AWS re:Invent 2019: [REPEAT 1] Amazon DynamoDB deep dive: Advanced design patterns (DAT403-R1)

I welcome everybody now thanks for taking this time out of this evening here in Las Vegas where there's many many other things to do to listen to me talk a little bit about data modeling and no SQL who knew that topic would be so popular huh my name is Rick Houlihan I am a principal technologist for no SQL for Amazon Web Services I've done a lot of no SQL data modeling over the years I worked on the specialist team I helped build the north-american practice there and I've worked on the blackbelt team internally to help migrate over 12,000 Amazon Services or some some odd number around there over several years as well to from Oracle to know SQL how many people have heard of that migration we've shut down 3,000 Oracle instances all right few you folks out there and in the course of doing all this we learned a lot of things and that's what I'm here to talk to you a little bit about tonight and some of these things might be surprising to some folks and maybe less so to others a lot of this stuff we we're going to get more into some of the complex modeling that we can actually do with with no SQL that people might not be so aware of so the first thing I like to talk about in all of these sessions is a brief history of data processing why are we looking at no SQL I know a lot of you have already heard this but some of you haven't and the real reason then the reason I do this is because if you want a message to be understood you have to repeat it it has to be broadcast so for those of you who've heard it I ask you for a minute or two for those of you haven't hopefully you'll get something out of that conversation we'll get into a little bit of an overview of Dinah mode to be more from a you know structural perspective what is no SQL I'm not going to talk a lot about api's features and functions that's not what we're here for really here to talk about NoSQL data modeling that's what this session is really about how do I actually model data