amazon CEO Andy Jassy’s shoes.
2. Amazon unveils Graviton3 processors to power C7g instances
Right out the gate, Amazon announced its next-generation of ARM-based Graivton3 processors, boasting 25% better performance with 60% less power used for an improved carbon footprint.
AWS also claims the chips will deliver 2x floating point and cryptographic performance and 3x better performance for machine learning workloads.
C7g Instances powered by Gravinton3 processors are currently available in preview.
Liz Fong-Jones, Principal Developer Advocate at Honeycomb, has previously talked about Honeycomb’s heavy usage of Graviton2 to help reduce cost and environmental footprint while increasing the amount of compute. She chimed in on Twitter to talk about the results she’s seen: “We’ve tried it, it’s even better than 25% better for our workload.”
3. Amazon EC2 Trn1 instances for deep-learning training are in preview
AWS announced AWS Trainium-based Amazon EC2 Trn1 instances are in preview.
These instances are optimized for training deep-learning models in the cloud, providing what AWS claims is the best price-performance for model training and offering 800 Gbps networking bandwidth.
These are ideal for use cases like language processing or image recognition. Get all the info on the product page.
4. AWS Mainframe Modernization to help migrate on-prem mainframe workloads
Selipsky says customers are trying to get off mainframes and “reduce costs by 70% or more by migrating.” Some choose to lift and shift, while others decided to refactor.
AWS feels neither is as easy as customers might like. The solution? AWS Mainframe Modernization.
Selipsky says this platform will help organizations migrate, modernize, and run mainframe workloads on AWS, cutting migration time by two-thirds, analyzing and assessing migration readiness and helping them with automated refactoring or replatforming.
More info here.
5. AWS Private 5G lets you own private cell network
Tired of lousy cell service? Maybe it’s time to create your own 5G network. It’s weirdly now sort of an option, with AWS Private 5G.
AWS Private 5G allows businesses to set up and manage their own private networks, claiming setup can be completed in days rather than months.
Why might you want a private 5G network (beyond getting your 5G-fearing neighbor to move away)?
AWS claims a private 5G network can help organizations dealing with enterprise network constraints by augmenting existing networks to deliver high bandwidth, long-range coverage to an increasing number of devices while maintaining the security and control of a private network.
AWS ships what you need (hardware, software, and SIM cards), your people pop in the AWS-supplied SIM cards, and the network autoconfigures itself. Selipsky offered a large campus, warehouse, or factory floor as potential use cases. Customers only pay for the capacity and throughput they request.
6. SageMaker Canvas: ML models for dummies?
People want access to cool machine learning systems. But that means leaning on a data science team that probably has better things to be doing. Enter: Amazon SageMaker Canvas, which is now GA.
SageMaker Canvas is designed to help business users and analysts generate ML predictions using a visual, no-code, point-and-click interface. After models are created, users can publish the results and collaborate with others.
More info here.
7. New serverless and on-demand analytics options
AWS announced serverless and on-demand options are now available for Redshift, EMR, MSK, and Kinesis in preview.
No configuration or scaling of clusters or servers. Just fire them up and the services scale up when you’re busy and scale down when you’re not. You pay only when service is in use.
ACG’s Stephen Sennett was particularly excited about the potential forKinesis Data Streams on-demand: “Kinesis Data Stream having an on-demand mode is interesting, and potentially opens up a lot of use cases to people who weren’t willing to commit on ‘X size’ — similar to how On-Demand DynamoDB opens up stuff to very infrequent use cases or highly unpredictable workloads.”
In the announcement around Kinesis Data Streams on-demand, Marcia Villalba explains some potential use cases.
“On-demand mode is great for customers that have an unknown or variable workload, or who simply don’t want to deal with capacity management. On-demand mode works best for workloads that have even partition key distribution. For example, you run a mobile game that has variable traffic through the week or day, as customers play mostly on nights or weekends. Or, you run a streaming platform that hosts live shows, and you see a sudden increase in demand depending on the guests you have.”
You can get more information about each at the following links:
8. AWS Lake Formation introduces spalshy new security features
AWS is upping the security features for AWS Lake Formation, adding row- and cell-level security and governed tables, which are GA today.
Governed tables are a new type of S3 table that make it easier to ingest and manage data, supporting ACID transactions that let users concurrently insert and delete data. Row- and cell-level security ensures that data is only revealed to authorized users as permitted down to specific rows and columns. This works for governed and traditional S3 tables.
As Selipsky put it, this “puts the right data in the hands of the right people and only the right people.”
9. Amazon S3 Glacier Instant Retrieval
But one that stands out is the announcement of Amazon S3 Glacier Instant Retrieval storage class, which looks very cool. (Sorry…)
AWS claims it will give the lowest cost storage for data that’s rarely accessed (up to 68% cheaper than S3 Standard-Infrequent Access) while offering milliseconds retrieval.
10. AWS reveals IoT services for industrial and automotive verticals
AWS IoT TwinMaker lets developers make digital twins of real-world systems, like factories, industrial equipment, or product lines.
AWS IoT Fleetwise aims to make it easier for automakers to collect and analyze the vast amounts of data generated by millions of vehicles in almost real-time. This data can be used for everything from remote diagnosis to training machine learning models for self-driving or assisted driving systems.
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