i'm excited to announce
the launch of amazon quicksite which is
a very fast
easy to use cloud-powered business
intelligence service which is available
in preview today
what the service allows you to do is
usually when you actually want to get
your first visualization and insight
from a product it takes
you know several weeks sometimes months
of an engineer doing data modeling
this is very different what quicksite
does as soon as we recognize
an aws customer we take all their data
that's stored in the various aws data
stores because so many of our customers
are storing data with aws
and we move it to our query engine so
that you can get your first
visualization within 60 seconds
and then we built what we consider a
pretty revolutionary
very innovative super fast parallel
in memory calculation engine which we
call spice
that our customers will be able to enjoy
much faster query response times than
they're used to enjoying
and we also believe that our bi partners
will be able to use for their own bi
tools to increase the query time
for their customers we also have very
beautiful and attractive visualizations
that you'll have at your disposal
you have the ability to capture graphs
or tables or stories and share them with
either internal employees or external
folks if that's what you want to do
the service integrates with all of aws
data stores which is important for our
customers
and we're offering it at a tenth of the
cost
of the traditional business intelligence
providers
to give you a demo of what quicksite
looks like
and show you a little bit more about it
it is my privilege to introduce to the
stage
the one and only dr matt wood
good morning
thank you andy good morning everybody uh
it's my privilege to talk to you a
little bit more about
amazon quicksite a lightning fast
business intelligence service
which aims to bring the power of
analytics to you
and everybody inside your organization
so as we were building out amazon
quicksite we knew we had to make it
as easy as possible for customers to be
able to work with the large data that
they have already stored on aws
and we focused on that in really three
ways the first was we wanted to make the
service as easy as possible to use
and what that entails is the ability to
be able to
look inside the data sources that are
already available
in your account on aws so amazon quick
site will go and with the right
permissions take a look at data inside
redshift
amazon rds dynamodb data that you have
stored in hadoop
in elastic mapreduce even flat files
that you have stored in amazon s3
then we take a look at not just the
sources of that data but we start to
inspect the data types
inside that data and look for the
relationships between those data types
and what this allows us to do is build
up a lot of information and intelligence
about the types of data that you're
working with and that allows us to
select the best visualization
for your data automatically using a
technology that we call
autograph and not only does autograph
make
recommendations we're also just using
the tools and the data and the
information that we've inferred
we'll make recommendations of not just
the types of graphs of the best way to
display your data
but the types of variables and data that
you want to plot against each other to
try and extract that visual
visualization and analysis the second
area that we knew we needed to focus on
was allow customers to analyze data as
quickly as possible
and to do this we created a custom data
layer which is the super fast
parallel in-memory computation engine
or something that we call spice and what
spice allows us to do
is to collect and compress data in a
columnar
way and optimize it for in-memory use
then we take queries and analytic
questions that you want to run against
that data
we compile them down we automatically
create the machine code
and we run them against that compressed
data and that allows us to do rich
multi-pass calculations against the data
that is stored
in spice these happen extremely quickly
because of these three things of
compression
compiled code which goes down to machine
code and then multi-pass calculations
spice is also designed to be as
extensible as possible both for us to
expand its capabilities over time but
also to allow partners to plug into it
over time as well
so you can access spice using the amazon
quick site user interface
but also partners such as domo click
tableau
and typco jaspersoft can plug directly
into spice and
access and run the same queries against
your data on aws
the third area that we needed to focus
on was to make it as easy as possible to
tell
data driven stories with your colleagues
so what amazon quickside allows you to
do
is to build and share and collaborate
around real
live analyses you can look at these
analyses inside the tool
you can also embed them on web pages on
the intranet
on public web pages you can even embed
them inside your org
inside your application you can use the
exactly the same tools to build out live
dashboards so you can collect and
aggregate different types of data
and different analyses and look at those
in real time up on the dashboard
and you can even do all of that on the
go with mobile apps
native mobile apps for ios and android
so with that i'd like to ask john to
bring up the demo so what you're looking
at here is your first look at amazon
quick site
and this is the home page here we can
see some analyses that we've previously
done
and you can see all the different types
and graphs of that are available to us
in quick site
you can see pie charts and histograms
and scatter plots
even heat maps and pivot tables so what
we're going to do
is we're going to drill into some retail
sales data
and we're going to see what information
and discoveries we can make based on
that data
so here over on the left hand side of
amazon quick site you can see all the
different data types that are available
to us
and the service itself has gone on and
extracted those from the data sources
along with the relationships
between those data types you can see in
the bottom left-hand corner
autograph automatically recommending the
types of visualizations and analyses
that we might want to run first
this is really helpful if you're coming
to a fresh data set without too much
context
and then we can switch tabs and start to
look at some of the other data types and
visualizations that are available to us
pie charts tree maps and again bar
charts
so this is retail data you can see just
how fast this analysis is this is
running against
tens of millions of records of retail
data and you can see
here broken out by category the number
of sales
so the longer the line the more of a
particular product we've sold
you can see fitness here and computers
and outdoor equipment
you can see that john's able to just
mouse over and get a summary of that
information
so now let's start to dive into this
data and see what intelligence we can
gather from it
directly from the visual without having
to write any sql or any programming
we can start to slice and dice this data
so what we can do is we can focus on
just the information that we're
interested in in this case our top
selling category fitness
then we can start to segment this out so
we can look at not just the category but
we can start to refine that query and
drill into it
looking at the subcategories so here you
can see the weight accessories and
turnstiles and monitors
that people are buying in the fitness
category again we can mouse over
and we can start to drill into this in a
bit more detail
so let's add another dimension this is
aggregated sales information but it's
likely that we might be interested in
looking at the trends of how these
things are selling
so what we can do is we can take a look
at these orders over time
and just with a single click autograph
has changed the
graph type into a line chart and here
you can see the annual
sales for each of these product
categories extremely easy and extremely
automatic
so this gives us an idea of the trends
but let's start to really drill in
so let's get a little bit more granular
and move just not from just looking at
this year by year
but looking at this month by month so
again directly inside the visual without
any real knowledge
of writing the code we can start to
drill into and start to visualize that
data in more detail
so here you can see the trends that you
might expect to see
we can see a peak around the holiday
season as people are buying presents for
each other
but over here on the right hand side
there's a peak that maybe we didn't
expect
and so we can take a quick hover over
that and we can see that we had a spike
in yoga accessory sales so let's drive
into this and see if we can figure out
what the cause
of that sale was we can focus in on that
just that one piece of data we can
remove
all the extraneous information so we can
just focus on that one piece of
information that we're interested in
we can restrict that query not just
looking at all of the information
we're starting to zoom in on just the
year by just adding a filter just a
single click
and then we can add granularity to that
information again directly from the
visual
not looking month by month but looking
week by week
and now that spike that we saw earlier
is becoming really really clear
we had a great week in the middle of
april for yoga accessories
so let's see if we can find the root
cause for this now we can start to
overlay additional
information and look at this same data
through the lens of marketing promotions
so our question is really did the
marketing promotions we ran have any
impact on sales
an age-old question so as we start to
add in the promotion data
you can start to see that yes the sales
have been attributed to the marketing
promotion that we ran
around spring fitness and so we can see
here that from a standing start
in probably less than about 90 seconds
we've gone from
the data we've sliced and diced it we've
segmented it
and we started to drive real
intelligence that are going to be really
useful
to our business in fact i think that
this is going to be so useful
that i probably want to be able to share
it around my organization
so we built in these sharing
capabilities directly inside
amazon quick site so with a single click
we can go ahead and we can capture
not just a snapshot of the data but the
live data that this is running against
and we can put that into a storyboard
and that storyboard allows us to build
an interactive document
which tells a data-driven story in much
the same way as i've been able to do
with you here today
and i can share this with my colleagues
internally i can have them
interact with change the axes query the
data
and then tell the same story with the
data to get the same results
so you can see here spring fitness again
and again these are not
snapshots these are interactive queries
which we could go ahead and embed on our
internet
inside our applications or even
published onto the public website
so with that i'd like to thank you very
much i can't wait to see what you all do
with amazon quickside
thank you
you