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From YouTube: Dagster Cloud Demo
Description
Dagster Cloud is the easiest way to get started using Dagster. This video explores how Dagster Cloud lets developers build, test, and deploy their code with confidence.
Get started with Dagster Cloud here: https://dagster.io/cloud
For more information on getting started using Dagster, check out this demo: https://www.youtube.com/watch?v=lRwpcyd6w8k&t=0s
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With
our
code
successfully
running,
let's
go
ahead
and
make
a
change
we'll
open
up
the
source
file
in
our
github
repo.
Here
we
have
a
few
software-defined
assets.
The
first
pulls
a
list
of
serials
from
an
api
into
a
data
frame.
The
second
filters
down
to
serials
manufactured
by
nabisco,
we'll
add
another
asset
which
filters
down
to
serials
manufactured
by
quaker.
A
Dexter
cloud
is
built
to
scale
with
the
size
of
your
data
team
and
your
data
workflows.
Let's
take
a
look
at
what
the
development
process
on
a
more
mature
data
platform
looks
like
here:
we've
loaded
a
set
of
data
pipelines,
our
code
fetches
and
processes,
some
population
data
producing
population
rankings.
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Next,
we'll
add
a
country
gdp
asset,
we'll
pull
some
data
into
a
data
frame,
then
we'll
add
the
per
capita
gdp
asset
we
need
to
depend
on
our
existing
gdp
and
population
assets.
Dagster's
programming
model
lets
us
specify
them
as
inputs
to
the
asset
function,
we'll
join
the
input
tables
compute
the
per
capita
gdp
and
return
it
we'll
create
a
branch,
commit
our
changes
and
push
up
to
our
github
repo.
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Branch
deployments
run
against
a
forked
environment
that
we
can
test
against
to
gain
confidence
in
our
work.
Here,
we've
configured
our
branch
deployments
to
create
a
temporary
copy
of
our
cloud
environment.
This
job
creates
a
duplicate
of
our
snowflake
schema.
Our
pipelines
and
assets
are
parameterized
to
write
to
this
copy,
so
we
can
materialize
assets
in
our
branch
deployment.
Without
interfering
with
production,
I
can
now
go
ahead
and
kick
off
a
run
to
ensure
that
my
changes
work
and
a
context
nearly
identical
to
production.
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Of
course,
these
branch
appointments
scale
with
the
size
of
your
team.
Every
pull
request
spawns
a
new
branch
deployment.
It's
easy
for
me
to
get
context
into
a
team
member's
change
as
a
reviewer
or
another
stakeholder
by
viewing
their
branch
deployment
where
I
can
explore
their
jobs,
their
assets
and
their
runs.