►
From YouTube: Mar 19 2021: DataHub Community Meeting (Full)
Description
Full version of the DataHub Community Meeting on Mar 19th 2021
Welcome - 00:00
Project Updates by Shirshanka - 01:49
- 0.7.0 Release
- Project Roadmap
Demo Time: Themes and Tags in the React App! by Gabe Lyons - 09:36
Use-Case: DataHub at Wolt by Fredrik and Matti-Pekka - 19:28
Poll Time: Observability Mocks! - 37:08
General Q&A from sign up sheet, slack, and participants - 51:12
A
A
A
A
A
I
think
data
hub
has
always
been
an
awesome
project,
but
we
never
really
put
it
together
to
explain
to
people
what
it's
all
about.
So
I'm
really
happy
with
how
this
turned
out.
We've
also
done
some
interesting
work
in
creating
a
live
demo
environment
so
every
morning
and
dexter
did
a
lot
of
this
work.
So
thanks
for
that
texture,
we
have
essentially
a
job
that
picks
up
the
latest
bits
from
master
from
the
main
branch
on
github,
deploys
it
out
to
a
demo
environment.
A
A
A
A
A
The
big
things
are
going
to
be
no
code.
Metadata
model
changes,
it's
going
to
be
a
big
hard
project
and
we're
going
to
spend
a
fair
bit
of
time
working
with
the
community
and
getting
it
out
and
there's
a
bunch
of
features
that
I
won't
get
into
in
detail
here
and
big
news.
We
have
a
new
release.
Finally,
it's
been
a
long
time
coming
about
three
months.
Since
we
had
released
a
new
release,
it's
0.7.0
there
are
a
ton
of
new
commits
in
it
about
200.
A
A
I
was
looking
at
the
number
of
contributors,
and
that
was
quite
nice
to
see
that
we
have
24
unique
contributors
to
the
project
for
this
time
period,
so
that
that
gives
us
a
lot
of
happiness,
because
it
means
that
the
project
is
actually
getting
the
kind
of
community
of
not
just
adopters
and
users,
but
also
people
contributing
changes
back.
So
this
is
great
and
we
want
to
see
more
of
that
all
right.
So
what
was
in
the
release?
A
A
A
Another
big
thing
was
the
metadata
injection
framework.
We
were
always
amazing
at
getting
data
in
through
kafka,
but
a
lot
of
people
were
not
quite
sure
how
to
get
all
this
metadata
into
kafka
in
the
first
place.
So
we
built
a
metadata
injection
framework
in
python.
It's
it's
actually
one
of
the
most
beautiful
python
code.
I've
seen
so
please
go
check
it
out.
I
love
what
our
shell
has
done
with
it,
and
a
lot
of
folks
are
giving
contributions
back.
So
this
is
amazing.
We
already
have
quite
a
few
sources.
A
Athena
and
druid
was
contributed
very
recently,
and
I
know
that
dbt
is
cooking,
so
I'm
super
excited
about
that.
One
product
features
the
big
enterprisy
one
was
sso.
A
lot
of
people
have
been
asking
for
this
john
put
in
a
ton
of
effort
and
got
the
sso
integration
done
on
the
react
app.
He
tested
it
out
with
its
oidc,
and
unfortunately,
john
is
not
here
today,
but
we're
gonna
do
a
deeper
dive
on
this
at
a
future
date.
A
A
The
linkedin
team
has
actually
been
moving
on
that
and
john
has
contributed
one
of
the
scripts
that
has
helped
them
migrate
from
elastic
five
to
seven.
So
hopefully
you
don't
need
to
do
these
big
migrations,
but
it's
better
to
get
them
done.
Early
before
we
start
getting
into
a
lot
of
more
exciting
features
so
get
on
with
it
and
hopefully
we'll
see
you
all
on
0.7
soon
awesome.
A
C
C
Can't
everyone
see
this
go
so
both
these
features
we
know
have
people
have
been
requesting
from
the
community
community
for
a
long
time
and
I'm
really
excited
to
have
finished
them
up
for
tags.
I
want
to
give
a
special
shout
out
to
the
frederick
and
monty
from
walt
for
helping
clarify
the
spec
for
this
the
needs
and
and
drive
that
rfc.
It's
really
awesome
collaborating
with
both
of
you.
C
So
if
we
go
over
to
my
data
hub
here,
I'm
at
a
airport
traffic
data
set-
and
you
can
see
that
I've
already
applied
a
tag
to
this
entity
and
this
side
could
have
been
applied
either
by
the
ingestion
pipeline
or
from
the
ui,
and
this
is
showing
a
direction
that
we
want
to
think.
Actually,
let
me
see
if
we
there's
some
type
thing
out
here.
C
I
don't
know
if
we
can
need
that
anyway,
so
this
tag
can
be
applied
from
the
ingestion
pipeline
or
from
the
ui,
and
this
is
showing
the
direction
that
we
want
to
take
data
hub
moving
forward,
which
is
that
we
want
to
start
data
hub
is
going
to
start
becoming
more
of
a
interactive
surface
as
well
as
a
read-only
surface.
So
if
you
look
at
the
mce
example,
json
file
in
the
metadata
ingestion
directory
you'll
see
some
example:
mce
events
that
are
ingesting
tags,
but
a
much
easier
and
more
intuitive
way.
C
It
can
be
just
applying
them
through
the
ui.
So
you
can
see
here's
this
tag
that
we've
applied
legacy
to
the
airport
traffic.
So
we're
saying
you
know
this
is
a
legacy.
Data
set
but
say
I
wanted
to
assign
say
that
this
data
set
also
needed
an
owner
assigned
to
it.
I
could
go
into
this.
Add
tag,
flow
and
type
in
owner
or
ownership,
and
this
is
going
to
search
our
repository
of
tags
and
in
the
type
head.
Any
tag
related
to
ownership
would
actually
come
up
right
here.
C
It's
serious
talking
to
me
since
no
tag
exists,
we
can
go
ahead
and
create
that.
So
we
can
say:
okay,
this
guy
is
going
to
need
ownership.
I
seem
to
be
having
trouble
hearing
we'll,
create
this
tag
and
say
give
it
a
description
and
when
we
create
this
tag,
it's
going
to
be
generated
as
its
own
entity,
so
that
if
we
wanted
to
reference
this
tag
from
other
entities,
we'd
be
able
to
say
this
element
needs
ownership.
C
Once
I
created
that
it
appears
on
the
data
set
and
if
I
click
on
it,
we
get
brought
to
the
tags
page.
We
can
see
who
created
it
again,
read
that
description
and
then
see
statistics
of
how
many
other
entities
this
tag
has
been
applied
to
and
when
I
click
on
this,
it
actually
brings
us
to
a
search
filtering
for
data.
Sets
that
have
this
tag,
so
you
can
see
that
it's
already
been
indexed
and
we
can
already
start
searching
about
it
and
we
can
use
tags
to
filter.
C
And,
as
I
said,
you
can
also
apply
tags
at
the
schema
level.
So
if
we
go
into
our
schema
and
to
enter
the
tags
column,
a
little
add
tag.
Button
will
pop
up.
We
add
that
we
get
practice
same
flow,
so
if
we
want
to
add,
say
that
this
field
needs
a
better
documentation,
I
can
add
this
needs
documentation,
tag
which
had
already
been
created
on
another
data
set,
it's
discoverable
here
we
click
on
that
and
add
it
and
there
the
tag
has
been
applied
to
the
schema.
C
C
So
the
second
feature
that
I
was
going
to
demo
is
themes.
We
know
that
this
has
also
been
something
that
the
community
has
been
requesting
for
a
while,
and
this
lets
you
customize
your
data
hub
instance,
so
that
it
can
feel
have
a
little
more
look
and
feel
that
perhaps
you
prefer
or
you
could
customize
it
to
look
a
little
more
like
your
internal
organization's
themes,
so
things
that
we
allow
you
to
customize
our
styling
like
background
color
line,
color
font
color,
the
font
things
like
that.
C
You
can
also
customize
assets,
so
you
can
insert
a
personal
logo
and
you
can
customize
things
like
a
welcome
message
as
well
and
we're
going
to
start
expanding
what
other
things
are
customizable
over
time.
Saxo
bank
is
currently
contributing
a
change
that
will
let
you
customize
menu
items
using
the
same
configuration.
C
I
think
dark
and
light
it's
a
little,
predictable
and
easy,
but
just
to
show
you
the
flexibility
that
themes
allow.
I
went
ahead
and
created
a
theme
of
my
own,
so
I
used
to
work
at
airbnb
and
there
I
made
contributions
to
airbnb's
internal
version
of
datahub,
which
we
call
data
portal
and
I
wanted
to
see.
Okay,
can
I
create
a
datahub
theme
that
would
fit
in
at
airbnb
and
again
just
using
the
same
theme
config
that
created
the
dark
theme,
the
light
theme
and
that
the
instructions
were
linked
here?
C
C
If
I
search
for
an
element
like
airport
traffic,
you
can
see,
the
banner
also
is
able
to
be
styled
and
labels
get
their
own
highlights
as
well.
So
this,
I
hope,
hopefully
gives
you
a
sense
of
the
flexibility
that
theming
allows
have
fun,
go
check.
It
out,
try
to
theme
your
own
and
again
look
forward
to
chatting
about
this
in
the
slack.
A
Awesome
that
looked
amazing
cool,
so
a
couple
of
things
the
tags
are
also
up
on
the
demo
site.
Please
go
there
and
add
some
tags
be
gentle.
We
have
not
added
bad
word
filters
or
other
kind
of
things.
We.
This
is
a
friendly
community.
Every
morning
we
are
going
to
replace
the
tags
with
kind
of
the
fresh
like
the
default
set
of
tags,
so
you're
going
to
see
your
tags
get
wiped
away.
A
A
A
A
D
E
All
right,
thank
you,
oh
hello,
so
I
am
materaka.
I
met
some
of
you
in
the
last
last
town
hall
and
to
give
you
a
bit
of
context
in
the
in
the
data
pipeline,
we
are
running.
We
have
been
now
moving
most
of
our
most
of
our
like
basic
etl
workloads
now
over
to
kafka.
So
we
over
at
kafka
connectors
on
top
of
our
operational
databases.
E
Then
we
use
kafka
to
store
and
distribute
the
data,
and
then
we
have
this,
like
in
in-house
developed,
streaming
framework
kind
of
a
model
repository
ingesting
the
data
and
then
uploading
that
snowflake
and
also
alongside
that,
we
utilize
heavily
air
flow,
especially
for
interacting
with
external
apis
third-party
systems,
but
also
other
of
our
like
internal
and
more
batch
batch
based
batch
based
systems
and
snowflake
is
our
main
data
lake.
That's
a
warehouse
kind
of
kind
of
an
in
between
a
model
model
there
and
for
data
hubs.
E
Perspective
like
we
have
this
luxury
of
storing
only
quite
well
structured
data.
So
we
don't
really
have
a
data
lake
where
we
dump
everything,
but
we
do
some
sensible
pre-processing
for
the
data
before
we
land
it
in
snowflake.
So
it's
in
a
like
usable
and
state
with
our
other
data
sets
and
to
make
sense
of
all
of
this.
We
started
last
year
to
evaluate
different,
open
source
and
also
other
other
options
for
managing
managing
all
of
this
all
of
the
metadata
we
have
and
like
we've
seen
previously
in
these
presentations.
A
E
E
A
E
So
we
created
this
service
and
sdk
called
cellist
for
our
internal
use,
and
this
is
the
main
way
we
want
to
interact
with
data
hub
and
also
expose
this
whole
storing
and
interacting
part
of
the
with
the
metadata
service
to
our
like
internal
developer
users
like
engineers
who
want
to
publish
their
metadata
in
data
hub
and
the
main
main
reasons
or
main
main
benefits.
We
saw
from
this
approach
or
well
the
easier
integration
with
data
hub,
as
is
especially
when
we
started
working
on
this.
E
There
were
no
existing
framing
works
like
currently,
so
you
need
to
parse
the
kafka
messages
and
the
overall
schemas
by
hand.
So
this
helps
then
the
end
user
a
bit
and
also
allows
us
not
to
have
kafka
dependencies
on
each
of
the
project
that
wants
to
actually
use
this,
and
then
this
allows
us
to
reduce
the
complexity
of
the
metadata
models.
E
But
for
our
end
users,
for
actually
for
like
say
our
business
business
users,
the.
Of
course
the
ui
would
be
the
date
substitute
ui.
This
is
for
storing
the
data
and,
for
example,
fetching
downstream
lynch
information
for
for
some
some
entity
and
here's
a
slight
small
example
how
it
looks
like
in
practice.
So
we
have
a
simple
python
sdk
that
you
can
then
use
in,
for
example,
the
metamorphosis
project
mentioned.
E
We
simply
run
a
small
python,
scraper
script,
that
iterates
over
the
models
creates
the
required
payloads
and
then
outputs
that
into
the
service,
which,
for
example,
look
like
looks
like
here
that
we
have
this
simplified
data
set
format.
That
then
includes,
for
example,
the
schema
information
instead
of
having
them
as
different
entities.
That,
then,
might
require,
in
some
cases
some
intermediate
entities
for
linking
them
together.
E
A
D
Yes,
thank
you
yeah
just
generally,
as
stated
already,
oh
yeah,
sorry.
So
the
main
our
sort
of
vision,
vision
for
for
our
metadata
ingestion
is
basically
to
to
be
able
to
cover
the
whole
sort
of
data
realm
at
all.
So
starting
at
the
operational
databases
to
and
third
third-party
apis
to
the
to
the
warehouse,
snowflake
can
and
all
the
way
to
to
individual
machine
learning
models
and
and
looker
dashboards.
D
D
This
global
tags
feature
addition
to
data
hub
and
now
we're
exploring
how
to
utilize
those
most
efficiently
so
playing
with
the
idea
to
to
use
them
as
sensitivity
classes
on
on,
for
example,
schema
fields
and
or
on
higher
level
entities,
and,
as
you
saw
in
the
in
the
diagram
earlier,
we're
heavy
users
of
airflow
so
and
we
use
it
to
transform
and
and
and
move
data
around.
So
so
support
for
airflow
was
a
must
for
us.
So
that's
why
we
participated
in
the
addition
of
that
that
feature.
D
D
D
Quite
soon
is
sort
of
utilizing
the
downstream
lineage
and
sort
of
ownership
and
and
sort
of
stakeholder
relationships
for
alerting
sort
of
taking
downstream
actions.
I'm
thinking
about,
like
stopping
machine
learning
training,
runs
if,
if
we
notice
that
a
data
set
just
is
going
stale
or
things
like
this
all
right,
let's
go
to
the
last
slide.
I
think
we
have
a
maybe
a
few
minutes
so
yeah.
D
D
We're
currently
sort
of
ingesting
our
or
working
yeah
in
just
the
the
sort
of
warehouse
tables
and
now
now
we're
sort
of
extending
that
outwards
to
our
kafka
topics
and
operational
databases
and
sort
of
downstream
as
well
into
into
looker
dashboards
and
so
on
and
yeah.
Maybe
maybe
one
of
the
cooler
cooler
features
that
we
we
want
to
look
into.
B
D
A
A
Which
is
observability
and
frederick
and
mati
talked
about
it
a
little
bit
as
well
right.
One
of
the
next
steps,
after
setting
up
a
a
data,
discovery,
catalog
or
platform
or
whatever
you
want
to
call
it
tool,
is
that
yeah
people
can
find
things
and
they
can
look
at
some
lineage
and
figure
out
who
to
talk
to
and
which
data
set
is
related
to
which
dashboard.
A
But
then
often,
we've
seen
that
the
next
question
that
comes
up
is
being
able
to
really
trust
that
this
data
set
is
indeed
the
right
one.
For
you
to
depend
on
for
the
rest
of
your
life,
I
mean
the
rest
of
your.
You
know
next
project
and
the
question
is:
why
would
you
depend
on
this
data
set?
What
makes
this
data
set
so
amazing
that
you
actually
want
to
use
it,
and
that's
really
that
difference
between
a
data
set.
That
seems
like
the
most
likely
candidate
for
you
to
use
versus
a
data
set.
A
That
clearly
seems
like
a
trusted
one
and
we
feel
like-
and
we've
seen
this
at
multiple
iterations
of
this
journey
at
different
companies-
that
the
operational
signals
coming
from
the
actual
active
part
of
the
data
ecosystem
gives
you
kind
of
that
important
signal
that
allows
you
to
understand.
If
you
should
trust
this
data
set
or
not
so
that's
kind
of
something,
that's
also
in
our
roadmap.
A
If
you
see,
we've
got
integration
with
data
quality
tools
and
things
like
that
coming
up,
and
we
wanted
to
instead
of
kind
of
working
on
it
and
then
finally
releasing
it
and
then
realizing
that
the
community
wanted
something
slightly
different.
We
wanted
to
flip
it
around
and
do
a
few
designs
do
a
few
mocks
and
then
share
it
with
the
community
and
kind
of
get
some
feedback
from
you
early
on,
so
that
we
get
the
chance
to
actually
build
the
product
in
the
right
way
for
the
community.
A
A
The
hive
data
set
in
question
you're
able
to
see
kind
of
a
quick
summary
of
what
that
data
set
is
all
about
it's
it's
not
just
that
it's
a
hive
data
set,
but
the
fact
it's
of
like
a
fact
table-
or
maybe
we
want
to
call
it
a
log
table
or
something
like
that.
But
it's
basically
a
immutable
set
of
facts
that
are
being
appended
to
the
data
set
over
time
and
there's
some
annotations
about
whether
it's
a
daily
partitioned
data
set
or
not.
A
A
What's
the
status
of
this
data
set,
when
was
it
last
updated?
What
were
the
checks
that
recently
ran
and
these
checks
might
be
operational
checks
like
hey?
It
was
recently
updated,
it's
a
daily
data
set
and
it
seems
to
have
landed
on
time.
So
that
looks
good,
but
also
that
it
has
past
validation.
There
were
data
quality
checks
that
were
run
and
all
of
them
seem
to
have
passed
successfully,
whatever
your
rules
are
and
that
there
are
no
active
issues.
A
A
The
next
kind
of
more
expanded
view
of
that
same
page
is,
you
know
you
might
search
for
the
data
set.
So
this
is
not
the
lineage
way
of
getting
to
the
data
set,
but
just
the
standard
search
and
then
you
click
and
you're
into
the
data
set
detail.
Page
very
similar.
You
can
see
the
same
card
below
most
people
are
familiar
with
kind
of
the
little
array
of
tabs
that
show
up
below
the
data
set
entity.
A
Page
right
and
most
of
us
are
familiar
with
the
schema
tab
and
you
know
the
ownership
tab,
but
imagine
that
we
put
up
the
ownership
up
to
the
right.
So
it's
always
there
and
available
and
schema
can
actually
stay
on
as
a
tab
in
this
array
of
tabs.
But
we
add
on
a
summary
which
includes
this
kind
of
operational
health.
A
Actually,
this
one
is
yeah
scrolling
down
a
little
bit
so
you're
you're
up
in
kind
of
the
entity,
detail
page
and
you
scroll
down
from
the
summary
into
kind
of
the
event
section,
and
you
can
see
kind
of
a
timeline
of
events
plotted
on
a
graph
that
shows
when
this
data
set
landed
now
for
streaming
data
sets,
there
might
be
a
different
thing
that
makes
sense.
This
is
a
fact-oriented
warehouse
table
right,
so
you
might
want
to
look
at
it
in
that
way.
You
know.
A
Typically,
you
know
the
standard
stuff
typically
lands
at
5
00
a.m,
but
at
the
sla
is
that
it
should
be
ready
by
6
a.m.
But
you
know
a
few
days
ago
there
was
a
problem
and
it
actually
showed
up
at
7.
00
am
or
8
am,
and
so
that
it's
late,
but
the
good
news
is
that
the
validation
checks
were
actually
run
on
that
partition
and
it
was
actually
validated
as
being
a
correct
partition
of
the
data
set.
A
You
have
the
overall
statistics
about
the
data
set,
including
a
quick
histogram
or
a
quick
trend
line
that
shows
you
how
many
rows
are
being
added
per
day
and
whether
that
is
in
line
with
what
you
usually
get
and
things
like
that.
The
goal
again
is
to
have
a
very
simple
and
easy
way
to
understand.
If
this
data
set
is
behaving
as
expected,
because
every
data
set
is
going
to
be
different,
there
will
be
some
data.
Sets
that
are
weekly
data
sets
be
hourly
and
then
for
streaming.
A
We
expect
to
allow
customization
about
what
you
think
are
important
events
and
allow
you
to
plug
in
different
producers.
That
can
say
different
things
about
the
data
set
so
that
it's
all
in
one
place
combined
and
multiple
tools
can
probably
be
emitting.
Important
updates
about
the
data
set
up
into
the
right.
You'll
see
this
report
and
issue
button
right
here,
and
that
is
meant
for
humans
to
essentially
say
hey.
I
think,
there's
a
problem
with
this
data
set
and
you
can
imagine
how
that
workflow
will
look
like
a
report,
a
problem.
A
The
problem
gets
logged
gets
routed
to
the
right
owner,
maybe
that's
integration
with
your
ticketing
provider,
whether
that's
servicenow
or
jira,
or
what
have
you
and
then
that
then
reflects
back
in
the
active
issues
that
are
going
on
with
the
data
set.
So
the
next
time
someone
goes
and
finds
the
data
set,
they
can
actually
see.
Oh
looks
like
there's
some
problem
that
someone
else
has
already
filed
and
they
can
go
check
on
how
that's
going
so
that's
the
events
table
stats
is
exactly
what
it
says.
A
A
So
once
you're
looking
at
a
data
set,
you
can
drop
into
the
validation
tab
and
it
shows
you
all
of
the
assertions
that
have
been
run.
Assertions
that
are
running
failing
succeeding,
what
those
assertions
even
are
and
being
able
to
kind
of
see
at
a
glance
how
things
are
going.
This
is
sort
of
like
your
cicd
view,
of
the
data
set
right.
A
A
A
Okay,
awkward
silence
done,
hopefully
everyone
at
least
have
the
form
open.
We're
gonna
drop
it
on
the
slack
channel
as
well,
so
you
can
always
get
back
to
it
and
we'll
drop.
Obviously,
the
slides
are
gonna
be
public,
so
you
can
go
back
and
look
at
them
and
tell
us
what
you
think
this
work
will
be
done
over
the
next.
You
know
month
or
two
months.
So,
as
you
can
see
from
the
roadmap,
we
do
have
quite
a
bit
of
work
ahead
of
us,
so
the
earlier
we
get
feedback
in
the
more.
A
A
All
right,
so
that's
pretty
much
it
from
a
programming
scheduled
programming
perspective.
We
are
not
yet
announcing
the
next
town
hall,
but
I'll
I'll
drop
in
the
it.
You
know
it's
the
standard
third
friday
of
every
month,
but
we'll
figure
out
the
exact
timing,
whether
we
move
it
by
one
hour,
we'll
get
some
feedback
from
the
committee
around
how
they
felt
the
timing
was
for
them.
For
me,
the
coffee
was
great,
so
I'm
feeling
fine
right
now
I
look.
A
I
took
a
quick
look
at
the
q,
a
section
and
there
were
a
couple
of
questions
that
popped.
One
was
around
business
glossary
and
the
second
goes
around
moving
from
confluence
to
data
hub,
I
can
address
the
first
one.
Really
quick
business
glossary
is
something
that
we've
been
working
with
the
community
on
for
a
while.
A
But
now
you
can
kind
of
browse
into
a
specific
section
of
the
taxonomy
and
attach
it
and
give
it
a
different
kind
of
semantic
meaning.
So
we
think
the
application
process
like
how
you
apply
terms
to
data
sets
or
to
fields
are
going
to
look
very
similar,
but
the
way
you
manage,
the
the
taxonomy
itself
is
probably
going
to
be
much
more
interesting.
There
will
be
hierarchies.
A
One
of
the
things
on
the
roadmap
is
role-based
access,
control
or
fine-grained
access
control
on
metadata
elements
themselves,
so
that
will
allow
for
kind
of
the
tag,
governance
and
allow
for
business
glossary
governance
so
that
you
can
essentially
demarcate
owners
of
a
certain
taxonomy,
and
that
then
allows
this.
This
feature
to
actually
make
sense
for
a
lot
of
companies.
A
A
A
A
A
A
One
of
the
easiest
features
that
we
can
think
of
is
to
just
allow
editing
markdown
directly
in
the
data
set
description
page
so
that
you
can
kind
of
have
a
living
document
that
you're
maintaining
about
the
data
set
just
like
we
have
our
readmes
and
other
things
in
our
github
repositories.
So
that
is
definitely
something
that
is
on
our
short
term
roadmap
and
that
should
be
coming,
but
deeper
integration,
maybe
back
and
forth
between
confluence
and
data
hub,
is
not
something
that
we
have
planned
for.
B
D
A
Didn't
know
there
was
a
town
hall,
so,
okay,
I
guess
I'll-
have
to
do
an
answer
just
to
remind
people.
So
so
we
will
announce
town
halls
over
there
and
sorry
office
hours
in
the
slack
channel.
Thank
you
just
pay
attention
to
it
and
we'll
give
kind
of
a
wide
range
of
time
so
that
people
can
hop
in
and
out.
A
A
Yeah
yeah
that
would
be
interesting,
keep
the
ideas
coming
and
keep
the
contributions
coming.
I
think
it's
been
just
amazing
to
see
how
the
community
has
kind
of
taken
this
project
and
taken
it
to
places
that
we
ourselves
are
not
able
to
do
small
teams
right
everywhere
busy
with
many
other
things.
So
this
is
amazing
and
I'm
really
excited
about
dbt
and
looker.