Ask Us Anything: Episode 19

Stephen

All right. Hello and welcome to “AWS Made Easy: Ask Us Anything” live stream. And this is episode number 19. With your hosts Stephen Barr, that’s me, and Rahul Subramaniam. How you doing, Rahul?

Stephen

Oh, really good. We drove out to the Eastern part of the state and we did some stargazing, because Jupiter was close by. And we had some friends with the telescope and we saw Jupiter, we saw Saturn. The next day, they had a 99.9% light filter, so we can stare at the sun and see sun spots. It was really neat.

Stephen

How about you, how was your weekend?

Stephen

Oh, Bangalore has nicer weather?

Stephen

Oh, okay. So, you get the cool…a little bit of a cool breeze?

Stephen

Oh, cool. I’m really glad to hear that. That’s another thing… Oh, here’s another thing I’m doing this…well, actually, it will be after the stream today. This weekend, I found at Costco, for sale, a DJI Mini 2.

Stephen

I can’t believe how tiny these things are. But after the live stream, I’m going to take the kids and we’re going to try and fly this around somewhere.

Stephen

So, excited to try that out.

Stephen

Yeah, get some good B-roll footage for the live stream, some good intro footage.

Stephen

Yeah, this is 4K at 60 FPS. And I’m amazed that you have this, plus the controller that takes your iPhone, came with a memory card, for like $430.

Stephen

So, I think I’m amazed at how much it’s gone down in price.

Stephen

So, looking forward to giving this a try.

Stephen

All right.

Stephen

Absolutely, let’s jump into it. I will cue our transition and we’ll jump into it. Here it goes.

Okay. So, like I said, the first one we’re going to do is introduction and itinerary. And I wanted to give this a try, where I actually put the name of the segment. Oh, I guess it blocks our names. Okay. So, introduction and itinerary. So, I wanted to show you what we’re going to talk about today. And also, since it’s behind the scenes, I’ll just share my screen.

Now, this is normally off to the side when I’m looking. But, actually, so, we see we’ve got episode 19, which is a stream. I’ll see… Can you… Do you need me to zoom in a bit?

Stephen

There we go. All right. So, I’ve got episode 19, which is a live stream. And then I had some tasks that were ahead of time. I had “create the banner.” And I wanted to give a shout-out to our really, really good graphic designer, she, you know, created the banner for the show. So, that was in the advertising. We see the… I asked her to do a little bit of a “Back to the Future” style banner. And then so that happened beforehand. We planned out a few segments. Some of those segments have these sub-tasks that had to get done beforehand.

And then we remember from last time, because we’ve wired up…we have those transitions, those transitions get generated and those URLs get populated here. So, before the show, once I’ve got all the segments planned out in the right order, I click here. That will give me a download of that little…the segment transition videos that you just saw.

So, this is our plan. And so, yeah, I said usually I have this off to the side, but since it’s behind the scenes. We wanted to talk about generating show notes. Getting a transcript, and we’re going to run AWS Comprehend on it. We’re going to show a QuickSight dashboard based on some of the results of Comprehend. And running Kendra on the transcript. And maybe other things that we can do in the future, have another brainstorm.

So, without further banter from me, let’s switch over to the show note generator, step two. Ready?

Stephen

All right. So, show notes. I want to talk about what the show notes are, and also give a shout-out to our really talented Web developer Alex. So, I’ll share this screen. Here we go. Oh, I see. Let me stop this screen and I’ll share. I wish that StreamYard could let you queue up multiple different screen-sharing windows. But anyway, here it goes.

So, at the end of the show, we put together some post-processing notes. And remember, in our “What’s New Review” segment, we’ve been giving…we can go over an article, like high-memory instances, SageMaker pipelines local testing. And then I kind of summarize what Rahul and I talk about. So, again, this is episode 16, we talked about these huge… What were the name of these instances, the 112X large with the 12 terabytes of RAM?

Stephen

Yeah, exactly. Yeah. Yeah, that’s right. So, this is what episode 16 looks like. We have our article, our summary of the discussion, our number of clouds out of five, and our verdicts. Remember, we talk about…

Stephen

I mean, and I really like the… You know, we could have added a lot of different variables to this, but I really like just the rating and also is it…does it simplify your life or does it complicate your life.

So, how do we get to this point from the show? So, I’m going to share my…go back to my ClickUp view for a second and I’m going to show you what a completed show looks like. All right. Give me a second here to swap screens. Share. I’ll go back.

Stephen

Possibly.

Stephen

Yeah. No. I mean, well, normally, I’m not zoomed in this far. But all right.

Okay. So, here is… Let’s go back to episode 16, which we just saw. So, here’s episode 16. And we talk about our segment view. Here’s the ratings that we give it. Here’s the articles, out of five. Here’s the URL. And I like this view. But, of course, not everyone uses the same tool, and I have to be aware of that. Our designer, Alex, he wants a Google Doc that he can then turn into this blog post.

So, the way we do that is we have an automation. So, I’ll show how this gets hooked up. In our stream automation here, we have a live…one of these… Ah, here we go. When status changes from “Recorded” to “Processed,” then let’s call the “createShowNotes” lambda. So, I don’t know if you can see that, but one of…

Stephen

It’s basically… Yeah. Okay. When “Recorded” to “Processed” and call the “createShowNotes” lambda. And then what that does is it will… Okay. So, let’s…

Stephen

Yes, this is a deployed…

Stephen

Yeah. So, let me go to stop my share of this and share the browser. Okay, here we go. Okay. So, here’s our lambda function. And all it does is it gets that parent stream task, looks up all of the segments associated with it, looks up a certain field called the “description,” it looks up the discrete fields of the rating and the number of stars. And then it will populate a Google Doc, which looks like this, with this table.

So, I can show how that works. I know Alex has already picked this one up, so I don’t have to…I’m not breaking his workflow. Now, if I… I’m back in ClickUp now and I’m going to switch this workflow from…I’ll switch it to “Recorded,” and then I’ll switch it to…I think it’s from “Ready” to “Recorded.” Here we go. And that should trigger the automation and this should start populating in a second. Let me double-check, I think it was. Yeah, “Ready” to… It’s… Oh, it’s “Recorded” to “Processed.” So, I’ve…it’s in “Recorded” state and I need to advance it to post-processed. There we go. And this should start populating in a second.

Stephen

Oh, gosh. Let’s see if it does it. Oh, there we go. There it is. It’s kind of cool seeing a Google document just kind of fill in. It’s giving our URL, our ratings. There it is. So, that was just based on that lambda function trigger.

And so I guess the takeaway from this automation is that one of the cool things, it’s really nice to have one tool where everyone can visit. But you also have to work…you don’t want to break other people’s processes. And so by using automation, we can then say, “Okay, we need a Google”… I have this structured way. Our Web guy, he wants a Google Doc. All right, here we go. We can make it for him really easily. And it has this nice table. It’s all powered by this one really simple lambda function. Which, again, all it does is get the parent task, get all the segments, and then it runs this “populate_google_doc” on the parent task and the list of segments and the Google Doc ID, which is also built into the ClickUp task.

So, it looks like our demo gods, that we have won favor with them. And so that’s how we do our show notes. And so it’s really nice. Because, again, earlier in the flow, we would have to…I forget to do this step and it would be…it’s a lot of duplicate information. And so, again, with that programming principle “don’t repeat yourself.”

Stephen

Right? So, I thought I’d rather not…yeah, I’d rather not have to do this twice when I’ve already got all the data nice and structured. So, that is how we create our show notes. All right.

Stephen

Cool. Well, let’s see. Should we go on to… Anything else to say about this one?

Stephen

Absolutely. It’s really nice just having this big…to not… I mean, even though, from a programmer’s perspective, it used to be fun to write the whole system yourself, but now thinking I really don’t want to have to…I want to use other people’s workflows, other people’s code, and be able to just integrate it together in a really kind of cohesive way and focus on what my task is. Which is, you know, delivering this live stream, keeping it organized, making sure we can disseminate the information as efficiently as we can.

Stephen

All right.

Stephen

Well, let’s switch over to the next segment, which is going to be using Comprehend. All right, we’ll take a quick break.

Stephen

All right. Well, here, we’ll take a 20-second break, and then we’ll be back for Comprehend. Here it comes.

Stephen

All right. We are back and we’re talking about Comprehend. So, Rahul, what is Comprehend? What’s the use of it?

Stephen

Yeah, that’s exactly it. We’re… Comprehend is really, really neat, to be able to extract this structured data out of natural text. And actually, you did a really good demo on our “AWSMadeEasy/LetsCode” repository. I’m putting that in the chat right now. So, this is…

Stephen

Yeah. It’s not very structured.

Stephen

Well, I’ll show you what we’re using Comprehend for. Okay. So, I put the Comprehend in the chat. So, what we’re using Comprehend… And I think this is really, really neat. I’ll share my ClickUp view first, and then I’ll share the…how it works. Let’s see. Here we go. Window and ClickUp.

Okay. So, in ClickUp, we’re looking at all of our episodes. And we want…we always talk about AWS. And one thing we want to know is what services did we talk about in a particular episode. And so we built this component using Comprehend. So, for example, in episode 11, we talked about QuickSight, Timesteam, Redshift, Dynamo, Neptune, which is a bit rare. Athena, Aurora, Kendra. So, we talked about all these different services.

So, I wanted to show you how this list gets populated. So, I’ll switch gears to… Again, this is all driven by automation. I’ll show you this automation first before we switch over. Here, we’re saying when we get a transcript, then we’re going to call a lambda webhook that runs Comprehend. So, let’s show you that lambda webhook.

So, I’m going to open up a dev space and share that screen with you. Right. Here we go. So, this is in my livestream-automation-v2 repository, which I’ll make public. It’s a… The cool thing about it, we mentioned this last week, this is…if you see here, it says, “Generated from AWSMadeEasy/sam-lambda-py-graviton.” So, we made a template repository that makes it really easy to just deploy a lambda function to a URL.

Now, in this case, I’ve…I have my, here, I’ll show you, my “app.py.” And it’s running Comprehend on…it’s a really simple body. Okay, when we get the event coming in, the task, the ClickUp task in JSON, is the payload. And so we want to run “comprehendTask” on that task. Now, what does “comprehendTask” do? Well, it gets the transcript, it gets the episode number, it runs “detect_entities,” and then it looks for entities of type “TITLE” or “COMMERCIAL_ITEM.” And then it will populate this field in ClickUp with AWS services.

So, I can show you what it looks like when you’re interacting with Comprehend. Give me a moment to get this set up. Let’s see here. “app.py.” Load in all the function definitions. And then we should have a task. Okay, great. So, we’ve got a task. And now, let’s run…we’ll step into “comprehendTask” over here.

All right. So…

Stephen

Oh.

Stephen

Yeah, I should have gotten… Oh, no, I see what I need. I need to populate a variable. Give me one second to… This is one variable I don’t want on the screen. This is my API key. Give me one second. There it is. Okay. One second here. Okay. Clear, and back on.

Okay. So, just stepping through this code, it’s getting the task, and then it’s running. There we go. Let’s look at the entities list. Entities list. Okay, here’s…we found an organization called Lambda, a commercial item called S3 SageMaker, a title called EC2, an organization called Amazon, and so on.

And so this here would… I’m just…this actually is probably not needed, we’re just aggregating it to some…I want to get a count in here. So, we mentioned EC2 once, SageMaker once. And now, what I do is, to map it to ClickUp, I get a list of the custom fields. I already did that. One second.

Stephen

Completely agree. It makes this so much easier.

All right. So, what I’ve done is I’ve queried ClickUp for my AWS services field, and these are all the UUIDs of each of the choices that I have. And now I just map what I found to the field values there. And I create a… Okay. So, in this case, I found EC2. And that other one, S3 SageMaker. I look at the value list, S3 SageMaker. That’s still…you know, it’s still natural language processing, sometimes it doesn’t separate it perfectly. So, it will give me this one value that I can update.

So, I wanted to show you how this all ties together. So, at the end of the episode… For example, here’s a speech, Rahul, that you gave recently at the AWS Summit Anaheim. We’ve got the transcript. Now, I’m going to switch over to my ClickUp view. I’m going to ask the StreamYard people if there’s a more efficient way to share multiple windows at a time. But here’s my ClickUp view. Now, here is your CloudFix talk that you gave. And I’m going to go over here and I’m going to paste in the transcript. So, we have automation that would populate this based on a webhook, but we’ll do this manually. Here is the transcript, we will close that. And that’s going to fire out automation. And we’ll wait a second or two for that. Let’s refresh ClickUp and see which AWS services you mentioned in your talk. Okay, there we go.

Stephen

You talked about CloudFront, SageMaker, Redshift, Dynamo, Athena, Aurora, EC2, EBS, EFS, and S3.

Stephen

Awesome. And then so now, if…

Stephen

Well, it’s really cool just to think about what are we actually talking about, we can do other analytics on this. If we wanted to then filter and say, “Well, AWS services, what are all of the times that we talked about,” I don’t know, “Redshift?” Okay. And then it goes back here. It says we talked about Redshift in episode 11 and episode 12 and in Rahul’s CloudFix Anaheim talk.

Stephen

So.

Stephen

And we can look at… So, the other thing that we want to look at, thinking about the future, is we can also make a custom entity recognition model. So, for example, when we talk about, you know, certain products that we like that aren’t S3. For example, we talk about dev flows, about dev spaces. We could make a custom entity recognition model that’s looking for those. And then we could look at when do we mention those, when do we talk about other companies, other people.

Stephen

Because people gets tagged as its own entity. So, we can run custom entity recognition models using the same template, and then having them mapped to a fixed set of tags. It makes it really easy to stay organized. I think that’s really the name of the game. When we were five or six episodes in, it’s really easy to remember what we did for each episode. But now we’re at, wow, this is episode 19. It is easy to forget when we talked about a particular topic. And so by being able to stay organized in this way, it’s been really helpful.

Stephen

So, let’s see.

Stephen

Yeah. I’ll put this on here. Oops. Add the stream. There we go. So, I’ll go to “AWS_SERVICES” and look at “SageMaker.”

Stephen

Here’s the times that we’ve talked about SageMaker. Let’s see. We’ve talked about it in episode 2, episode 7, the “What’s New Review.” Let’s… And we talked about it in 10 and 16.

Stephen

Well, let’s have a look at 16 and see which segments we covered. Okay. So, it’s… Here we go. We talked… No, that was the SageMaker pipelines.

Stephen

Let me add this filter.

Stephen

Yes. I think this is actually a really good… What do you call this? This is a really good preview. Because even this is good for tagging, and eventually I want to tag the individual segments. Okay, here we go. Segment “SageMaker Autopilot experiments are now 10x faster.”

Stephen

Yeah.

Stephen

And then we can look at the… We’ve got the article URL. This was with Badri. So, pretty neat. We have all that mapped. Now, some of this automation we’ve developed as we’ve gone along. So, I’m still kind of backfilling all of the processes.

Oh, and also, there’s one thing I also wanted to share, I guess as part of this little sub-segment. The data model that we’ve come up with to kind of manage all this. So, we’re talking about segments and highlight as if they’re, you know, very distinct things. Let me share this, share my screen. Here we go.

So, this is…I think this makes sense in terms of how I think about it. So, when we’re planning, we’ve got segments that we want to talk about. Right? We’ll say, “Okay. First, we’re going to talk about our show notes generator. Then, we’re going to talk about Comprehend. And then, we’re going to talk about QuickSight. And then, we’re going to talk about Kendra.” So, we have this linear progression of segments that we’ve thought through. And we don’t know how long we’re going to talk. Sometimes you can talk a long time or a short time.

So, then, when we actually record it, we run those transitions that, again, we talked about last time, that we generate, that put a few of these frames in here that we can key on. And then we talked about in the last episode segment…AWS, this is, recognition segment detector API is going to tell us exactly when that happens.

And then, so, we run that automation against our video, we get these numbers, and that helps us generate what we call highlights. So, highlights, we have one highlight per segment, but we can also have more. For example, if we just have a short little quote that we wanted to talk about. So, a highlight, it’s at least a one-to-one with the segments, but sometimes there’s more.

And then when we post the highlights, depending on where they’re posted… So, these get posted to LinkedIn, YouTube, and our Twitch channel. And then when we make a social media post, we’ll post the highlight, plus some commentary, in the social media post. But the trick thing is on LinkedIn and YouTube and Twitch, you’re not really limited. But on Twitter, you’re limited to a 90-second clip. So, we take the highlight and generate what we call a preview highlight, which is, at most, 90 seconds. And then it says…we have this little three-second video that we tack on to the end that says, “For the full video, see the link in the tweet.” And that way we can use Twitter live, but stay under that 90-second threshold.

And so that’s our flow. And so this whole process is managing this from our free-form recording here, plus the segments, all the way into this very structured view. And then I think in the…in a future episode, we can show how we actually gather this all back in terms of a big-picture view of how the social is doing.

Stephen

Cool. So, that’s our data model. And so when we’re talking about segments to highlights to social posts, that’s what we’re doing.

Stephen

Well, let’s jump out of order here and let’s go to Kendra. Here it goes.

Okay. So, Kendra is super cool. So, here’s the flow we want to talk about with Kendra, let me put this up on the screen. So, you saw here that we have our transcript as a field in ClickUp.

Stephen

We have…

Stephen

Oh, here we go. So, we have our transcript as a field in ClickUp. Now, when we also set that transcript, we also have a lambda function that’s going to take that transcript and it’s going to write it out as a text file and put it into an S3 bucket. Oh, not a video bucket. Again, this is behind the scenes. Just an S3 bucket. Now, this bucket is configured to talk to Kendra, and Kendra is watching it, and then we can query it.

So, let’s look at it from the AWS perspective. Here is our Kendra setup. I probably should zoom in here. So, we have… I’ll have to regenerate some of this later. Anyway, we have an index, we have a data source. And now, let’s have a look at how we can interact with it.

So, I’m starting up…

Stephen

Yeah, absolutely. All right. Here it goes. And again, I remember at one point I worked at an e-mail hosting start-up and I remember building my first search engine literally as a wrapper around grep. So, you had a “cgi-bin” script which was taking some input, calling grep on the file system under the hood, parsing the results back, and then, you know, using, you know, old CGI, if you remember that, and posting it back. And it worked kind of well. I remember at some point thinking…like, I understood the Perl regular expression engine well enough to think, “I know how I can crash this.” And I searched for a sequence of characters, and sure enough. And that’s the thing that you’ll always be dealing with that.

Stephen

Even now, 25 years later, you’ll still be dealing with random issues that come up if you try and roll your own.

Okay. So, here is Kendra. So, again, just showing the…how it works. I think I showed that. Yeah. So, we have our episodes that go into an S3 bucket, Kendra indexes it, and then we can query it. So, here’s a dev space that’s located…that has a Kendra script in it. And I’ll show you how we can interact with it. So, let’s say “python3 kendra.py.”

Now, a few episodes ago I told a story about how my Australian wife went into a store and she ordered hot chips. And they microwaved a bag of potato chips and handed it to her. And I don’t know if they were just joking with her or what they were doing, but it was a funny anecdote. And I don’t remember exactly what episode it was. So, say if we look for “French fries hot chips” into Kendra.

Okay. Now, it gives us the answer. Oh, our first one is an answer. It says in the episode 12 episode, and then here’s the little sentence that we use. I guess I was talking at this point telling…in our opening banter, telling that funny story. Oh, there’s episode 12. So, that’s our answer.

Now, let’s see. What other things do we have? Oh, well, here, document three. So, then, the score starts dropping. We’re talking about “hot,” it being hot. So, it’s not matching the “chips” part, but it’s matching “hot.” And then later, we talked about Rahul’s plants in episode 10 being hot, but we used the word “44 centigrade” and “hot.” So, then, you see the search kind of goes down.

What about a time we talked about stored procedures? Let’s do “stored procedures.”

Stephen

Well, we have a couple of times, but they’re always… Let’s see. So, the most one here. Here we go, episode 15, “1,400 stored procedures.” Just a mountain of stored procedures.

Stephen

Yeah. Here’s episode 11, “…we look at sediment buildup under these rocks…that’s obviously stored procedures calling stored procedures.” Here we go, episode 12, “SQL, where it’s both a strength and a weakness that you can do something…complicated.” So, again, episode seven, we talked about stored procedures. I don’t think we ever talk about them in a great way.

Stephen

Let’s see when we talk about SageMaker. This is cool. Okay, SageMaker. Episode seven, “SageMaker Data Wrangler,” “SageMaker Canvas.” We talked about it in episode 16, SageMaker cloud, “Why would they do this?” Still, again, we found that one.

So, it’s really neat. We can now search our documents intelligently. I think when we have a few more episodes under our belt, we’ll be able to use the SageMaker querying, which will be kind of fun.

Stephen

Okay.

Stephen

Let’s… Episode 12, document.

Stephen

Interesting.

Stephen

Look at this one, “graviton.” 14, 13, 13, 12, 11, 18, 4, 15. We like the Graviton. And we can…you know, we should run sentiment analysis on all of our different topics.

Stephen

On the text near. And then, we can… That would be a funny table to generate. I’ll work on that for next time. So, I’ll run all of the different AWS services, run a Kendra search on all of them, and then report our sentiment. Because I think that would be really…

Okay. And, okay. So, we got a comment from a viewer, really happy about this one. “Whoa, I just joined with just a raw idea of AWS from end user perspective…curious about all this…to see the magic happening…” All right, thank you. I hope you do check out the full episode and let us know if you have any questions or ideas for a future one. And I think this summarizes the recognition of Kendra, is just, yeah, that’s how we feel about it, too. Kendra is super cool. And this is stuff that, you know, it’s only a few lines of Python and mostly, you know, moving data around. The magic is really just pointing it to an S3 bucket full of text and asking it questions.

Stephen

All right. Well, anything else we…

Stephen

What I really like, in addition to everything you just said. Just thinking back to the promises of the semantic Web projects, right? Where they wanted to add all this structure. And that was a real… If you dig back through some of that stuff and all the stuff that got invented, it was really interesting to see, “Okay, how much”…if you have to do this manually, when are you talking about a person, when are you talking about a place, when are you talking about a quantity. And in order to, like, actually write semantic-compliant documents, how cumbersome it is, and you have to learn about RDF triples and all this stuff to really be able to add the structure to your document.

But now, you can just say…get it into a text file. If it’s an audio, that’s no problem, you can get audio or video to text pretty quickly. And then, now that it’s just in plain old English, or whatever language, you can add structure to it automatically and be able to say, “Here’s the persons, here’s the places, here’s the things, here’s custom entities that we have trained our model on to look for,” and add all of that structure in after the fact.

Stephen

Which is…it’s really, really exciting. And like you said, because you’ve stored all those meetings, you can then run it retrospectively and add all this richness. It’s not just, you know, going forward. You can, you know, through one big batch job, push all your meetings through. It’s really cool.

Stephen

Actually, I used Textract at a consulting product a few years ago where these people were working with a supplier that were very old-fashioned. And there was no ERP or any kind of accounting software, they just sent a PDF with, you know, item number, order date, whatever. And we set up a system where the guy who was receiving these orders, whenever they sent him the invoice, he could just forward it to an e-mail address that was dedicated to Textract and Textract would take it from there. And we didn’t have to… Because before that, he was literally opening up this PDF, opening up his Xero accounting software, and just moving the data around. And with Textract, it made it so much easier to get all these different structure out of this data. Because it was really neat data, but they would only give us PDFs, the vendor. So, Textract is a great way to go.

Stephen

Oh, I wonder how Kendra would do with legal questions.

Stephen

Well, you know, let’s…I’ll reach out to someone on the Kendra team. Because I think we have enough questions that it would be good to have a full interview. So, I’ll reach out to someone on the Kendra team, let’s make that happen in the next couple of weeks. That would be really fun.

Stephen

Well, should we do our last segment?

Stephen

All right. We’re going to show you QuickSight.

All right. So, I’m going to share my Web…my browser again. Here we go. There we go. Okay. So, QuickSight. Now, looking at the QuickSight dashboard, here, I’ll just show you an example. So, we’re taking our transcripts, and then running Comprehend on them, getting the tags. Another thing I did was just dump the text files into an S3 bucket. And now, here is what our QuickSight dashboard looks like. I did a word count because the numerical quantities, you know, that’s a bit more proprietary. But the word count here, let’s see if we can do this.

So, let’s look at a word count by episode. Let’s go to episode…an older one, let’s go to episode…well, actually, let’s go to 12. Let’s see what we talked about episode 12. Interesting. Wickr Pro. We still have to follow up on Wickr and see how that’s going. Episode 13. So, these are…what I’ve done here is I’ve made a size-weighted dashboard on the different entities that we found in the transcripts, and then parameterized it by the episode number. So, if you’re a Tableau user, this would feel very familiar, QuickSight.

Now, Rahul, what are some of the uses of QuickSight that we use?

Stephen

It’s really powerful. And having been around and seeing people in the Tableau world, right? This can get you so… I mean, this is very, very much in the same realm. It’s really easy. I mean, I’m not a…I hadn’t used it very much before, but it only took me a few minutes of experimenting to… Literally, all you do, you make a data set here. You can point it at an S3 bucket or you can… Let’s see.

Stephen

Oh, here we go, new data set.

Stephen

Let’s make this a bit bigger here. Yeah. So, you have lots of choices about where you point this, where you want to analyze. I want to try the Twitter analysis at some point.

Stephen

You point it to an S3 bucket, Athena, MySQL. We do all of that, and then, literally, just make a dashboard. You can, you know, drag and drop different dimensions, look at different types of visualizations. And I think it’s a really fun way of just, again, quantifying and understanding what we’re talking about. I’m going to make a dashboard we can use to understand, you know, once I’m pulling together all the different social data, to understand which of our highlights are more impactful to understand how all of that works. But it’s really exciting that we have this to visualize and, you know, we don’t have to think about building this out ourselves. Right? It’s having to…not having to deal with plotting engines and all of the… You know, behind the scenes of all of this is some really nitty-gritty code about joining and dealing with date formatting and all of this stuff. We can just focus on the data that we actually want to display.

Stephen

Oh, it’s so much better than trying to… I still remember, again, the custom dashboards that I wrote where I’m trying to script R to process a CSV file, dump a PNG somewhere, and then pull it back in. And it just…it becomes so fragile. Having, yeah, this very rock-solid dashboarding solution that’s integrated with all of your other data sources, really neat.

Well, we have about a minute or two left. I wanted to see if we wanted to brainstorm for future automations or talk about any of those things. Let’s just do a quick transition.

So, what do you think? What’s the future hold for automation, for us and in general?

Stephen

112.

Stephen

For the big SAP HANA instances. Or, actually… So, and I think I like that idea we came up with earlier, is take every service, run it through Kendra, take the blob of text that comes out, run that through, I guess it would be, Comprehend to get the sentiment.

Stephen

And then just have a little table.

Stephen

No, I want to be able to search the transcripts for each topic.

Stephen

And then once we have the little snippet of the service that we’re talking about, to get the sentiment of the text surrounding that snippet. It’s going to say, “Okay, every time we talk about stored procedures, sentiment negative. Every time we talk about”… Let’s see, what’s a good one? “If we talk about lambda function URLs, that’s positive.”

Stephen

And then maybe there will be some that are mixed. And I would love to see the distributions of each of those.

Stephen

Oh, we might be in trouble.

Stephen

All right. We’ll have to see. You know, sarcasm is hard enough for some people, so can’t blame Comprehend.

All right. Well, this has been a really fun show and hopefully it was useful to the audience, of automating your own live streams. There will be a blog post out by the end of the week that will have some template repositories that you can go play with, whether it’s Kendra, whether it’s Comprehend. And again, I hope this is useful to you, and please let us know if you have any questions. And we’ll see you next… Let’s see. Not next week, but the week after.

Stephen

All right. My pleasure. All right.

Stephen

Have a good one.