[MUSIC PLAYING]
TIMOTHY: Hi, I'm Timothy, and I'm here at Google Cloud Next
London 2018.
And I'm standing with Deepti, who just gave
a session on a Cloud Spanner.
Now, what's one feature you talked
about today for the first time you're really excited about?
DEEPTI SRIVASTAVA: So today at London Next,
we launched DML, which is Data Manipulation Language, which
is basically SQL Insert, Update, and Deletes,
and I demoed that at my session, which is very exciting.
And this feature basically makes it look and smell and feel
a lot more like traditional relational databases,
so they're very excited to have the feature.
TIMOTHY: I imagine that really reduces
the complexity of the Getting Started experience with Cloud
Spanner.
DEEPTI SRIVASTAVA: That's exactly it.
Spanner is basically a no compromise database,
and our customers love it for that
because you get horizontal scalability, as well
as relational semantics, transactions,
and strong consistency across the globe that nobody else has.
However, this was the one key feature
that was missing for our customers
because they wanted to use existing ORMs, existing
tooling, et cetera, with Cloud Spanner,
which becomes a lot easier to do now with DML,
with SQL Insert, Update, Deletes,
and you can use JDBC drivers.
We're launching the database driver with this capability
today as well, so it just much easier
for you to use ORMs, existing tooling,
and to migrate existing apps or create new apps with this.
TIMOTHY: Thank you, Deepti.
DEEPTI SRIVASTAVA: Thank you, Tim.
I'm sitting here with Robert Saxbey,
who gave a session yesterday on-- was it blobs to tables?
Is that right?
ROBERT SAXBY: Blobs to table--
you got it absolutely right, yeah.
TIMOTHY: Robert, what was that session about?
ROBERT SAXBY: So that session was about the different storage
types that we have at Google Cloud
and how to choose which one for your use case.
TIMOTHY: If you were to choose one as your favorite,
what would it be?
ROBERT SAXBY: Oh, I have at least three favorites.
TIMOTHY: Give me two.
ROBERT SAXBY: Give you two?
OK, I'll give you two.
One is Bigtable.
I love our ability to be able to actually random read
a single row of data, a single digit latency,
and scale that up to infinite size.
We have customers really reading 10s
of millions of rows per second, and they
can scale that up on demand to be
able to address their use case.
The second one has to be BigQuery.
I'm a data analytics guy, I mean,
so I'm heavily involved-- heavily invested.
So BigQuery-- first of all, it's a common misconception,
say, BigQuery, they think, oh, it's just this one thing?
Yeah, it is one thing.
It's our data warehouse, but our data warehouse
consists of two parts.
It consists of the storage, which is capacitor,
and it consists of the serving tree, the part that
actually runs your SQL query.
Now, from that serving tree, that seat where
you run your SQL, you can actually
query our native storage capacitor
or you can query storage which is federated to Google Cloud
Storage.
But when it's that native storage,
we get absolutely phenomenal performance.
Think about, you have a petabyte of data in a table.
How long does it take to query that?
TIMOTHY: Forever.
ROBERT SAXBY: Forever, absolutely.
So what we do, we partition it, and we cluster it.
So there was another talk yesterday,
a talk on BigQuery performance [INAUDIBLE]..
Then he showed how we can actually query that data
and get it within a few seconds by applying
the right partitions and clusters to your data.
TIMOTHY: That's amazing.
And it actually goes a lot quicker than forever.
ROBERT SAXBY: Yeah, that's a lot quicker than forever.
TIMOTHY: I'm standing next to Henry Bell, who
gave a session on Kubernetes.
Henry, what was the one part of the session that gets you
most excited about Kubernetes?
HENRY BELL: Right, sure.
So we're doing quite a lot of work
to integrate the native Kubernetes world with JCP,
services so thinking about things
like Cloud Armor for DDoS protection,
the Identityware proxy for strong authentication,
and the integrations with our global load balancer.
So this is a globally distributed system
that allows us to provide a single any cost IP address,
and this means that we can route users to resources
that are near to them.
So for example, you might have an application
running in two Kubernetes clusters
in two different regions, which means that users are routed
to the cluster that's nearest to them, which means that we can
improve on latency and offer the best user experience we
possibly can, as well as being able to provide
nice things really strong, solid [INAUDIBLE]
redundancy, for example.
TIMOTHY: That's awesome.
Thank you so much, Henry.
HENRY BELL: Thank you.
TIMOTHY: I'm sitting with Oliver Madden, who gave a session
today about the Chrome browser.
Oliver, could you tell us what's your favorite feature
of the Chrome browser that, say, you use in Enterprise,
but average users might not know about?
OLIVER MADDEN: I'd probably have to go with legacy browser
support.
TIMOTHY: Tell me about that.
What is it?
OLIVER MADDEN: Basically, it's an extension
that allows admins to populate URLs that specifically
need legacy technology, so Silverlight, ActiveX, et
cetera.
So you can help your users when they
need that particular technology, Chrome
will flip into your designated browser,
probably IE in this case, just for the duration of that.
And then when they need or when they
can use a more modern browser, and they key in the URL,
I don't know, Gmail.com, it will flip back into Chrome for them.
TIMOTHY: That's amazing.
OLIVER MADDEN: Seamless.
TIMOTHY: Awesome.
Thanks so much, Oliver.
OLIVER MADDEN: Thank you.
TIMOTHY: Now, Lee's doing a demo in the Demo Derby later.
Lee, could you give us a quick synopsis of what the demo is?
LEE BOONSTRA: Sure.
So I'm building this customer care application
where I'm using Dialogflow and other Google Cloud
components to create a real chatbot experience.
TIMOTHY: That's awesome, and what I really
like about the demo is that you're
using a bunch of different JCP components
so that you can create a complete solution
for this customer care.
LEE BOONSTRA: Oh, yeah, I'm using a lot.
So actually, I'm using Kubernetes first of all,
so I create a front end container
and then back end container.
The back end container that talks to Dialogflow
through the SDK.
Then every time when customers, they
type something in the chatbot, we pass it also to Pub/Sub,
and from Pub/Sub, there is a cloud function
that captures the message.
It goes into the LP API to remove sensitive information.
Then we move it to a third LP API to detect the sentiment,
so people can really figure out what people are thinking
and if they're happy or not.
And then we pass it into the data warehouse like BigQuery
so we can push it into a dashboard,
and then you can really see some awesome things
and start to optimize your chatbot.
TIMOTHY: What a cool flow.
LEE BOONSTRA: Yeah.
TIMOTHY: What's your what's your favorite technology in there?
LEE BOONSTRA: Definitely Dialogflow.
TIMOTHY: Why is that?
LEE BOONSTRA: Because it's so easy to create a chatbot.
You don't need to be technical to set up a chatbot,
but same time, you can go very deep and use the SDK
and do all crazy stuff.
TIMOTHY: Awesome.
Well, if you want to find out some more,
click the links in the description below.
Thanks, Lee.
LEE BOONSTRA: Thank you.