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From YouTube: 2022 State of Observability and Log Management
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A
Hi,
everyone
welcome
to
today's
webinar,
the
state
of
the
observability
and
log
management
in
2022.,
we're
currently
in
the
midst
of
a
perfect
storm
of
massive
data
growth
and
a
need
for
innovation
in
order
to
shed
light
on
key
trends,
observability
challenges
and
some
of
the
approaches
to
resolving
those
observability
challenges.
We
went
ahead
and
surveyed
over
315
it
professionals
across
a
variety
of
industries.
A
In
this
survey,
we
got
their
perspectives
on
the
current
state
of
exploding
data
and
their
struggle
to
gather
valuable
insights
from
that
data.
Our
goal
in
running
the
survey
was
one
to
understand:
what's
driving
the
massive
growth
of
observability
data
and
two,
what
approaches
will
help
teams
be
more
productive
today,
we're
excited
to
share
the
results
of
the
survey
and
some
of
the
data
points
that
help
us
answer.
Those
questions,
I'm
richard
gibson.
I
work
on
the
marketing
team
here
at
era.
A
Software
has
a
few
items
I'd
like
to
address
before
we
dive
in
first
we'll
be
sharing
the
full
presentation
after
the
webinar,
so
no
need
to
worry
about
taking
notes
or
screenshots.
Second,
there
will
be
some
attachments
you
could
open
and
download
during
our
presentation
and
finally,
if
there's
any
questions
that
come
to
mind
during
the
presentation
go
ahead
and
submit
them
and
we'll
save
some
time
at
the
end
to
answer
those
questions
before
we
get
into
the
webinar
I'd
like
to
introduce
you
to
our
two
speakers.
A
B
Thank
you
very
much
richard.
It
is
my
pleasure
to
share
a
very
exclusive
findings
from
our
first
ever
state
of
observability
and
log
management
reports
with
you.
As
richard
mentioned,
here's
some
of
our
goals.
The
primary
research
goal
was
to
capture
a
very
hard
data
on
trends,
observability
and
log
management
trends
and
understand,
what's
happening
in
the
market,
going
forward
in
2022
and
beyond.
B
We
ran
this
survey
in
february
2022
and,
as
richard
mentioned,
we
surveyed
more
than
300
professionals,
both
executives
and
individual
contributors,
and
all
professionals
had
responsibility
for
managing
availability
and
cloud
environments,
application
and
infrastructures.
B
We
divided
demographics
in
this
volume
of
data
and
industry,
we
gather
data
from
across
the
spectrum
and
industries
from
fintech
technology,
transportation,
energy,
etc,
and
also
we
targeted
customers,
enterprises
and
organizations
that
have
at
least
10
terabyte
of
log
data
to
manage,
and
when
it
comes
to
different
role
levels,
we
had
almost
a
third
of
distribution
of
I.t
executives,
a
third
of
devops
and
sre
practitioners,
and
also
cloud
and
application
and
enterprise.
Architects
consisted
for
about
34
regionally.
B
This
survey
focused
on
north
america,
with
about
75
percent
of
people
coming
from
united
states
in
canada,
some
in
europe
about
fifth
and
a
little
bit
in
asia
pacific.
So
there
are
three
big
groups
of
trends
that
emerged
when
we
ran
our
survey.
B
There
is
one
very
interesting
finding
that
we
have
captured,
and
this
also
that
log
data
is
still
very
heavily
used
for
understanding,
not
only
user
experience,
but
both
user
and
product
experience
and
that's
very,
very
interesting,
despite
some
of
them
like
over
the
years
investments
made
in
more
specialized
tooling,
such
as
application
performance
monitoring
and
one
of
the
reasons
that
could
be
the
case,
because
apm
tools
are
seen
as
very,
very
expensive,
so
people
still
resort
to
using
logs
to
understand
user
experience.
B
So
we
expected
that
I
t
has
variety
of
users
from
log
data,
but
we
were
also
pleasantly
surprised
to
see
that
insights
from
understanding
from
analyzing
log
data
is
seen
across
the
organizations
and
also
within
the
within
the
business
and
lives
of
business
stakeholders.
For
a
variety
of
use
cases,
one
is
understanding
customer
activities.
B
Compliance
reporting
is
big
and
also,
as
I
mentioned,
improving
product
and
user
experience.
One
anecdotal
finding
is
that
less
than
one
percent
also
commented
that
they
use
essays
to
predict
failures.
B
And
in
this
next
finding.
There
is
almost
unanimous
consensus
that
from
our
server
responded
that
all
log
data
is
important
for
itc
outcomes
as
well.
As
vast
majority
of
our
respondents
said.
Log
data
is
also
essential
for
business
outcomes
and
about
70
state
that
it's
critical
and
very
very
important
and
the
larger
the
organization,
the
that
that
has
more
log
data
to
manage
they're,
seeing
they're
seeing
I-team
the
they're,
seeing
that
it
outcomes
is
even
more
important
in
those
larger
organizations
with
with
lots
of
data
to
manage.
B
And
one
of
the
reason
is
that
when
an
organization
thinks
that
that
they
will
get
useful
information
for
critical
insights
from
log
data,
then
they
will
work
to
harness
that
information.
From
log
data-
and
they
have
volumes
of
log
data
so
still
very,
very
interesting.
Finding
and
there's
also
universal
agreement
across
all
that
overall,
in
the
coming
years,
we'll
see
that
data
that
log
data
volumes
will
just
keep
continuing
to
grow.
B
So
what
are
some
of
the
data
sources
that
are
driving
that
growth
within
top
three
data
sources
are
infrastructure,
security
and
cloud
services
logs,
and
that's
very
closely
followed
by
application
development
containers,
environments
and
as
a
standalone
sources,
we're
seeing
content
delivery
network
accounts
for
22
percent
of
growth
of
log
data
that
people
selected
as
a
major
source
such
as
cloudflare,
and
I
find
that's
really
really
interesting
so
tell
what
do
you
think
about
content
delivery
networks
as
a
major
source
for
log
data
harvesting.
C
Yeah,
I
think
one
of
the
things
that
we've
seen
is
that
you
know
I
think
you
highlighted
the
places
that
people
tend
to
use
log
log
management
heavily
for
infrastructure,
security
and
cloud
services.
Those
are
seen
as
certain
as
more
critical
logs
and
I
think,
as
we
look
at
part
of
the
the
volume
explosion,
it
seems
like
teams
have
to
make
decisions
about
which,
which
logs
are
the
most
valuable.
C
So
I
think
cdns
are
kind
of
lower
on
the
percentage
spectrum,
primarily
because
they
they
tend
to
be
the
one
of
the
first
ones
to
get
tossed
to
the
side.
So
I
think
we've
actually
seen
a
lot
of
interest.
You
know,
especially
with
services
like
cloudflare,
where
you
can
actually
get
use
their
log
push
service
to
receive
those
cdn
logs.
C
I
think,
as
we
as
we
see
more
affordable
and
more
efficient
ways
to
manage
logs
at
scale,
I
think
we're
starting
to
see
cdns
as
one
of
those
easy
wins
for
some
of
these
teams
to
be
able
to
get
back
insights
into
what's
coming
into
their
network,
and
I
think
in
particular,
as
we
start
to
look
at
the
rise
of
you
know
the
importance
of
cyber
security
we're
seeing
cdn
as
kind
of
a
of
a
leading
edge
for
being
able
to
assess,
assess
threats.
B
Thank
you
very
much
todd
now.
Moving
on
to
the
next
question
and
the
set
of
finding
is
that
I.t
executives
and
enterprise
architects
report
the
most
types
of
block
data
growth
when
compared
to
devops
series
and
operational
operations,
professional
and
one
thing
it
could
be-
that
because
of
their
position
in
the
organization,
but
also,
interestingly,
is
that
if
you
look
at
security
data
growth
here,
there
is
a
discrepancy
between
executives
and
the
rest
of
the
roles
we
polled
so
as
executives.
B
Consider
that
security
source
as
a
source
for
data
growth
is
going
to
be
for
78,
while
the
rest
is
kind
of
at
least
like
almost
10
10
percentages
lower,
and
it
could
be
that
it
executives
also
need
to
keep
a
close
track
of
that
data
growth.
So
they
can
manage,
and
then
security
is
seen
as
a
big
source
for
data
growth
for
them.
So
it's
interesting
finding
when
you
analyze
different
demonialis
differences
in
responses
between
different
roles.
B
Now,
when
it
comes
to
how
much
log
data
is
expected
to
grow,
there
is
not
a
consensus
whether
it's
going
to
be
25,
and
everybody
said
that,
but
also
there
is
something
that
is
going
to
be
two
to
five
times:
growth,
for
instance,
twenty
percent
of
people,
something
that
there's
fifty
percent
of
growth.
But
still
the
overall
trend
is
that
data
will
continue
to
grow
and
as
you
compound
that
that
information
across
several
five
years,
this
is
just
in
2022.
B
We
are
seeing
kind
of
skyrocketing
trend
in
overall
growth
of
this
data
source
data
type.
B
So
even
as
log
data
is
growing,
it
seems
that
not
everyone
is
happy
about
that
growth
and,
in
fact,
majorities
see
that,
while
it
is
very
necessary
for
global
data
to
grow,
they
have
a
very
mixed
feelings
about
this
growth,
because
log
data
is
seen
as
kind
of
massive
incoming
variety
of
former.
So
it's
not
easy
to
reap
the
the
or
glean
insights
for
massive
amount
of
data,
at
least
not
with
existing
methods.
B
So
before
I
dive
into
the
next
group
of
research
findings,
I
thought
what
do
you
think
about
this
kind
of
set
of
findings
that
they're
summarizing
the
in
the
first
part.
C
So
I
think
I
think
that
the
thing
that's
most
interesting
is
that
there's
there's
basically
a
you
know:
everybody
assumes
that
that
these
data
volumes
are
going
to
keep
growing
we're
seeing
come
from
a
lot
of
different
places,
and
I
think
the
really
the
only
question
is
you
know,
depending
on
each
each
individual
respondents
sort
of
perspective
like
do
they
think
it's
going
to
grow.
C
You
know
like
a
medium
amount
or
a
huge
amount,
and
so
I
think
you
know
really
we're
just
seeing,
I
think
we're
seeing
kind
of
the
the
result
of
a
lot
of
you
know
pushes
towards
moving
things
to
the
cloud
scaling
up
infrastructure.
I
mean,
I
think,
a
lot
of
this
even
started
with
kind
of
the
rise
of
containers.
C
You
know
five
to
seven
years
ago,
we're
just
seeing
a
lot
more,
a
lot
more
systems,
a
lot
more
micro
services
and
a
lot
more
infrastructure
and,
as
a
result,
you
know
monitoring
these
systems
keeping
them
healthy.
I
think
it's
just
it's
starting
to
show
us
that
the
data
volume
required
to
really
manage
these
systems.
Well,
it's
gonna
be
pretty
massive
and
I
think
you
know,
with
the
the
pie
chart
you
showed
about
people's
feelings
about
the
log
growth.
C
I
think
we're
starting
to
really
see
that
practitioners
are
struggling
to
figure
out
how
to
make
use
of
all
this
data
that
they're
generating.
So
I
think
it's
I
think
it's
going
to
be
a
continued
challenge,
as
these
volumes
just
continue
to
grow
year
over
year.
If,
if
the
tooling
doesn't
change.
B
Thank
you.
That
seems
like
a
perfect
segue
into
our
next
set
of
findings
and
then,
as
richard
highlighted,
it's
kind
of
a
perfect
storm
of
changes
in
the
application,
architectures
and
more
massive
adoption
of
clouds.
So
so,
let's
see
how
it
practitioners
and
it
executives
deal
with
all
that
data
growth,
so
76
of
survey
responses
found
that
enterprises
do
take
steps
to
minimize
the
overall
log
data
volume
and
log
data
growth
and
the
biggest
one
is
that
they
just
store
the
crit.
B
The
first
group
is
62
said
they
just
store
the
only
critical
data
and
some
of
them
decide
to
quickly
erase
data
within
24
hours,
and,
what's
really
amazing
to
me
to
is
to
see
that
almost
20
percent
18
of
iit
teams,
we
pulled
choose
to
disable
logging,
which
could
be
quite
dangerous
right.
You
know,
incidents
may
happen
right
at
a
time
and
you
you're
choosing
essentially
to
fight
blind.
B
Another
answer
which
we
found
is
an
undocumented
anecdotal,
but
frequent
response
is
that
enterprises
do
erase
at
some
point,
but
they
keep
log
data
for
a
longer
times,
as
the
log
data
is
needed,
for
compliance
for
audits
and
for
forensics
analysis
so
delete
log
data.
B
But
after
after
it's
useful,
then
is
still
you
know
needed
for
for
troubleshooting
and
all
these
analyses
that
they
just
mentioned,
and
it's
also
interesting-
that
ik
executives
are
far
less
likely
to
report
all
the
efforts
to
minimize
log
data
volumes
and
one
thing
it
could
be
either
like
those
efforts
really
don't
reach
them
or
it's
it's
something
that
they're
they're
not
reporting
on.
But
it's
something
that
essentially
one
needs
to
keep
tabs
on
because
of
all
the
proliferation
also
incurs
rising
costs.
B
So
and
then,
when
it
comes
to
those
costs,
almost
80
percent
states
that
they
are
trying
to
minimize
costs,
because
there's
various
methods
that
they're
trying
to
minimize
costs,
one
can
use
like
a
offline
storage
method
like
s3
or
one
approach.
Is
that
to
try
to
reduce
licensing
at
least
costs
for
commercial
vendors
is
to
use
open
source
tools,
and
some
of
the
danger
is
that,
like
there's
still
costs
associated,
but
they
just
may
not
be
tracked
in
a
more
traditional
way,
there's
still
infrastructure
costs
that
need
to
be
taken
into
advantage.
B
But
it's
interesting
to
see
that
there's
a
huge
group
of
people
about
40
percent
that
chooses
to
route
data
to
less
expensive
at
all.
So
there's
a
lot
of
effort,
invested
into
figuring
out
costs
and
not
just
simply
managing
volumes,
and
there
are
also
mixed
results
when
it
comes
to
the
success
of
these
efforts.
So
some
of
the
survey
responders
said
that
they
wish
they
had
data
they
erased,
or
it's
very
difficult
to
access
data
once
you
store
it
to
some
offline
method
like
a
cold
storage
and
only
12.
B
Think
that
all
these
efforts,
they're
they're,
making
to
reduce
the
the
volumes
and
costs
are
effective.
B
C
Yeah,
I
mean,
I
think,
the
the
use
of
of
s3
or
gcs,
just
you
know,
object.
Storage
in
general.
Is
it's
interesting?
I
mean
it.
I
think
it
kind
of
indicates
that
those
logs
have
some
value,
and
while
there
may
be
some
artificial
limits
for
storing
in
the
in
primary
log
management
systems,
whether
it's
you
know
a
cost
or
just
hardware
limits
they,
they
can't
store
it
all
in
the
systems
that
they
they
want
to.
They
don't
want
to
get
rid
of
them.
C
They
don't
want
to
lose
them
forever,
so
they
sort
of
put
them
in
a
holding
place.
You
know
it's,
it's
sort
of
like
basically
like
a
data
lake
like
a
low
quality
data
lake,
you
can
just
send
archives
archives
of
logs
there,
but
you
know
it
means
that
there's
probably
a
desire
for
those
logs
to
be
useful
at
some
point
in
the
future,
but
the
the
really
the
tr
the
trouble
comes
in
when
you're
trying
to
balance
the
growing
data
volumes.
C
We
were
talking
about
earlier
with
the
the
kind
of
fixed
costs
of
existing
systems
and
those
those
costs
generally
aren't
going
down.
But
the
data
volumes
are
going
up.
So
at
a
certain
point,
you
kind
of
hit
that
decision
point
where
you
have
to
figure
out
what
to
do,
and
I
think
again
it
just.
C
It
comes
back
to
kind
of
what
we
were
saying
at
the
beginning
of
this
section,
which
is
it
feels
like
there's
a
need
for
new
tools
that
can
kind
of
change
that
cost
value
paradigm
a
bit,
because
we're
we're
seeing
that
folks
are
trying
a
bunch
of
strategies
to
reduce
costs.
But
ultimately
it
feels
like
they're
they're
having
to
take
a
path
that
really
minimizes
the
value
that
they
can
get
from
logs
because
things
become
considerably
less
surgical
when
they're.
Just
you
know,
static
archive
left
on
object,
storage.
B
Yeah,
lots
of
lots
more
lots
of
fascinating
findings
here,
which
brings
us
to
the
next
next
set
of
questions.
Next
question
is
we
wanted
to
understand
what
are
some
of
the
challenges
in
dealing
with
log
data
and
our
survey?
Respondents
identify
all
those
challenges
and
they
chosen
what
are
some
of
the
steps
they
find
really
hard
to
do,
and
it
turns
out
that
preparing
filtering
and
cleaning
data
seen
as
sort
of
hardest
step,
which
is
also
storing
data,
followed
by
the
storing
data
in
the
cost,
efficient
way
and
also
another.
B
Another
response
that
we
captured
that
is
not
in
in
this
chart
is
that
people
find
the
event
correlation,
not
easy,
which
also
underlines
the
fact
that
you
just
cannot
dump
data
to
things
such
as
s3,
but
you
have
to
have
the
data
accessible
and
easy
to
consume
and
correlate
and
then
figure
out
insights
and
queries
that
we
need
to
run
on
those
on
those
data
and
then
moving
to
the
next
one.
B
97
percent
of
I.t
practitioners
report
that
existing
tools
have
challenges
in
particular
because
they're
not
built
to
handle
this
huge
massive
amount
of
log
data,
and
there
are
variety
of
things
that
those
challenges
are
the
first
one.
Is
it
just
takes
too
much
time
to
analyze
data
that
are
coming
from
a
variety
of
different
tools
like
traditional
tool?
B
Proliferation
is
still
not
sold,
then
you
know
you
see
the
the
the
resources
that
they're
dedicated
to
manage
all
these
tools,
the
larger
organization,
the
more
resources
they
need
to
manage
these
tools,
and
then
some
of
the
also
very
answers
that
are
captured
is
that
different
departments
have
different
needs
and
want
different
tools.
So
when
that's
the
case,
how
do
you
see
the
complete
picture
across
all
the
departments
and
there's
no
there's
lack
of
logging
standards,
which
also
makes
ingestion
pretty
hard,
which
goes
in
alignment
with
the
point?
B
What
we
just
made
earlier,
that
preparing
filtering
and
making
data
easy
for
injection
is,
is
not
nothing
easy
and
also
another
one
is
there's
no
central
place
to
capture
all
the
all
those
data.
So,
there's
lots
of
challenges
also
scaling
a
price
with
the
scaling
of
data
volumes
is
also
seen
as
another
challenge,
and
this
is
where
kind
of
scalability
of
tools
comes
into
pictures,
so
that
we
we
ask
teams
to
evaluate
the
risks
they
face.
B
When
you,
you
grow
your
data
volumes,
but
your
tools
may
not
scale
with
the
amount
of
data.
That's
coming
as
you
as
todd
mentioned
earlier,
from
this
kind
of
adoption
of
different
application.
Architectures,
like
all
different,
like
sources
of
data
that
you
need
to
handle
that
you
didn't
think
about
which
also
can
have
impacts
such
as
one
obvious
one
is
troubleshooting,
but
a
troubleshooting
and
kind
of
long
time.
B
It
takes,
for
instance,
to
resolve
because
you
need
to
deal
with
all
these
data
sources,
so
troubleshooting
takes
longer,
but
also
one
interesting
response
that
we
got
is
that
insecurity
risks
are
larger
and
people
complain
that
they
can
expose
and
accidentally
log
per
pii
data
as
well
as
credentials
which,
which
kind
of
brings
again
into
the
mix
that,
especially
for
security,
scaling
all
the
data
and
making
sure
the
tool
supports.
The
massive
data
growth
is
essential
because
your
security
risks
are
going
to
be
higher.
B
It's
also
interesting
that
executives,
it
executives,
are
more
aware
than
you
would
potentially
expect
of
resolution
times
and
loss
of
revenue
impact
when
compared
to
the
operations
or
I.t
practitioners,
and
it's
very
always
interesting
to
see
what
is
those
discrepancy
between
different
roles,
and
you
know
it's
in
the
second
one
incidents
take
a
longer
time
to
resolve.
That
seems
to
be
a
top
of
mind
for
executives,
because
that
ultimately
impacts
customers
and
then
I'll
turn
to
todd
to
share
our
fir.
Our
third
group
of
findings
here.
C
So
I
think
really
just
looking
at
the,
I
think,
basically
what
we're
seeing
here
and
I'm
trying
to
think
about
how
to
recap
everything
we've
already
said
is
that
you
know
we
see
that
the
amount
of
data
that
people
are
handling
that
are
gonna
have
to
manage
going
forward
is
gonna
grow.
The
costs
are
rising
and
we're
seeing
a
lot
of
the
the
tooling
choices
that
I
think
folks
have
made
are
directly
impacting
how
much
they're
spending
just
managing
these
log
volumes,
and
so
essentially,
what
I
think
we're
we're
proposing.
C
C
In
order
for
these
log
volumes
to
be
able
to
be
handled
with
the
you
know,
we
at
one
end
we
were
talking
about
10
to
50
percent,
year-over-year
growth
and
as
high
as
you
know,
5x
year-over-year
growth,
and
so
I
think,
in
order
for
companies
to
be
able
to
really
get
value
out
of
the
logs
that
they're
creating
the
logs
that
we've,
you
know
kind
of
already
articulated
they.
We
know
there's
value
in
them.
C
The
the
tooling
really
needs
to
evolve
to
get
to
a
point
where
not
only
are
the
costs
manageable,
but
are
the
insights
that
people
want
to
get
out
of
those
logs
manageable
to
make
it?
You
know
worth
worth
collecting
and
keeping
that
data
so
that
you
know
the
entire
business
can
get
used
out
of
it
and
I
think
stella
one
thing
you
were.
You
were
saying
on
the
the
slide
about
the
the
awareness
you
know
it's,
it's
not.
C
It's
not
too
surprising
to
me
that
you
see
the
the
I.t
executives
are
the
ones
that
are
like
most
concerned
about
revenue,
loss
of
revenue,
they're
most
concerned
about
mttr,
and
whether
it's
you
know
reality
that
it
takes
a
long
time
to
them.
I
think
when
they're
looking
at
business
impacts,
it
feels
like
a
long
time,
and
so
I
think,
the
more
the
harder
it
gets
as
the
data
volumes
grow
to
get
insights,
the
more
value
there's
gonna
be
further
and
further
up
the
business
to
have
tooling.
That
really
gets
you
to.
C
C
And
then
I
think
the
the
other
thing
that
is
is
really
interesting
is
that
you
know
observability
has
been
around,
I
mean
as
a
term,
let's
say
less
than
a
decade,
maybe
seven
seven
years
or
so.
I
think
the
thing
that
we're
seeing
is
that
there's
still
a
lot
of
folks
that
are
early
in
their
in
their
observability
journey.
So
I
think,
looking
at
this
slide,
you
know
only
11
feel
like
they
have
a
mature,
observability
implementation.
C
I
think
you
know
it
really
just
says
that
there
are
a
lot
of
tools
out
there,
but
there
are
still
a
lot
of
people
who
are
evaluating
and
adopting
tools,
but
really
figuring
out
how
that
works
into
a
full,
full-blown
organization-wide
strategy.
And
so
I
think,
if
you
look
at
the
folks
that,
are
you
know
to
the
right
on
this
graph
that
have
either
never
really
embraced,
observability
or
heard
of
it
or
you
know,
just
haven't,
picked
any
tooling.
C
Yet,
there's
still
a
pretty
big
subset
of
folks
out
there
who
are
early
in
that
journey,
I
think,
are
really
trying
to
figure
out
what
this
means
for
their
business
at
large,
and
so
I
think
what
we'll
see
is
as
these
as
these
folks
continue
to
move
to
the
left.
C
You
know
there's
going
to
be
a
lot
more
opportunities
to
bring
new
companies
into
the
observability
space,
but
I
think
also
we're
going
to
just
continue
to
see
those
folks
who
are
new
to
the
journey
be
surprised
by
the
amount
of
data
that
they're
generating,
whether
it's
you
know
logs
metrics
or
traces,
and
so
I
think
it's
you
know.
While
it
feels
like
a
relatively
widespread
concept.
I
think
this
data
is
telling
us
that
there's
still
a
lot
a
lot
yet
to
to
go
before
we
reach
maturity
in
the
observability
space.
B
And
I
did
similar
survey
last
year
and
it's
very
interesting,
even
though
it's
still
early
that
compared
to
2021,
we
see
the
overall
growth
of
observability
data.
Adoption
is
really
staggering.
180
percent,
so
it's
it's
growing,
but
we
still
have
room
to
grow
our
observability
and
todd
moving
on
to
a
little
bit
kind
of.
If
you
can
walk
us
through
some
of
the
next
findings
on
the
kind
of
the
types
of
data
and
what
are
the
different
varieties
and
et
cetera.
C
Yeah
definitely-
and
I
think
the
you
know
as
we
as
we
talk
about
observability-
we
think
about
kind
of
there
are
three,
you
know
traditional
pillars,
logs,
metrics
and
and
traces,
and
so
you
know
logs
have
probably
been
around
for
the
longest
or
the
most
ubiquitous
like
they
were.
C
They
were
kind
of
the
original
form
of
getting
data
out
of
out
of
these
systems,
and
so
it's
kind
of
not
surprising
that
logs
are
the
most
data,
but
also
you
know,
as
data
volumes
continue
to
grow,
logging
is
really
the
easiest
easiest
to
add
easiest
for
application
developers,
and
I
think
the
other
thing
to
keep
in
mind
is
that
the
way
that
most
of
these
metrics
are
generated
logs
are
generally
generated.
C
You
know
per
request,
you
know,
like
the
the
more
users
interact
with
systems,
the
more
logs
are
generated,
you
know
in
in
direct
correlation,
and
there
are
logs
from
other
things
as
well,
but
I
think
metrics
generally
tend
to
have
a
relatively
stable
output
cave,
and
so,
if
you
look
at,
you
know
kind
of
prometheus
standard.
It's
you
know.
Data
gets
reported
once
every
10
seconds
or
every
30
seconds,
depending
on
how
you
have
it
configured,
so
those
generally
are
decoupled
from
you
know
growing
user
volumes.
C
C
We
can
keep
adding
more
tags
and
more
fields
and
more
descriptive,
modifiers,
and
I
think
logs
see
that
a
lot
more
than
I
think
the
other
other
data
formats
you
know
and
then
coming
back
to
the
data
variety
you
know
we
mentioned
earlier,
like
it
can
be
cloud
cloud
native
deployments.
It
can
be
kubernetes
infrastructure,
it
can
be
cdn,
it
can
be
application
logs.
C
There
are
all
these
different
places
where
logs
come
from
and
I
think
sort
of
by
almost
by
design
you
know
logs
have
that
flexibility
to
really
be
able
to
just
be
inserted
anywhere.
Metrics
continue
to
be
relatively
well
structured
and
the
places
that
metrics
are
exposed
or
are
also
well
structured.
So
I
think
we're
gonna,
we'll
just
see
you
know
logs,
come
from
a
wide
variety
of
sources
contain
a
wide
variety
of
of
information
within
them,
and
you
know
continue
to
be.
C
You
know
for
better
or
worse,
less
less
structured
than
the
other
other
types
of
metrics
and
then,
when
it
comes
down
to
cost,
you
know,
I
think
these
other
two.
You
know
drivers
really
lead
to
cost
having
more
data
having
more
variety
you
know.
C
Obviously,
the
higher
data
volumes
is
just
expensive
because
it's
more
data,
but
I
think
logs,
because
they
are
so
they
have
so
much
variety
they're
harder
to
index
on
they're,
harder
to
scale
they're
harder
to
get
value
out
of
the
queries
end
up
being
more
complex,
and
so
I
think
kind
of
all
all
three
of
these.
These
pieces
here
really
feed
in
together.
To
just
say
you
know,
logs
are
a
place
where
I
think
companies
are
going
to
continue
to
invest
heavily.
C
B
That's
that's
something
that,
on
this
graph
also,
is
that
you
know
the
costs
related
to
managing
tracing
is
also
very
interesting.
Just
by
the
sheer
amount
of
traces
it
could
be
billions
of
traces
recorded.
So
it
seems
also
that
sampling
needs
to
further
grow
to
get.
B
C
Yeah
yeah,
I
think
we,
you
know
we
talked
a
little
bit
about
some
of
these,
the
offline,
cold
storage
options,
and
I,
I
think
those
make
sense.
You
know
I
mean,
in
terms
of
you,
know,
cost
per
unit
of
storage.
It's
it's
hard
to
get
much
cheaper
than
than
s3.
C
You
know
for
the
especially
for
the
durability
that
you
get,
but
I
think
what
we're
seeing
here
is
that,
as
these
data
volumes
grow
that
we're
talking
about
you
know,
we
see
companies
that
have
been
traditionally
heavy
users
of
tools
like
splunk,
start
to
look
for
more
affordable
options.
For
for
some
of
these
bigger
data
volumes,
potentially
data,
that's
less
mission
critical,
and
so
we
see
the
adoption
of
open
source
tools.
C
I
think
elasticsearch
is
very
common
in
this
space
and
we
see
the
you
know,
kind
of
that
stratification
of
costs
and
we
see
it
sort
of
get
pushed
down
to
lower
tiers.
But
the
thing
that
I
think
companies
are
starting
to
realize
also
is
that,
even
as
as
those
tools
are
embraced
and
start
to
grow
now,
someone
has
to
maintain
those,
and
someone
has
to
manage
a
new
piece
of
infrastructure
which
has
its
own
cost.
C
It
has
its
own
operational
overhead,
so
there's
sort
of
the
you
know:
cloud-based
blog
management
products
that
are
that
are
in
the
mix
and
there
obviously
are
a
bunch
of
folks
out
there,
and
I
think
what
we're
seeing
also
is
that
those
costs
have
continued
to
grow
because,
as
as
these,
you
know
largely
sas
players.
I
think
in
the
log
management
space.
C
Data
volumes
grow,
you
know
that
cost
gets
passed
on
to
customers,
and
so
I
think
whether
it's
you
know
using
traditional
log
management
products
or
kind
of
doing
in
a
diy
manner
and
hosting
your
own
elastic,
search
cluster
or
pushing
those
costs
to
the
cloud.
I
think
what
we're
seeing
is
that
it's
becoming
a
big
expense
and
I
think,
with
the
data
volumes
that
I
think
a
lot
of
companies
are
starting
to
approach
now,
it's
becoming,
I
think,
very
difficult
to
to
fit
into
the
traditional.
C
C
That's
coming
is
finding
a
way
to
straddle
some
of
those
traditional
costs,
whether
it's
you
know
on-prem
or
in
the
cloud
versus
the
the
the
low
cost
of
object,
storage
and
finding
a
middle
ground
where
you
can
leverage
some
of
those
cost
savings,
but
still
get
the
you
know
the
queryability
and
data
insights
that
you
want
from
that
log
data,
while
while
finding
ways
to
over
time
kind
of
bring
those
costs
down.
C
I
think
this
this
slide
also
is
interesting,
just
kind
of
looking
at
how
many
how
many
companies
actually
have
a
team
that
can
manage
some
of
these
these
tools,
and
so
you
know
30
37
say
that
they
don't
have
any
dedicated
resources,
so
it
almost
is
a
given
that
they're
going
to
have
to
use
a
cloud
service
or
something
that
that's
managed
by
another
vendor.
C
But
20
percent
have
a
dedicated
team
like
a
full
team
to
manage
their
log
tooling,
and
so
you
know
whether
it's
that
19
or
the
other
44
that
say
they
have
some
some
individuals
that
are
responsible.
You
know
there
there's
a
significant
investment
there,
because,
generally
these
are
going
to
be.
You
know
technical
employees
they're
going
to
be.
You
know
relatively
advanced
with
the
the
ability
to
manage
these
tools.
C
You
know
there's
a
significant
cost
there
that
goes
above
and
beyond
any
software
licenses
or
any
hardware
that
you're
having
to
pay
for
to
manage
these
platforms,
and
my
guess
is
that
if
we
took
data
took
the
same
data
from
a
year
ago,
these
numbers
would
have
expanded
that
we
would
see
more
dedicated
teams
more
dedicated
individuals
spending
their
time
on
this,
and
I
think
really
just
you
know
whether
it's
logs
metrics
or
traces,
I
think
a
lot.
C
It's
the
values
is
increasing
to
companies,
but
also
so
is
the
investment
in
keeping
these
systems
running,
and
you
know
the
I
don't
know
unsurprising
part
of
it
is
the
more
these
are
used,
the
more
they
become
part
of
the
critical
path
for
uptime
incident
resolution,
and
so
it's
it's
not
acceptable
for
those
systems
to
to
be
down
or
to
fail.
So
having
those
folks
in
charge
of
keeping
those
systems
up
and
running.
C
You
know
it
becomes
a
critical
business
function
and
I
think
this
is
just
showing
us
that
the
bigger
the
company,
the
more
data,
the
more
likely
they
are
to
have
a
dedicated
team.
You
know
or
a
dedicated
set
of
individuals
that
are
managing
this,
and
this
comes
back
to
I
think
kind
of
we
were
saying
about.
You
know
business,
outages
and
loss
of
revenue.
I
think,
as
these
systems
become
more
more
critical,
more
complex
and
more
a
focus
of
you
know
how
really
how
outages
affect
the
business.
C
You
know
we
see
more
and
more
resources
from
a
human
capital
perspective
and
also
from
you
know,
just
infrastructure
and
hardware
expenditure
on
on
managing
this
data
and
getting
value
out
of
it.
So
yeah.
I
think,
with
all
the
things
that
kind
of
led
up
to
this.
This
slide
isn't
super
surprising
to
me.
It
probably
isn't
to
most
people.
C
And
then
I
think
one
other
thing
that
we've
been
starting
to
see
a
little
bit,
and
I
guess
this
feeds
feeds
a
bit
into
sort
of
the
overall
narrative
here
about
growth
of
observability
data,
how
this
gets
managed
in
larger
companies
by
looking
at
you
know,
stream,
processing
or
or
streaming
observability
data
we're
starting
to
see
some
of
the
early
phases
of
this,
and
I
think
this
this
is
kind
of
lagging
the
overall
observability
market
a
bit,
because
it's
it's
becoming
more
important,
more
valuable.
C
I
think,
as
these
data
volumes
have
been
growing,
and
so
I
think,
probably
going
back
a
bit
a
lot
of
the
a
lot
of
existing
observability
pipeline
work
is
probably
built
around
something
like
kafka
or
rabbitmq,
or
some
of
the
other
kind
of
existing
open
source.
Just
general
general
purpose
message
cues,
but
I
think
what
we're
starting
to
see
is
that
other
solutions
are
emerging
that
give
you
the
ability
to
have
something.
That's
more
specifically
focused
on
observability
data
and
primarily
recognizing
that
that
data
is
it's
time,
series
data.
C
C
I
think
a
lot
of
that
data
becomes
very
specific
in
the
way
that
it
needs
to
be
handled,
and
so
I
think
this
is
really
showing
you
kind
of
can
see
almost
like
two
two
cohorts
in
here,
and
the
first
is
folks
that
have
some
sort
of
of
a
tool
or
in
the
process
of
picking
a
tool,
and
my
guess
is
that
most
of
the
folks
that
say
they
have
a
fully
deployed
solution,
are
probably
running
kafka
or
something
like
it.
Under
the
hood.
C
C
So
there
there's
sort
of
becoming
this
set
of
problems
that
I
think
observability
pipelines
are
made
to
handle
that
are
becoming
more
important
to
to
companies,
and
I
think
a
lot
of
this
kind
of
the
subtext,
I
think,
is
that
a
lot
of
this
comes
back
to
the
things
we
talked
about
previously,
which
are
managing
the
complexity
of
data,
managing
the
cost
of
the
data
figuring
out
how
to
optimize
your
you
know:
infrastructure
resources
to
get
the
most
value
out
of
this
data,
and
so
I
think
it's
it's
still
very
early,
but
I
think
observability
pipelines
are
going
to
be
a
thing
that
make
more
and
more
appearances
in
the
overall
observability
space
and
start
to
become
that
connective
tissue
between
all
these
different
tools
that
I
think
people
are
using
and
then
again
this
this
kind
of
breaks
down.
C
C
You
know
all
the
different
tools,
all
the
different
observability
tools.
They
may
be
using
all
the
different
data
sources
where
the
data
is
coming
from
and
how
they
think
about.
You
know
across
a
large
organization.
How
do
they
manage
that
data?
How
are
they
thinking
about
the
growth
of
that
data
and
what
they're
going
to
need
to
have
in
place
to
make
that
something?
That's
scalable
down
the
road.
C
Everybody
thinks
there's
value
in
innovating
for
in
observability,
and
you
know
whether
it's
in
the
the
what
the
tools
are
capable
of
or
the
type
of
data
that
can
be
supported.
C
C
I
think
it's
becoming
clear
that
there's
no
there's
no
perfect
solution
out
there,
there's
no
there's
really
no
single
tool
that,
I
think,
does
everything
that
teams
want,
and
so
I
think
really
what
we'll
continue
to
see
over.
I
think
the
next
next
few
years
is
that
we'll
see
probably
more
folks
move
towards
and
stella.
I
know
this
is
your
one
of
your
favorite
terms.
C
The
single
pane
of
glass,
like
I
think
there,
there's
a
desire
for
more
teams
to
be
able
to
see
more
sort
of
cohesive
views
into
this
data,
and
it's
not
it's
not
a
super
easy
thing
to
do,
because
I
think
we're
you
know,
especially
when
we're
talking
about
data
in
you
know
the
petabyte
scale.
There's
a
lot
there
there's
a
lot
going
on
a
lot
of
different
sources
on
a
lot
of
different
stakeholders,
but
I
think
we'll
you
know
we'll
continue
to
see
more
and
more.
C
I
think
interesting
innovations
in
not
only
just
how
we
store
this
data
at
scale,
but
also
how
we
just
how
we
get
value
out
of
those
tools
and
how
we
start
to
pass
that
value
further
further
up
the
chain
within
these
organizations,
so
that
more
more
people
higher
up
can
can
get
value
out
of
the
the
insights
that
they're
gathering
from
observability
data
and
then
yeah.
C
I
think
this
goes
back
to
another
slide
earlier
that
you
had
stella,
but
I
think,
having
having
tools
that
I
think
do
a
good
job
of
supporting
this
observability
data
growth
gets
people
more
excited
about
what's
possible.
I
think
a
lot
of
what's
happening
for
the
teams
that
are
not
interested
is
it
becomes
so
much
about
managing
the
infrastructure,
managing
the
costs
and
the
pain
associated.
C
It
becomes
almost
this
constant,
constant
stress
that
you're
trying
to
manage
these
things
that
you
that
are
growing
out
of
control,
and
you
can't
actually
get
to
a
point
where
you
can
sit
back
and
enjoy
having
having
really
good
tools
in
front
of
you,
and
so
I
think
really.
This
is
just
showing
us
the
more
mature
the
observability
infrastructure
is
at
an
organization
the
the
more
exciting.
I
think
the
insights
are
the
more
value
they
get
from
the
data
and
the
more
the
more.
C
I
think
the
organization
feels
like
more
data
is
better
and
I
think
that's
that's
what
we've
we've
all
wanted
to
believe,
but
I
think
we've
been
all
we've
been
hindered
a
bit
by
by
tooling
in
the
observability
space
and
yeah.
I
think
this
is
just
some
some
hope,
hopefully,
for
those
of
you
out
there
that
have
been
struggling,
that
modernizing
your
observability
infrastructure
can
can
be.
A
good
thing
can
be
achieved.
B
Now,
moving
on
to
more
of
a
summary
of
our
discussions,
we,
you
know,
there's
some
there's
lots
of
excitement
out
there
and
to
underline,
though
still
innovation
is
urgently
needed.
How
do
you
see
these
percentages?
It's
very
high
percentages
that
are
reporting
some
of
the
kind
of
big
statements
here.
C
Yeah
definitely,
I
think
I
think
this.
This
fits
into
a
bunch
of
the
things
we're
already
saying,
but
you
know
you
know
just
sticking
your
log
data
in
s3.
You
know
you've
achieved
the
goal
of
storing
it,
but
you
haven't
achieved
the
goal
of
getting
any
business
value
out
of
it,
and
so
I
think
really
it's
it's.
You
know.
Maybe
the
last
slide
was
the
perfect
one
to
kind
of
fit
into
this.
C
So
you
know
100
of
people
think
that
the
yeah
sorry,
the
previous
yeah
100
of
people,
think
that
there's
you
know
value
in
innovating.
A
C
C
We
want
to
be
able
to
make
the
tools
actually
solve
the
problem,
rather
than
just
becoming
a
pain
point
for
for
engineering
teams,
and
you
know
sometimes
you
just
take
the
problem
and
you
just
push
it
on
the
you
know,
kind
of
those
dedicated
teams
we
were
talking
about
before
and
then
yeah,
and
I
think
you
know
innovation,
I
think,
is
going
to
be
key
here,
moving
away
from
some
traditional
traditional
tools
and
I'll
I'll,
throw
I'll
throw
elasticsearch
out
here.
You
know
it's
it's
been
around
for
quite
a
while.
C
It's
it's
a
relatively
mature
tool.
It
gets
used
a
lot
in
log
management
use
cases,
but
the
the
kind
of
my
my
summary
of
it
is
that
it's
it
wasn't
built
for
that.
It
happens
to
be
a
good
fit
for
some
of
those
use
cases,
but
ultimately
it
tends
to
become
more
and
more
painful
and
more
and
more
expensive.
C
As
the
data
volumes
grow,
and
so
I
think
we're
seeing
a
lot
of
that
right
now,
starting
to
play
out
in
the
market
and
so
yeah,
it's
it's
comforting
to
me
to
see
that
everybody
seems
to
agree
that
there's
some
innovation
needed
and
that
I
think
there
there
is
a
possibility
to,
I
think,
do
better
with
observability
through
a
lot
of
the.
I
think
the
new
products
that
are
starting
to
come
to
market.
B
Now
that
we
covered
quite
a
lot
of
research,
we
gleaned
some
new
insights,
some
expected
some
unexpected
thought.
Could
you
share
with
us
a
little
bit
about
what
are
some
of
the
recommendations
we
can
have
for
people
out
there
in
the
space.
C
Yeah,
so
I
think
some
of
these
are
going
to
be
relatively
straightforward,
but
I
think
you
know,
as
you
start
to
look
at
your
at
your
options
at
your
at
your
existing
choices
for
observability.
C
There
are
you
know,
products
that
are
becoming
available,
that
are,
you
know,
leveraging
cloud
native
architectures
that
are
you
know,
sitting
on
top
of
things
like
s3
or
gcs
that
are
are
finding
a
better
way
to
sort
of
dynamically
stratify
these
these
log
layers
that
I
think
people
are
starting
are
trying
to
figure
out
themselves.
So
I
think
really,
you
know,
keep
an
eye
out
for
for
tools.
I
think
help
with
that
cost
management.
C
I
think
we'll
start
to
see
a
lot
more
of
those
out
there
in
the
market
and
I
think
right
now,
a
lot
of
the
tools
that
folks
are
using,
I
would
say,
are
relatively,
I
guess,
they're
sitting
on
the
kind
of
a
previous
generation
of
of
technology,
and
so
I
think,
a
lot
of
the
costs
that
are
out
there
in
the
market.
Right
now
are
artificially
high.
C
You
know,
I
think,
and
along
with
that,
I
think,
there's
there's
a
desire
to
keep,
especially
with
logs,
not
so
much
with
metrics
a
desire
to
keep
data
around
for
longer
periods
of
time.
So
I
think
you
know,
as
we
see
desires
grow
to
have
you
know,
audit
and
compliance
ready
storage
for
logging,
there's
also
the
ability
to
be
able
to
go
back
and
run.
Potentially
you
know
machine
learning
models
on
historical
log
data
to
get
customer
insights.
C
You
know,
I
think,
starting
to
factor
those
those
storage
terms
into
your
plans
and
then
figure
out
how
you
can
how
you
can
leverage
your
tools
to
be
able
to
to
make
that
storage
work
in
a
cost.
Effective
way
is
going
to
be
important,
finding
ways
to
expose
some
of
this
observability
data
to
other
parts
of
your
organization.
I
think
largely
it's
been
mostly
restricted
to
devops
and
sre,
but
I
think,
as
we
start
to
see,
you
know
more
of
these
mature
type
of
observability
infrastructures.
C
C
You
know,
I
think,
paired
up
with
the
the
low
cost
and
and
long-term
storage
just
finding
finding
tools
that
give
you
the
power
to
get
extract
data
from
your
from
your
historical
archives,
whether
it's
being
able
to
have
it
online
and
queryable,
or
it's
you
know,
being
able
to
have
a
set
of
of
tools
that
can
quickly
look
over
some
of
that
cold
data
without
having
to
you
know,
delegate
individual
engineers
to
go,
pull
that
data
down
and
and
and
look
through
it.
C
I
think
there
are
a
bunch
of
different
ways
to
go
about
about
getting
there
and
then
I
think
kind
of
on
to
the
innovations
in
observability
pipelines
and
observability
data
management.
Looking
for
tools
that
can
help
you
with
some
pre-processing
some.
You
know
overall
reduction
of
data
volumes.
C
I
think
I
think
that's
going
to
be
something,
as
I
said,
that
is,
is
growing
a
lot
and
gets
a
lot
of
innovation
going
forward
and
we'll
start
to
see
that
appear
in
a
lot
more
organizations
and
then
yeah
just
integrating
with
with
other
existing
tools,
and
so
I
think
a
lot
of
this
is
as
you
look
at
potentially
observability
pipelines.
You
know
you
don't
necessarily
have
to
replace
your
tools.
C
C
You
can
find
tools
that
can
be
operated
alongside
them
and
you
can
use
observability
pipelines
and
things
like
that
to
figure
out
which
data
goes
into
which
tool
and
how
do
you
kind
of
shuffle
that
that
data
in
an
automated
way
and
make
it
possible
to
you
know?
Essentially
let
the
tools
rebalance
that
data
for
you,
so
that
you
can,
you
know
more
effectively
optimize
the
costs
of
the
tools
that
you're
choosing
to
put
data
into
and
let
the
the
use
cases
or
the
needs
from
that
data
drive.
C
You
know
which
which
tool
and
which
cost
profile
is
associated
with
that
data.
So
I
think
kind
of
you
know
all
these
things
together,
I
think,
are
things
that
we'll
start
to
see
more
and
more
as
these
log
volumes
continue
to
grow.
But
the
upside
is
that
there
are.
There
are
ways
and
tools
and
strategies,
I
think,
to
get
to
a
a
manageable
profile,
and
I
think
again,
going
back
to
one
of
the
earlier
slides,
the
more
mature
organizations
that
have
gotten
to
a
place
where
they
figured
these
things
out.
C
B
Thank
you
very
much
todd
now
moving
into
the
the
q,
a
portion
of
our
presentation,
we
covered
quite
a
lot
of
insights
and,
if
you'd
like
to
get
the
full
report,
here's
the
link
you
can
download
it
for
free
and
read
the
details
and
keep
it
for
you
and
kind
of
see
if
you,
if
you
find
this
summaries
of
this
report,
something
that
you've
seen
in
real
in
practice
so
over
to
richard
to
see.
If
there
are
any
questions
on
the
line.
A
C
Yeah
happy
happy
to
answer
that
one
yeah,
so
I
mean
I
think
we
we
talked
about
it
in
a
bunch
of
different
ways,
but
I
I
think
the
biggest
piece
of
it
is
that
you
know
observability
is
here
to
stay.
I
think
we're
just
going
to
continue
to
see
data
volumes
grow,
I
think,
jumping
into
a
parallel
space.
You
know,
iot
data
is
another.
It's
another.
You
know
time
series
use
case,
that's
similar
to
observability.
C
You
know
that
that's
also
been
continuing
to
grow
and
largely
fueled
by
the
number
of
devices
that
are
out
there.
The
number
of
data
points
that
you
want
to
be
able
to
get
insight
on,
and
I
think
what
we're
really
seeing
is
is
a
lot
of
these
parallel
evolutions,
where
you
know
the
compute
resources
are
becoming
more
ubiquitous,
the
number
of
whether
it's
microservices
or
containers
or
individual
processes
that
are
being
monitored.
C
You
know
we're
seeing
that
hardware
become
hardware
and
sort
of
the
the
low-level
software
becoming
commoditized
to
a
point
where
it's
just
become
a
utility,
and
I
think
the
the
growth
of
observability
data
is
really
kind
of
just
the
tail
end
of
that
of
that
growth,
and
now
everyone
expects
there
to
be
logs
and
metrics
and
charts
and
graphs
and
insights
into
all
of
this
stuff,
and
I
think
that's
just
going
to
continue
to
grow.
C
Probably
not
you
know,
I
think
we
had
some
of
the
initial
some
of
the
respondents
that
you
know
thought
that
their
organizations
might
see.
You
know
5x
growth
or
more,
but
I
think
my
guess
is
that
a
lot
of
those
a
lot
of
those
companies
are
seeing
that
growth,
because
they're
in
the
middle
of
their
you
know
kind
of
cloud
migration
journey
or
embracing
tools
like
kubernetes
and
they're,
starting
to
see
that.
C
But
I
think
the
the
industry
as
a
whole,
I
think,
we'll
see
pretty
healthy,
year-over-year
growth
for
quite
a
while
yeah,
and
I
think
just
going
back
to
the
things
we
said
before
it's
going
to
come
down
to
tooling
choices
to,
I
think,
really
get
value
as
that
stuff
grows
yeah.
B
A
B
Yes,
I
spent
last
10
years
in
you
know:
monitoring,
observability
log
management
space.
Some
of
the
some
of
the
responses
are
quite
expected,
such
as
you
know,
troubleshooting
times
like
longer
or
proliferation
of
tools,
but
some
of
the
findings,
such
as,
if
you
don't
scale
with
the
your
tool,
doesn't
scale
with
the
growth
of
data
that
you
will
be
exposing
yourself
to
pii
risks
or
accidentally
exposing
credentials
and
some
of
the
kind
of
the
the
the
more
urgent
need
we
have
in
security
space
to
to
take
care
to
take.
B
You
know
harness
all.
The
data
is
very,
very
interesting
to
see,
especially
as
the
amount
of
data
volume
grows,
that
kind
of
keeping
tabs
on
incidents
and
potential
risk
and
security
is
becoming
more
urgent
than
ever
in
this
space.
So
that's
something
that
was
very
surprising
for
me,
because
this
survey
specifically
targeted
rt
organizations
and
those
survey.
The
security
realm
is
kind
of
shining
through,
even
for
for,
even
though
we
didn't
pull
directly
security
professional.
So
that's
very,
very
interesting
finding
for
me.
C
Yeah
great
question
and
we
didn't-
we
didn't
talk
too
much
about
the
specific
product
offerings
but
yeah,
I
think
our
our
era
search
product
is,
is
definitely
deployable
on
prem
or
or
in
a
you
know,
a
self-managed
cloud.
I
think
it's
it's
been
it's
something
I
think
is
somewhat
unique
to
log
management
versus
metrics
and
traces,
because
I
think
to
stella's
point.
C
Just
a
minute
ago,
logs
tend
to
have
a
lot
of
risk
for
having
pii,
and
so
I
think,
for
a
lot
of
our
customers
being
able
to
take
our
product
and
run
it
in
their
own
infrastructure.
Whether
it's
cloud
or
actually
like
physical
hardware,
is
a
relatively
big
selling
point,
and
I
think
there
are
a
lot
of
other
sas
vendors
that
you
know
they're
sas
only,
and
so
it
becomes
very
difficult
to
actually
have
full
control
over
that
infrastructure.
C
So
I
think
given
given
the
potential,
for
you
know,
security
breaches
and
things
like
that,
I
think
being
able
to
go.
On-Prem
for
for
log
management
is
actually
really
important.
I
think
is
going
to
be
a
kind
of
a
key
part
of
our
business
going
forward,
and
I
think
it
also
just
gives
it
gives
those
customers
the
ability
to
run
the
software
they
want
and
control
how
they
manage
costs
and
whether
they
want
to
you
know,
move
to
you
know
a
different
hosting
provider
or
negotiate
discounts
on
hardware.
C
A
C
Yeah
awesome-
I
I
get
pretty
excited
about
this
because
I
see
that
we've
had
you
know
there.
There
are
lots
of
logs
coming
from
lots
of
different
places.
I
think
going
to
some
of
the
the
charts
we
highlighted
earlier,
and
I
think
you
know
we've
seen
a
bit
of
my
new
favorite
term
agent,
sprawl
kind
of
in
recent
years,
and
it
becomes
very
hard
to
get
the
the
right
logs
from
the
right
places
in
the
right
format.
C
Deal
with
back
pressure
deal
with
failures,
and
you
know,
especially
as
we've
got.
You
know
really
infrastructure
running
all
these
different
clouds.
All
these
different
places-
you
know
downtime
happens
all
the
time,
and
so
I
think,
finding
a
way
to
remove
the
reliance
on
on
agents
to
be
responsible,
for
you
know
really:
data
data,
cleansing
data,
formatting
data
transformation
and
pushing
that
to
a
more
centralized
place.
C
You
know
whether
it's
you
know
just
a
very
generic
message:
queue,
something
like
that
or
more
you
know
a
specific
type
of
observability
pipeline.
I
think
we're
going
to
see
that
that's
going
to
become
more
common
and
it's
going
to
fit
better
with
the
the
tooling
and
trends
that
I
think
modern
development
teams
are
starting
to
embrace
whether
it's
you
know
get
up
style,
workflows
being
able
to
push
configuration,
changes
more.
C
You
know
more
dynamically,
whereas
I
think
rolling
out
configuration
changes
to
thousands
of
agents
across
your
entire
infrastructure
is
actually
pretty
pretty
difficult
to
do
pretty
difficult
to
coordinate,
and
I
think
that
that
evolution
of
the
observability
pipelines
and
the
broader
kind
of
observability
data
management
concept,
I
think
we're
going
to
see,
become
something
that
more
more
organizations
and
more
teams
are
going
to
become
reliant
on,
and
it's
actually
going
to
start
to
be
a
pretty
big
part
of
how
those
orgs
manage
these
huge
data
volumes
across
lots
of
different
tools
and
have
the
have
the
confidence
that
things
are
working,
the
way
that
they
want
and
that
they
can
continue
to
make
dynamic
changes.
C
As
you
know,
as
new
products
ship
as
the
company
changes,
as
as
teams
grow
and
as
volume
scale,
so
yeah,
I
think
we'll
start
to
see.
I
think
it's
it's
still
really
early
in
that
space.
I
think
we'll
start
to
see
a
lot
more
of
that
over
the
next
year
or
two
for
sure.
A
Yep,
thank
you.
Thank
you.
Todd
vicky,
stella
thanks.
Everyone
for
participating
in
today's
session
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For
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