Think in Context: AWS re:Invent 2022 Swami Sivasubramanian Keynote

Published:
  Lastmod: 2024-04-16
Think in Context
by Ernest Chiang

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Initing

Since I was selected as AWS Community Hero in early 2020, I have encountered the epidemic and have not flown for three years. It is a great honor to be invited to physically log in to the AWS annual developer conference re:Invent 2022 in Las Vegas this year. I am very happy to be able to experience all kinds of live events again.

For three consecutive years, I have decomposed the CEO Keynote, which attracts the most attention among the four keynotes held at AWS re:Invent each year. This year, I want to try to sort out the other keynotes. Maybe there will be different gains in the process of sorting out, and then I will share the results of these sorting by the way. Share it with everyone.

As in the past, at the beginning of the full text, I will first try to grasp the structure of the speech, then put some observations and inferences, and then put some running notes in each paragraph for future search and use. There are extended readings at the end of the article, which can enrich the background situation or information of the speech content. I also hope to have the opportunity to lead everyone to reason together why this product or function was launched at this time point, where the trends of various industries around the world are going. Read more, compare more, and refer more. Maybe we can avoid some minefields of misjudgment. In the case of limited resources, if we go in the wrong direction, the market may not give us a chance to cut it off and practice again. My reasoning is not necessarily correct, just as a kind of practice and sharing.

New services or new launches are marked with [NEW 🚀] in this article, so that you can press Command/Ctrl+F to search on the inner page.

This article deliberately removes most of the product links first, so that everyone can focus on reading (we are all less focused these days, aren’t we?). If you need a product link, you can refer to the AWS Product List that I organize regularly.

I also welcome everyone to give me some feedback or corrections (also come and see if there is a chance to include these materials in ChatGPT 2022). Then let’s get started!

tl;dr

Construct the cornerstone of the future foundation, connect various organizational units, and release tools to educate the future.

Echoing the other three keynotes:

Structure

This year, the structure of Dr. Swami Sivasubramanian, VP of AWS Data and Machine Learning’s speech is quite clean and clear, and after dismantling, there are actually many interesting small details. I think the burying is beautiful, and it is a very delicate speech. (for bracket people like me :p)

There are two main structures of the whole speech: lay the hidden foundation in the opening, and unfolding the hidden foundation. (Okay, stop making trouble, this is what people who are preparing to give speeches want to see, not what everyone wants to see, I know)

The whole speech is basically a main axis "Connect the dots", and three paragraphs.

  • lay the hidden foundation / Opening
    • This year’s opening animation vision first brought out “dot”, but the text guides everyone to think of it as “data”.
    • “dot” is the key here. Everyone thinks of a very long-term layout, which is usually “points, lines, and planes.”
    • Dr. Swami Sivasubramanian came out and introduced three scientists, but in fact, the focus is on “connect the dots”, which Spencer has accumulated more than 30 years of experience. Link to himself from here “just like me do with our data”. After breaking the hidden foundation, let’s start to get into the topic.
  • unfolding the hidden foundation / Core elements of a data strategy
    • Build future-proof foundations - supported by core data services
      • Tools for every workload
      • Performance at scale
      • Removing heavy lifting
      • Reliability and scalability
    • Weave connective tissue - across your organization
    • Democratize data - with tools and education

Opening

  • This is your idea —> It’s the opening animation, but it’s already laying the hidden foundation! Because “star” —> “dot”
    • The idea that keeps you up at night —> For example, my idea makes me wake up at 05:30am to continue organizing this keynote study note?! XDD
    • The idea that you thought about in the shower this morning
    • But did your idea just happen?
    • Or did it come from an accumulation of
      • Live Experiences
      • Victories
      • Defeats
      • Trials
      • Errors
      • Information
      • Data
    • What happens when we connect those moments?
    • Suddenly experiences become epiphanies (頓悟).
    • How can you organize?
    • How can you unify?
    • How can you analyze your data can become your innovation?
    • How, indeed.
  • Story Time —> Memes slowly emerge: Scientists spent thirty years accumulating experience connect the dots + just like me do with our data
    • How do scientists innovate?
      • 250BC - Archimedes discovers buoyancy
      • 1665AD - Newton discovers gravity
      • 1945AD - Spencer invents the microwave
        • Spencer has spent over 20 years of experience, over 30 years to: connect the dots
        • Just like me do with our data
  • The human brain —> You can refer to Learning How to Learn by Barbara Oakley. There is a link within this article 【AWS Learning Path & Strategy】.
    • Processing data even while sleeping
  • The organization <— describe the problem; working backwards
    • Data isn’t centralized
    • Data don’t automatically processed
    • Data doesn’t naturally flow
    • Data isn’t easy to visualize
    • Data is the genesis for modern invention
    • Data beats intuition
  • AWS <— various data services “in the cloud”
    • 15yr+ years of data innovation
    • First scalable object storage in the cloud with Amazon S3
    • The first purpose-built in the cloud with Amazon DynamoDB
    • The first fully-managed warehouse in the cloud with Amazon Redshift
    • Managed data streaming with Amazon Kinesis and MSK
    • First ML IDE in the cloud with Amazon SageMaker
    • Amazon RDS scores 95 out of 100 in Gartner Solution Scorecard for dbPaaS
    • More than 1.5M AWS customers use database, analytics, or ML AWS services
      • BristolMyersSquibb - Analytics & 3D Visualization - Leveraging data services to advance drug development
      • Nielsen - Unparalleled scalability - Using data lakes to process consumer insights at scale
      • HYUNDAI - Leveraging ML - Monitoring ML models performance with 10x reduction in training time

(continuously updated)

Next step

If you feel that this thread dismantling note has caught the key points you want to read and is helpful to you, I hope to continue this kind of in-depth article in the future, welcome to pat and feed me a coffee ☕, or forward the article to your friends and colleagues, community, thank you for your encouragement and support.

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