►
Description
The CNCF End User Technology Radar is a quarterly overview from the CNCF End User Community which shows what end users really use and recommend in cloud native. Join Kunal Parmar (Box), Marcin Suterski (NY Times), Jason Tarasovic, (payitgov.com) and Jon Moter (Zendesk) as they discuss what projects and trends they chose for Observability. Moderated by Cheryl Hung (CNCF).
Speakers:
Kunal Parmar, Director, Software Development, Cloud Native @Box
Marcin Suterski, Software Architect @The New York Times
Jason Tarasovic, Principal Engineer @payitgov.com
Jon Moter, Senior Principal Engineer @Zendesk
A
A
Hi
everyone
I'd
like
to
thank
everybody.
Who's
joining
us
today
welcome
to
today's
cncf
webinar,
which
is
on
the
end
user
technology
radar
for
september
2020
on
observability,
I'm
cheryl
hung
I'm
the
vp
of
ecosystem
at
the
cncf,
and
I
will
be
moderating
today's
webinar
and
we'd
like
to
welcome
our
presenters
tonight
today.
A
Kunal
palmer
who's
director
of
software
development
at
box,
martin
ciutersky,
who
is
software
architect
at
the
new
york
times,
jason
tarasovic,
who
is
principal
engineer
at
payetgov.com
and
john
motor
who's
senior
principal
engineer
at
zendesk,
a
few
housekeeping
items
before
we
get
started
so
during
the
webinar
you're,
not
able
to
talk
as
an
attendee,
but
there
is
a
q,
a
box
at
the
bottom
of
your
screen.
Please
feel
free
to
drop
your
questions
in
there
and
we'll
get
to
as
many
as
we
can.
At
the
end.
A
A
A
All
right
so,
as
I've
already
said,
today's
webinar
is
going
to
be
about
the
end
user
technology
radar.
So
I'm
cheryl.
Basically,
I
work
with
end
users.
So
end
users
are
companies
who
are
not
selling
any
cloud
native
products
or
services,
but
helping
them
get
active
and
involved
with
the
open
source
community
and
get
engaged
with
meeting
each
other.
A
A
B
Hello,
I'm
john
motor,
I'm
a
percentage
principal
engineer
at
zendesk.
I
work
in
the
what's
called
a
foundation
organization
or
a
foundation
engineering
organization.
In
zendesk
we
provide
compute
storage
infrastructure,
all
the
technologies
and
tools
needed
for
the
rest
of
the
zendesk
engineering
org
to
deploy
and
run
their
update.
Their
applications
been
working
with
kubernetes
and
cloud
native
technologies
for
probably
about
five
years
now,
right
around
the
time
that
kubernetes
first
came
out
and
I've
been
part
of
the
cncf
end
user
group.
C
C
D
A
So
this
is
the
second
time
that
we've
run
the
cncf
end-user
technology
radar,
where
we
survey
the
different
companies
in
the
end
user
community
and
ask
them
to
report
what
solutions
they're
using
and
whether
they
would
recommend
them
to
other
people
effectively.
So
there's
three
levels
that
we
have
one
is
adopt,
meaning
we
clearly
recommend
it.
We've
used
it
for
a
long
time.
A
It's
stable,
secondly,
is
trial,
which
is
we've
used
it
with
some
success,
so
we
can
recommend
it,
but
maybe
it's
only
applicable
for
certain
use
cases,
or
we
only
use
it
in
certain
ways
and
then
the
third,
the
third
level
is
called
assess,
so
assesses
we've
tried
it
out.
We
think
it's
promising
and
we
recommend
that
you
take
a
look
at
it
and
then
each
technology
radar
is
accompanied
by
some
themes,
which
is
anything
that
the
radar
team
thought
was
interesting
or
unexpected
or
noteworthy
about
what
they
saw.
A
A
D
I
think
it's
really
difficult
to
run
any
business
or
an
organization
without
knowing
how
it's
doing
and
having
an
insight
into
the
processes.
The
products
and
engagement
with
users,
for
example,
is
essential
to
be
successful,
and,
given
that
we
are
all
running
systems
and
providing
features
and
products
to
users,
it's
important
for
us
to
provide
them
in
a
reliable
way
and
understand
how
how
we're
doing
and,
as
you
know,
the
landscape,
including
cncf
products,
sort
of
exploded.
D
So
you
know,
starting
with
metrics
logs
traces,
there's
many
things
to
to
analyze,
to
collect
and
to
measure,
and
there
is
also
many
different
things
and
capsules
in
the
observability
from
tools
to
different
protocols
to
different
processes
to
different
ways
of
collecting
a
lot
of
those
things.
So
I
feel
like
that.
This
is
something
that
everyone
could
benefit
from,
knowing
how
other
people
are
doing,
and
we
chose
to
talk
about
it
and
give
it
a
try.
A
B
Well,
yeah,
just
just
for
everyone
watching
the
this
started.
Cheryl
invited
us
to
be
a
part
of
this
radar
team
and
it's
not.
She
like
she
or
anyone.
She
has
to
came
with
us
with
a
topic
or
a
gender
or
anything
like
that.
We
just
started
with
a
conversation
about
like
all
right.
What
would
be
both
interesting
to
us
and
would
we
think,
would
be
interesting
to
the
larger
community.
B
We've
announced
ideas
back
and
forth
to
try
a
few
things
and
observability
seemed
like
something
like
like
marston
said
it
is
both
universal.
Like
you
can't
you,
a
company
can't
run
and
be
successful
without
in
some
way
observing
the
state
of
their
servers,
their
users
and
that
sort
of
thing
it's
something
that
we
all
need
to
struggle
with
and
there's
also
been
a
lot
of
change
in
development
like
when
I
first
started
zendesk
about
five
years
ago.
C
Yeah,
I
think,
similar
to
what
marcin
and
john
have
mentioned.
You
know,
from
my
perspective,
there's
been
an
and
kind
of
rapid
increase
in
the
cloud
native
space,
a
lot
of
adoption
of
the
new
tools,
the
new
technologies
and
the
new
kind
of
paradigm
in
which
developers
are
writing
code
and
operating
their
code
in
and
and
a
lot
of
them
are
choosing
like
a
micro
services
based
architecture.
That's
kind
of
what's
become
the
norm
and
in
this
kind
of
you
know
massively
distributed
system.
Observability
is
really
really
important.
C
E
Yeah,
I
think
the
topic
of
observability
made
a
lot
of
sense.
It
seemed
both
timely.
I
think
this
is
something
that
is
top
of
mind.
A
lot
of
organizations
seemed
like
there
were
a
lot
of
projects
open
source
and
enclosed
projects,
standards,
vendors,
like
software
as
a
service
type
solutions,
so
it
seemed
like
we
really
could.
A
A
A
These
are
the
companies
sizes
by
the
number
of
employees,
the
total
number
of
employees.
So
you
can
see
that
there's,
probably
maybe
50
50,
maybe
slight
bias
towards
the
1000
employee
plus
so
mid
to
large
size
companies,
and
then
the
companies
that
were
represented
were
across
a
range
of
different
different
industries.
B
Yeah
and
just
just
one
note
about
the
as
we
start
to
get
into
the
some
of
the
actual
numbers
here
so
yeah,
we
canvassed
32
companies.
We
said
effectively
like
a
survey
or
a
spreadsheet,
so
we
did
not
have
like
in-depth
scientific
interviews
with
like
super
deep
analysis
here
so,
and
there
were
a
larger
number
of
technologies
that
we
kind
of
the
people
that
companies
brought
up
then
are
represented
here.
So
you
might
be
asking
yourself
hey
why
you
know
why
isn't
so?
B
E
A
Yeah,
thank
you
for
adding
that
john.
You
make
a
really
good
point
that
this
is
not
supposed
to
be
a
hundred
percent
objective.
I
don't
even
know
if
you
can
be
100
objective
on
this,
but
hopefully
you
can
see
at
least
from
this
roughly
what
kinds
of
companies
and
the
size
of
companies
are
represented,
and
this
tech
radar
is
really
intended
to
be
a
guide
for
people
as
well.
It's
not
supposed
to
say
there
is
what
exactly
one
technology
stack.
That's
going
to
be
perfect
for
you.
A
B
Well,
the
first
thing
I
want
to
point
out
is
that
the
observer
might
notice
that
open
telemetry
is
effectively
a
format,
whereas
thanos
and
kiali
are
like
prod
or
you
know,
products,
software
systems
that
you
install
and
use.
So
it
points
out
another
interesting
bit
about
the
observability
space.
There
was
like
you
know:
there
are
sas
providers,
they're
open
source
software.
You
can
run
locally.
B
There
are
like
data
formats
and
methodologies,
and
we
we
decided
to
kind
of
make
it
a
fairly
broad,
like
you
know,
asking
companies
like
what
are
you
using
and
leaving
it
a
fairly
broad
question
just
to
see
what
people
came
up
with,
so
you
might
notice,
there's
some
interesting
like
okay,
how
do
you
compare
open
telemetry
to
thanos?
That's
a
bizarre
thing,
but
again
this
is
more
just
like
what
people
are
interested
in
using.
So
first
of
all,
these
are
all
like.
A
A
C
E
Yeah
and
I
think
that
we're
seeing
tracing
as
a
little
bit
of
a
newer
thing,
and
so
you
you
can
really
see
that
here
with
opentilmer
telemetry
jager
light
step
things
like
that
being
a
little
bit
more
towards
the
bottom,
as
organizations
are
starting
to.
You
know,
experiment
with
these
tools
and
really
get
on
board
with
them.
E
D
D
I
think
you,
if
you're
running
on
aws
cloudwatch,
is
something
that
you
have
to
be
familiar
with,
that
at
least
familiar
with
and
many
many
organizations
already
sort
of
assess
what
it
can
do
and
what
what
it's
capable
of
and
cloud
login
and
statsd
are
things
that
have
been
available
for
a
long
long
time.
So
people
are
also
familiar
with
both
of
them.
B
D
So
when
we
look
at
those
they,
those
are
the
tools.
I
think
that
solve
the
actually
solve
problems
for
people
in
reasonably
good
ways
and
compared
to
the
the
products
and
things
that
we
have
in
assess
and
try
out
trying
an
asset
seems
to
me
like
things
that
people
have
are
looking
for
solutions
for
in
many
cases
and
are
trying
out
those
tools
to
check
if
they
can
actually
solve
those
problems
for
them
and
are
hoping
to
get
those
solutions
to
solve
those
problems
for
them
and
with
adopt.
A
Well,
awesome.
Thank
you.
I
think
my
audio
is
back,
so
I
appreciate
the
summary
that
you
just
gave.
I
think
that's
a
really
good
summary
and
there
were
a
lot
more
solutions
that
people
gave
answers
for
that
were
not
listed
here.
So
I
think
we
had
more
than
30
in
total
and
I
think
it
was
we
had
to
sort
of
choose
how
many
we
could
actually
fit
onto
one
radar
just
to
not
be
overwhelming
so
jason,
I'm
gonna
ask
you:
how
did
you
find
creating
the
radar?
E
D
B
For
me,
like
I
wanted
more
like
there
were
several
companies
like
zendesk
included
that
had
like
said
that
were
adopting
like
multiple,
arguably
competing
products
or
things
you
know
with
prometheus
and
datadog
and
splunk,
all
kind
of,
as
as
tools
we've
used-
and
I
was
wondering
like
okay
are,
you
know,
is
that
is
one
of
them:
a
legacy
that
you're
moving
on
to
the
new
one?
Are
you?
Is
it
different
teams
like
these
groups
in
the
organization?
It
has
different
use
cases.
D
So
we
did
pocs
with
open,
open
source
tools,
but
at
the
end
we
decided
to
go
with
a
sas
provider
and
I
was
very
curious
about
what
other
organizations
do
and
how
they
do
it
and
what
kind
of
tools
they
adopting
for
all
those
different
use.
Cases
and
data
points
that
we
now
have
to
sort
of
keep
track
of,
and
the
number
of
tools
was
sort
of
surprising
to
me,
like
the
how
many
tools
there
are
that
I
was
not
aware
of
of
some
of
them.
D
D
A
A
All
right
so
now
I'm
going
to
talk
about
the
themes.
So
what
things
did
you
find
interesting
or
noteworthy
about
what
you
saw
and
the
first
one
that
the
radar
team
came
up
with
was
the
most
commonly
adopted?
Tools
are
open
source,
and
I
thought,
when
I
saw
this
like
well
duh
right
of
course.
Of
course
this
is
open
source
because
everything
is
open
source
in
this
world
right,
so
john,
maybe
you
can
comment
on.
Why
was
this
interesting.
B
Oh
yeah,
I
think
I
mean
like
yeah
like,
like
you,
said
it's
kind
of
unsurprising
in
so
far
as
the
like.
The
end
user
group
are
a
set
of
people
who
are
you
know
most
almost
all
of
us
are
running
kubernetes,
either
managed
or
open.
So
we've
all
kind
of
bought
into
the
idea
of
open
source
community
supported.
B
You
know
cloud
provider
supported
technologies,
so
it
kind
of
makes
sense
to
use
other
ones
as
well,
but
at
least
in
our
experience
at
zendesk
at
least
the
once
you
get
to
a
certain
scale
of
like
of
like
data
and
company.
It
takes
a
lot
of
effort
and
time
to
actually
run
a
lot
of
these
open
source
tools
at
scale
and
it's
easy
to
spin
up
in
a
weekend
following
a
blog
post
that
sort
of
thing.
B
So
it's
interesting
to
see
that
even
at
fairly
large
scales,
a
lot
of
these
companies
are
investing
the
time
and
energy
to
run
their
own
prometheus
clusters
or
rafana,
or
you
know
and
like
manage
the
complexity
there
and
in
many
cases,
rather
than
like
okay
using
a
sas
provider
or
paying
someone
to
handle
that
stuff.
For
it
now
to
be
fair,
some
of
some
of
the
companies
like
datadog
and
splunk
are,
you
know,
were
in
the
upper
upper
range
of
commonly
used.
D
Or
maybe
those
organizations
didn't
get
yet
to
that
point
where
they
had
an
opportunity
to
evaluate
what
they
actually
want
to
do,
and
they
just
go
with
a
flaw
like
they
started
with.
I
don't
know,
promise
and
grafana
and
as
they
are
growing,
they
expand
and
the
deployment
of
those
tools,
and
that
was
very,
very
surprising.
C
A
B
Right,
I
mean
robust,
we,
we
are
a
sas
company,
so
we're
all
like
yeah.
You
know
sas
is
a
good
idea.
Everyone
should
do
that,
but
yeah
we
like
for
a
while,
like
logs,
for
example,
for
a
while.
We
had
we're
running
for
you
know
way
back
in
the
day,
we
just
had
a
log
server
that
people
used
grep
for
and
then
eventually
moved
to
a
system
where
we
were
like
pushing
logs
to
kafka
and
coffee
goes
to
an
elastic
search
cluster.
B
But
then
we
realized
that
that
that
elasticsearch
cluster
was
getting
bigger
and
bigger
and
we
realized
okay.
We
either
need
to
hire
several
engineers
just
to
keep
that
up
and
running
and
tuned
and
scaled,
and
that
sort
of
thing
like
that
and
that's
expensive
or
we
should
you
know
we
should
try
to
find
a
sas
provider
to
handle
logs
for
us,
and
we
looked
at
the
you
know
the
cost
of
hiring
people
the
opportunity
cost.
B
The
fact
that
you
know
we
just
figure
a
good
sas
provider
would
probably
do
a
better
job
of
managing
logs
than
you
know.
Three
and
three
or
four
or
five
engineers
would
do
on
our
team,
so
we
decided
okay,
let's
like
take
that
money
and
give
it
to
a
provider.
So
we
use
datadog
in
this
case,
but
that
was
you
know,
there's
a
fair
bit
of
like
back
and
forth
and
trying
to
determine
the
like.
Okay
do!
Do
it
in-house
versus,
like
admit,
that's,
not
our
core
competency
and
have
someone
else.
A
E
C
Yeah
this
one
was
actually
very
interesting
from
my
perspective.
You
know
what
we
noticed
was
that
a
large
number
of
companies
had
actually
given
opinions
on
a
large
number
of
the
tools
that
have
been
mentioned
here,
which
means
that
they
have
actually
tried
and
have
experience
with
at
least
many
of
these
tools.
C
You
know
five
or
more
tools
that
have
been
mentioned
here,
which
is
a
lot
of
tools
for
for
observability,
and
so,
as
the
you
know,
as
the
radar
team
was
kind
of
looking
at
the
data
and
trying
to
understand
that
one
of
the
things
we
realized
was
a
that.
You
know
the
the
cloud
native
space
is
a
very
thriving
community.
C
There's
a
lot
of
interesting
innovation,
that's
happening
here,
and
so
there's
a
lot
of
new
tools
that
are
coming
in
that
are
looking
to
solve
some
of
the
problems
as
people
build
more
cloud
native
systems,
and
so
as
these
new
tools
are
coming
in,
that
are
solving
problems.
People
are
looking
at
those
tools
to
try
and
understand
how
to
use
them.
So
I
think,
that's
kind
of
what
makes
a
lot
of
people
at
least
have
some
experience
with
with
these
tools
to
be
able
to
give
some
opinion
on
them.
C
D
D
C
D
B
We
kind
of
have
like
multiple
ways
of
of
monitoring
stuff
which
is
or
close
to,
but
have
their
own,
their
own
quarks.
So
what
are
the
like?
What's
the
relative
value?
What
is
most
useful
to
teams?
You
know
trying
to
monitor
system
to
know
up
their
errors
to
be
able
to
troubleshoot
things.
If
an
incident
does
occur,
look
at
historical
trends,
so
they
all
have
these
trade-offs.
B
So
we
end
up
like
running
a
couple
of
different
tools
in
different
both
for
different
use
cases,
and
I
think
we're
still
experimenting-
and
you
can
see
like
I
think,
slack
came
out
with
a
blog
post
the
other
day
about
their.
You
know
new
their
system.
What
is
it
yeah,
just
tracing
and
netflix
has
their
egg
drifting.
So
it's
it's
a
constantly
churning
domain
with
lots
of
really
interesting
technologies.
Kind
of
constantly
popping
up.
A
Oh,
and
I
see
we
have
a
comment
from-
I
guess
one
of
the
founders
maintainers
of
openmetrics.
You
said
the
assume.
I
think
this
was
after
martin,
like
that's
in
part,
why
I
started
openmetrics
to
force
metrics
into
a
label
system
and
then
let
the
best
one
win.
So
he
loves
to
see
that
it's
being
used
and
thought
about
as
such,
which
thanks
a
great
comment.
A
I
also
actually
found
this
second
one
interesting
compared
to
the
first
radar
that
I
did
where
it
was
fewer
projects
and
sort
of
more
easy
to
choose
where
it
was.
I
felt
like
this
one
had
a
lot
more
projects
and
it
was
much
less
clear
which
levels
they
should
fall
into
and
I
was
corrected
he's
the
founder,
so
I'm
sorry
founder
of
openmetrics,
okay,
third
theme,
prometheus
and
grafana
are
frequently
used
together.
A
D
D
Sometimes
there
is
a
mention
of
graphite,
but
those
two
are
like
as
a
bundle
that
come
together
and
even
if
you
look
at
things
like
hand,
charts
or
different
deployment
patterns
for
those
systems,
they
all
are
ban.
They
both
are
bundled,
and
it
may
be
that
they
just
essentially
work
very
well
with
each
other
and
provide
people
with
what
they
are
looking
for
and
because
it's
so
widely
adopted.
It
is
now
easy
to
deploy
and
maintain
them
as
a
bundle,
essentially.
E
No,
I
think
it
it
makes
a
ton
of
sense,
but
it
was
really
striking.
You
may
not
be
able
to
see
it
in
the
radar
on
the
way
that
the
data
is
presented
there,
but
in
looking
in
the
responses,
it
was
almost
100
percent
kind
of
overlap,
so
everyone
that
was
using
prometheus
was
also
using
grafana,
which
was
interesting.
I
don't
think
it
was
exactly
100,
but
it
was
close
enough.
It
was
very
close.
Yes,.
B
Oh,
I
just
want
to
point
out
that
as
soon
as
ns
we've
got
like
a
thousand
engineers,
we
have
a
foundation
team.
We've
got
a
vendor
review
board,
but
but
at
the
end
of
the
day,
usually
when
we
are,
you
know
trying
something
out
or
doing
proof
of
concept.
There
is
some
engine
like
one
engineer
who
is
like
googling
and
reading
blog
posts
to
figure
out
okay.
How
do
I
get
this
up
and
running
on
a
test
cluster
or
something
like
that?
B
So
the
you
know
the
the
process
that
goes
through
initially
is
like
the
exact
same
as
you
know,
hobbyist
or
a
three-person
startup,
or
something
like
that.
So
I
think
that,
like
all
right,
a
couple
of
tools
that
fit
together
nicely
and
make
it
really
easy
to
see
value
and
end,
you
know
it's
a
lot
easier
to
get
to
the
point
of
like.
Oh,
this
is
cool.
I'm
going
to
go.
Tell
my
boss
about
this,
and
you
know
I
had
to
work
up
the
chain.
B
A
C
Yeah,
so
so
we
got
into
the
observability
game.
You
know
right
around
the
time
when
we
were
starting
to
get
into
the
kubernetes
side
of
the
world
as
well
and
when
we,
when
we
started
on
that
journey,
prometheus
actually
wasn't
around,
and
so
we
made
our
bet
on
a
different
tool.
We
instrumented
all
of
our
systems,
and
today
we
have
millions
of
metrics
being
emitted,
and
we
have
you
know
over
400
engineers
in
the
company
who
completely
trained
on
the
tool
and
understand
how
to
use
it.
C
You
have
you
know,
hundreds
of
dashboards,
thousands
of
alerts
set
up,
and
so
at
this
point
in
our
journey
kind
of
to
the
theme
number
two
that
you
see
there.
It's
really
kind
of
a
lot
of
investment
for
us
to
move
from
our
existing
solution
over
to
something
like
prometheus
or
grafana,
and
and
that's
not
just
the
cost
for
the
the
redoing
all
the
work.
But
it's
also
like
keep
in
mind
to
train
400
engineers
on
the
new
tool.
C
There's
going
to
be
a
brief
period
in
time
where
we're
probably
going
to
live
in
two
worlds
in
the
existing
world
and
in
the
new
world,
and
like
me,
going
through
that
whole
transition
just
seems
like
a
lot
of
work,
and
we
just
don't
see
enough
roi
in
making
that
investment.
At
this
point
and
again
it's
not
contributing
towards
our
core
business.
It
doesn't
really
buy
us
anything
in
terms
of
where
our
business
wants
to
go.
So
that's
kind
of
what's
holding
us
back.
A
This
is
basically
what
the
the
final
radar
looks
like.
So
in
a
in
adopt,
we
have
these
five
projects:
prometheus
grafina,
elastic,
datadog,
openmetrics
in
trial.
We
have
six
here
and
then
three
in
assess
and
I'm
going
to
ask
each
of
our
panelists,
just
one
thought
or
takeaway
or
something
that
you
learned
from
going
through
this
exercise.
B
I
think
it
was.
It
was
nice
and
somewhat
validating
to
discover
like
so
many
so
many
companies
use
so
many
tools
and
are
still
like
it's
not
it's
not
just
us
that
has
three
or
four
different
observability
tools
running
at
the
same
time
and
we're
all
you
know,
sharing
thoughts
and
ideas
with
the
other
people
on
the
team,
like
oh
yeah.
B
This
is
something
that
we
all
struggle
with
and
I
think
the
value
of
talking
to
your
peers
and
getting
you
know
either
to
get
like
advice,
commiserate
or
just
like
have
someone
who
has
an
opinion
on
this
that
isn't
really
biased.
I'm
trying
to
sell
you
some
one
thing
one
way
or
the
other
is
really
valuable
to
me.
E
Yeah
this
was
a
really
really
fun
process
and
it
really
wouldn't
have
been
possible
without
you
know:
cheryl
and
julie's
coordination,
so
big
thanks,
mad
props
to
them
for
that,
but
I
think
my
thoughts
on
the
the
kind
of
the
radar
and
the
process
and
whatnot
is
that
yeah.
I
again
I
want
to
reiterate
that
there
were
a
lot
of.
There
are
a
lot
of
tools
and,
unfortunately,
because
of
kind
of
the
subset
of
data
that
we
have.
You
know
we
can't
make
judgments
about
tools
that
you.
B
E
That
subset
of
of
cncf
end
users
didn't
use
widely.
So
we
know
that
there
are
a
lot
of
good
tools
that
kind
of
didn't
make
the
cut
because
they
didn't
have
the
votes.
We
couldn't
make
a
judgment
about
them.
So
that's
not
a
reflection
of
the
quality
of
the
projects
that
aren't
or
the
tools
that
aren't
on
here.
A
D
So
I
really
really
enjoyed
the
process
and
the
collaboration
and
I'm
happy
to
that.
I
was
able
to
learn
how
other
organizations
do
things
and,
as
for
the
radar,
I
I
read
it
as
there
are
things
that
do
solve
problems
really
well,
there
are
things
that
solve
problems
well
for
some
people
and
there
are
tools
or
things
that
people
hope
to
be
solved
for
them
in
the
future
like
when,
I
think
about
open
telemetry,
it's
something
that
we
are
excited
about
and
waiting
for.
C
Yeah,
so
I
I
will
echo
what
the
other
panelists
have
said
and
thank
cheryl
and
julie,
and
the
entire
cncf
team
to
you
know,
help
shepherd
this
whole
thing.
I
think
this
is
a
very
valuable
effort.
I
also
want
to
thank
all
the
fellow
panelists.
I
think
it's
been
a
lot
of
fun
having
these
conversations
and
learning
what
everybody
thinks
about
what's
happening
and
and
kind
of
trying
to
come
up
with
some
way
to
wrangle
all
of
this
data
together
to
present
in
some
meaningful
way.
C
I'm
super
excited
actually
to
see
the
large
number
of
tools
and
the
various
kinds
of
problems
they
solve
here
and
how
end
users
are
using
this,
I
think,
for
me,
it
reflects
really
some
of
the
challenges
in
running
a
distributed
system
in
a
cloud-native
way.
I'm
super
happy
to
see
the
amount
of
interest
and
investment
that's
happening
in
the
industry.
That's
leading
to
you
know
newer,
newer
tools
coming
up
that
are
looking
to
solve
some
of
the
problems
that
you
know.
C
We
as
end
users
are
facing
in
trying
to
build
this
kind
of
an
architecture.
So,
for
me,
like
a
big
takeaway,
is
that
this
observably
landscape
is
pretty
large
lots
of
people
looking
to
solve
interesting
problems
here.
So
I
would
encourage
both
the
people
who
are
building
and
creating
these
tools
to
continue
all
the
hard
work
that
they're
doing
and
then
the
end
users
to
share
all
of
their
feedback
with
cncf.
A
A
A
A
A
Thank
you
so
much.
I
really
appreciate
everybody's
time
chatting
today
and
just
the
last
things
to
wrap
up.
I
want
to
say
thank
you
to
all
of
our
presenters
for
coming
and
joining
today,
and
the
webinar
recording
and
slide
will
be
posted
online
later
today.
We
look
forward
to
seeing
you
at
a
future
cncf
webinar.
Thank
you
and
have
a
great
day.