►
Description
Apache Kafka is getting used as an event backbone in new organizations every day. However, most of the time, connecting to simple third party applications and services becomes a headache that involves several lines of code and additional applications. As a result, connecting Kafka to services is a challenge nobody wants to face.
Wouldn't you like to have hundreds of connectors readily available out-of-the-box to solve this problem? The Camel Kafka Connect project, from the Apache foundation, has enabled their vastly set of connectors to interact with Kafka Connect natively. So, developers can start sending and receiving data from Kafka to and from their preferred services and applications in no time without a single line of code.
In summary, during this session will:
- Go over the list of connectors available as part of the project
- Showcase a couple examples of integrations using the connectors
- Share some guidelines on how to get started with the Camel Kafka Connectors
A
Five,
all
right
everybody
and
welcome
to
another
monday
morning,
openshift
commons
briefing
as
we
like
to
do
on
mondays,
is
talk
about
upstream
projects
and
how
they
interact
with
things
like
openshift
and
today.
I'm
really
pleased
to
have
hugo
guerrero
with
me
from
red
hat
and
he
is
going
to
walk
us
through
tuning
kafka
to
speak
with
almost
everything
I
like
that
one
title
and
talk
about
the
camel
connector
for
apache
kafka
and
I'm
going
to
let
hugo
introduce
himself
and
do
his
demos.
A
B
B
So
a
little
bit
of
about
me
for
the
people
who
who
know
who
doesn't
know
me
it's
it's
hugo.
It's
I'm
a
mexican
currently
based
in
westford
in
the
massachusetts
area
in
the
us
and
I'm
a
specialist
in
in
apis
and
even
driven
architectures
from
redcat,
I'm
currently
working
as
a
developer
advocate
for
redhead
integration,
and
today
we're
gonna
expand
the
next
couple
of
minutes.
Talking
about
three
four
specific
topics.
First,
we're
gonna
be
doing
a
quick
recap
on
what
it's
apache
kafka.
B
What
are
the
challenges
and
a
little
bit
on
on
the
way
to
do
integration
using
using
apache
kafka
and
then
we're
gonna
be
drifting
into
apache
camel,
so
we
will
be
talking
about
apache
kafka
and
we're
going
to
be
talking
about
camel
and
we're
going
to
be
talking
about
the
camel
sub
projects
that
allows
us
to
extend
the
kafka
ecosystem
to
other
systems.
So
we
will
be
talking
about
the
cafe.
Connector,
the
and
the
new
camellids
that
are
coming
as
part
of
camel
k.
B
So
let's
do
a
quick
recap
on
what
is
apache
kafka
and
what
is
so
important.
So
apache
cup
is
a
project
that
was
created
by
linkedin
around
2010
and
it
was
mainly
focused
to
to
be
tracking
the
website
activity
within
the
linkedin
portal.
You
know
all
the
clicks
who
you
are
checking
what
jobs
they're
looking
for
and
and
so
on,
but
they
they
noticed
that
their
mrexcel
system
were
not
handling
enough,
the
transactions
that
they
were
having
in
the
site.
B
So
they
decide
to
refactor
and
rethink
outside
the
box
the
way
to
handle
events
that
are
happening
in
a
web
system.
So
that's
how
they
created
apache
kafka-
and
this
is
this-
has
been
now
many
many
uses.
Now.
It's
been
mainly
focused
as
a
publish,
subscribe
messaging
system,
but
also
it's
also
a
data
streaming
platform
because
now
has
a
ground
with
some
apis,
but
in
the
core
it
is
a
distributed,
commit
log.
B
So
it's
a
very
well
tuned
broker
that
delegates
most
of
the
of
intelligence
or
the
activities
into
the
producer
consumer
clients
and
it
specializes
only
in
receiving
events
and
then
persisting
those
events.
However,
it
now
has
outgrown
from
the
apache
project
and
there's
different
components
around
that
that
has
created
a
broader
ecosystem,
so
we
do
have
components
for
doing
a
replication
like
mirror
maker.
We
have
the
cafe
connect.
We
will
be
talking
about
that.
B
We
have
the
apis
and
and
also
third-party
toolings,
like
http,
bridges
and
and
so
on,
and
the
thing
important
hit.
Apache
kafka
is
that
apache
by
itself,
it's
not
the
goal,
so
you
are
not
alone.
So
it's
not
just
kafka
by
itself.
You
won't
really
get
any
benefit
by
just
having
kafka
and
being
able
to
deploy.
You
need
to
go
beyond
that,
so
you
cannot
be
just
one
single
jedi,
try
to
fight
and
defeat
the
galaxy
you
need
some
help
for
for
that.
B
So,
let's
talk
about,
how
can
we
get
a
little
bit
of
help
as
a
jedi?
So
the
first
thing
that
I
was
mentioning
it's
about
connect,
so
kafka
connect.
Was
this
first
attempt
from
from
the
apache
community
to
be
able
to
increase
the
scope
and
being
able
to
simplify
the
way
to
connect
to
apache
kafka
and
then
bring
data
from
data
and
events
and
and
messages
from
other
systems
and
also
being
able
to
take
those
messages
away.
B
So
it
basically
start
to
wrap
around
the
producer
and
consumer
apis,
because
that's
that
was
the
original
way
to
connect
to
kafka.
You
need
to
create
your
own
application
code
it
by
yourself
and
being
able
to
handle
all
these
all
these
different
things
like
offsets
like
the
way
to
commit
and
and
so
on.
So
the
idea
was
to
create
this
framework
that
can
be
reused
to
easily
manage
the
most.
B
And
then,
when
you
are
already
deployed,
you
are
able
to
get
all
the
benefits
from
the
from
this
framework
and
being
able
to
run
as
a
standalone
service,
or
the
benefit
is
that
you
can
also
create
a
kafka
connect
cluster
that
is
able
to
manage.
Then
the
life
cycle
of
those
connectors.
So
you
are
able
to
handle
the
information
and
you're
able
to
add
some
integration
pieces
like,
for
example,
the
single
message
transformations
that
allows
you
to
do
some.
B
So
that's
that's
a
very
good
use
case,
and
this
is
mainly
because
cafe
connect
and
if
you
remember,
apache
kafka
was
created
in
the
2010
and
it
was
mainly
focused
and
heavily
on
on
on
virtual
machines
and
and
servers
and
bare
metal
and
so
on.
So
let's
check:
how
can
we
move
them
into
a
kubernetes
space?
For
that
so
remember,
you're,
a
jedi
you're,
not
alone.
B
You
need
to
talk
to
other
jedi,
so
you're
now
able
to
get
into
the
galaxy
gather
some
friends
and
you
suddenly
realize
that
you
are
able
to
to
to
together
a
team,
but
they
are
from
different
species.
You
need
to
talk
different
languages.
So
that's
where,
where
it
comes
the
the
title
of
this
session
right,
so
how
can
we
tune
kafka
to
to
be
able
to
speak
these
languages?
So
if
you
remember
this,
this
movie
is
star
wars.
There's
one
way
there
or
everybody
can
use
to
communicate
with
others.
And
that's
true.
B
You
know
a
specialized
device
like,
for
example,
gpo
that
helps
you
to
be
fluent
in
over
six
million
forms
of
communication.
So
that's
why
you're
able
to
then
communicate
with
whatever
you?
You
require
so
this
is
what,
for
example,
apache
coming
will
come
in
will
become
this
adapter
that
helps
us
to
speak
with
all
the
different
systems,
either
as
a
sync
or
as
a
source
to
our
apache
kafka
cluster.
B
They
have
more
than
350
different
connectors
for
different
systems
that
go
from
slag
to
telegram
to
to
twitter,
to
aws
services
and
and
so
on,
and
it
also
allows
you
to
handle
some
different
types
of
data
formats
and
protocols
like
json
abroad.
If
you
want
to
serialize
allows
you
to
handle
web
sockets,
http
and
so
on.
So
that's
why
it's
it's
almost
everything.
If
you
have
imaging,
then
you
need
to
do
an
integration
with
one
system
it.
B
It
certainly
has
been
addressed
by
camel
or
if
not
it's,
it's
underworks,
because
that's
something
that
anyone's
doing
integration
has
been
has
been
facing
in
the
past,
so
most
of
them
are
coming
to
apache
camera
and
it's
a
it's
an
open
source
project
that
allows
you
to
also
be
able
to
contribute
to
those
to
those
connectors.
So
it's
it's
a
growing
ecosystem
all
the
time
and
you
have
the
connector.
You
have
the
formats
the
protocols
you
can
then
implement
enterprise
integration
patterns.
B
So
for
for
the
for
the
regular
framework,
you
can
do
complex
things
like
content,
enrichment,
twitter,
aggregators
and
and
so
on.
It's
it's
a
very,
very
active
project
and
community.
That's
one
of
the
most
committed
projects
in
the
apache
foundation.
So
it's
a
it's
a
very,
very
live
project
and
it
basically
takes
these
connectors
and
and
and
now
the
camera
is
it's
able
to
run
on
on
different
environments.
B
It's
like
you
know,
looking
at
c3po
without
the
the
the
shell,
there
are
different
components:
the
contacts,
the
routes,
the
filters
and
the
processors.
But
the
important
thing
here
is
really
the
the
lower
part
where
you
have
all
the
components,
and
these
components
are
the
ones
that
will
allow
us
to
plug
into
you
know
all
these
systems
slag
telegram,
either
as
a
sync
as
a
source
and
then
the
rest
of
the
of
the
engineering
on
on
the
camel
framework
then
do
the
processing
and
and
and
the
communication.
B
However,
we
won't
be
really
going
that
deep.
This
is
for
like
people
that
it's
deploying
their
other
connectors
and
or
creating
these
integrations.
So
most
of
the
times
are
you
know
the
the
the
camel
team,
but
you
can
also
do
that.
The
exact
same
thing.
So,
if
you
feel
brave
enough
and
you
think
that
will
benefit
the
community,
you
can
go
in-depth
and
then
start
working
with
with
internals,
but
for
avoiding
that
going
too
deep
into
the
into
the
details.
B
So
today
we
will
be
covering
two
of
those
companies,
so
there
are
more
than
six
projects,
but
but
the
ones
that
we
are
really
targeting
with
this
integration
with
camel
are
two
pro
suit
projects.
The
first
one
is
the
camel
kafka
connector
project
and
the
other
one
is
the
camel
k
and
the
camelitz
one
that
allows
us
to
have
two
different
visions
of
how
to
connect
to
kafka
using
apache
camera
and
get
the
the
most
of
of
this
framework
for
for
doing
your
integration.
B
So
basically
they
add
this
tiny
layer
to
make
it
easy
to
plug
and
deploy
and
been
able
to
manage
by
a
kafka
connect
cluster.
So
there's
a
list
of
all
the
connectors,
all
the
three
40
type
of
connectors
available.
If
you
want
to
go
later
and
at
the
end,
I
will
share
some
of
these
links
with
you
and
it's
a
project
from
the
apache
camera
project.
B
B
So
in
in
this
way
you
need
to
define
the
name
of
your
of
your
of
your
connector.
Then
you
need
to
obviously
have
already
download
those
connectors,
so
you
need
to
download
the
jar
file
you
deploy
that
in
your
connect
cluster
and
then
start
configuring
this.
So
you
can
either
deploy
as
a
standalone
configuration
using
this
kind
of
approach
or
you
can
reuse
the
rest
api
when
you're
deploying
on
a
distributed
mode
for
the
kafka
connect
cluster.
B
So
then
you
can
define
your
serializer
and
your
key
and
value
converters
or
not
serializer
converters
to
to
handle
the
the
different
type
of
of
serialization
for
for
your
data,
and
then
you
can
configure
all
the
specifics
of
the
common
components.
So
you
can
do
the
topics
that
you're
going
to
be
reading
from
or
write
into,
and
then
you
can
configure
specifics
of
each
one
of
the
components
like,
for
example,
for
for
amazon
s3.
You
need
to
configure
the
access
key,
the
secret
key.
B
B
So,
let's
get
a
little
bit
on
how
these
camel
cannon
cameras
are,
are
making
a
camel
cool
for
kafka.
So
a
little
bit
of
camo
k,
as
we
mentioned
camo
camel,
has,
is
an
umbrella
project
that
has
different
sub
projects,
handling
different
specific
things.
So
the
core
is
obviously
focusing
on
the
framework
by
itself.
We
talk
about
the
camel
kafka
connectors
that
focus
on
how
to
make
these
connectors
to
work
with
with
a
kafka
connect
and
camo
k.
B
It's
the
platform
that
is
specialized
on
running
camera
integrations
on
openshift
and
kubernetes
in
general.
So
basically,
it's
taking
this
declarative
approach
that
kubernetes
offers
us
to
be
able
to
define
these
components
these
connectors.
These
integrations
these
transformations
as
as
custom
resources.
B
We
we.
We
realized
that
there's
also
other
kind
of
approaches
like,
for
example,
now
doing
doing
kafka
to
simplify
the
the
the
kind
of
integrations
that
we
want.
So
it's
also
part
of
the
community.
It's
also
a
sub
project
and
it
started
around
three
years
ago.
So
it's
now
on
on
the
the
latest
version,
so
1.4.
So
it's
already
maturing
and
the
idea
is
that
camel
k
uses
the
operator
pattern
to
be
able
to
manage
integrations,
and
the
interesting
thing
about
camel
k
is
that
it
has.
B
B
You
know,
be
restarted.
You
can
check
what
is
the
status
and
you
can
also
be
able
to
query
the
kubernetes
api
to
get
access
to
other
kubernetes.
Other
sorry,
custom
resources
available
in
that
exact
same
cluster.
So,
as
we
were
going
to
be
seeing
in
in
demo,
we
have
a
little
bit
of
time.
You
are
able
to
reference,
then
other
components
that
are
available
in
your
cluster.
B
So,
for
example,
we
are
going
to
be
seeing
that
if
you
are
running
kafka
and
kubernetes
using
stream
c,
for
example,
extreme
c
also
uses
this
declarative
approach
to
define
your
kafka
resources.
You
can
define
your
kafka
cluster
custom
resource
and
your
kafka
topics
as
custom
resources,
so
the
benefit
of
having,
for
example,
the
definition
of
a
kafka
topic
as
a
custom.
Resource
definition.
Is
that
then
camel
k
can
reference
that
that
resource
and
being
able
to
link
all
the
information
attached
to
that
resource.
B
As
you
can
see,
we
have
some
of
the
companies
that
we
were
mentioning
from
from
the
camel
kafka,
connector
and
also
available
scamlet
scamlets
in
in
the
camelot
catalog,
where
you
can
get
all
these
connectors
and
then
just
reuse
them
within
your
cluster
to
be
able
to
install
them
and
then
being
able
to
start
using
those.
So
the
apache
camel
site
in
in
in
in
the
in
the
web
has
this
catalog,
where
you
can
sort
and
search
for
the
different
connectors
you
have
connectors.
B
You
also
have
some
transformations
and
we
will
be
adding
as
part
of
the
project
more
and
more
connectors
and
more
and
more
transformations,
so
you
are
able
to
reuse
most
of
them.
So,
for
example,
when
you
are
using
kafka
connect,
you
have
not
only
the
connectors,
but
also
you
are
able
to
leverage
some
of
the
transformations
like,
for
example.
B
If
I
want
to
you
know,
add
a
field
or
extract
some,
some
value
from
the
from
the
from
the
payload
or
being
able
to
change
the
the
key
of
my
record,
my
kafka
record
or
so
on.
Some
those
are
some
of
the
things
that
you
can
do
as
part
of
the
single
message
transformations
that
I
were
that
I
was
mentioning
at
the
beginning.
B
B
B
So
this
with
this
information,
you
just
need
to
tell
that
you
are
going
to
be
taking
from
the
timer
and
then
you
will
be
delivering
your
event
or
your
record
into
that
specific
channel
there.
You
can
also
replace
that
not
just
with
kennedy
channels,
but
also
you
can
use
for,
for
example,
kafka
topics
or
you
can
use
other
camelets
as
the
destination
for
your
for
your
for
your
binding.
B
So
you
can
then
start
to
build
your
own
data
pipelines
using
this
type
of
camera
bindings
reusing
some
of
the
cameras
already
available
to
create
more
complex
flows.
In
this
case,
it's
very
simple:
we
are
just
having
a
timer
that
is
producing
those
events,
but
you
can
then
have
something
more
clinics
are
more
more
focused
on
on
the
on
more
complex
integrations.
B
B
B
I
will
recommend
to
to
have
this
the
exact
same
exact,
same
approach.
Now,
as
I
was
mentioning,
so
we
do
have
camelets.
So
this
is
a
camel
example,
for
example,
for
a
transformation,
and
this
is
the
components
that
are
part
of
the
camelot
catalog.
So
if
you
want
you
can
create
and
configure
and
develop
your
own
camelets.
B
So
unlike
this,
there
are
different
type
of
cameras.
Another
example
for
is
the
telegram
thing.
So
if
you
want
to
send
kafka
messages
into
a
camelot,
then
you
are
able
to
check
the
description
of
this
of
this
one,
the
different
properties
that
you
require,
like
the
authorization
token
from
from
telegram
the
chat
id
where
you
want
to
to
deliver
those
messages
and
then
the
different,
the
different
flows,
and
here
we're
using
this.
This
yaml
approach,
because
it
makes
very
easy.
B
So
then,
how
you,
how
you
look
at
those
when
you
are
binding
them,
for
example
the
the
telegram
one,
how
you
can
use
it
is
yeah.
We
already
see
the
the
camera,
and
now
we
are
using
the
camelot
binding.
Then
we
are
referencing
referencing,
for
example,
the
kafka
topic
that
we're
going
to
be
taking
those
those
messages,
and
then
this
this
example
shows
how
to
build
these
pipelines
that
I
was
mentioning
in
the
past
right,
so
you
can
have
different
different
steps
or
different
phases
of
your
data
pipeline.
B
So
this
is
this
is
how
the
the
different
objects
can
can
be,
can
be
related
and
then
how
you
deal
with
them
is.
If
we
go
to
the
terminal,
if
we
get
here,
we
are
also
in
the
same
in
the
same
process.
So
what
we
recommend
to
use
it's
the
camel
cli
the
camel
command
line,
tooling,
helps
you
to
deal
and
easily
manage
your
your
camera
resources.
B
If
this
yeah
there's
a
one
timer
that
is
already
working
but
also
we
can
add,
mark
hamlet,
so
we
can
see
as
you
as
as
you
are
seeing
I'm
using
casey.
It's
the
cubicle
command
for
occurring
the
the
openshift
api.
So
we
are
able
to
get
all
these
custom
resources
as
part
of
the
openshift
ecosystem,
so
casey
get
camel
x.
B
So
you
see
there's
no
cameras
here,
but
we
can
then
apply
and
then
let's
and
this
will
install
all
the
different
hamlets.
So
in
this
case
I
have
these
cameras
here,
local
in
my
machine,
but
you
can
also
use
the
ones
that
are
already
available
in
the
site.
So
if
you
are
going
to
the
to
the
camelet
camelot
side,
you
will
be
able
to
find
them.
B
And
so
you
can
see
here.
What
is
the
information
about
the
configuration?
So
you
need
the
message.
That's
the
the
the
the
field
that
you,
the
property
that
you
need
to
to
do
it
and
then
there's
other
information
like
the
context,
type,
the
period
and
and
so
on.
But
these
are
optional.
The
only
one
mandatory
is
the
message,
and
there
are
some
examples
like
the
one
that
I
share
with
you.
B
B
B
So
in
my
case
as
I
was
mentioning,
we
need
to
create
the
camel
kafka
topic
and
because
we
create
that
as
a
custom
resource,
then
we
are
able
to
reuse
them
in
the
in
the
example
that
I
was
that
I
was
showing
so
this
when
we
create
this.
What
we
see
is
that
we
have
the
parts
running
all
my
three
kafka
brokers,
my
three
zookeepers
and
the
operator
is
already
available
here
and
then
I
have
this.
B
This
spot
already
running
with
information
on
the
on
on
on
on
the
integration,
and
this
is
created
because
the
the
the
integration
is
already
running,
but
I
can
delete
what
that
went
as
I
was
mentioning
so
casey
deleted,
camelot,
binding
timer
to
kafka.
This
will
delete
the
that
integration
or
that
part
that
it's
running
the
actual
integration
between
the
timer
and
my
kafka
cluster,
and
it
removes
that
part.
B
If
I
do
apply
again,
the
the
timer
to
kafka
the
ammo
file,
the
camel
k
operator
takes
that
definition
that
was
created
now.
As
a
custom
resource
and
then
deploys
my
pod
and
starts
then
to
run
within
the
connection,
so
if
you
notice,
I
didn't
have
to
configure
my
my
kafka
bootstrap
server.
I
just
add
the
reference
to
the
to
the
kafka
topic
that
it's
obviously
linked
to
a
kafka
cluster
where
it
was
being
created
using
the
string
c
operator
in
the
case
of
the
chemical.
B
I
just
make
the
reference
to
the
kafka
topic
and
then
it
automatically
wired
all
the
information
to
connect
to
my
my
kafka
cluster.
So
that's
one
of
the
interesting
things
about
using
camera
lights
and
camel
k.
You
don't
need
to
configure
everything
so
it's
able
to
easily
connect
and
plug
into
this
into
this
cluster.
B
B
Topic
if
everything
works,
then
we
should
be
able
to
see
yeah
our
hello
world
message
being
received
every
one.
Second,
as
we
configure
in
our
our
information
and
our
integration.
If
I
want
to
update
this
message,
it's
very
straightforward.
I
just
go
to
my
timer
to
kafka
configuration
so
in
this
instead
of
saying
hello
world,
we
will
gonna
be
putting
hello,
open
shift,
save
that
one
and
then
let's
apply
this
again.
B
B
You
can
get
from
from
something
simple
or
complicated,
depending
on
what
it's
your
integration
and
then
we
are
defining
here
directly.
So
the
good
thing
about
this
is
that
you
don't
need
kafka
connect
anymore,
so
you
can
rely
on
all
the
benefits
of
the
distributed,
kubernetes
api
to
handle
the
lifecycle
of
your
post,
restarting
scaling
and
and
so
on.
So
it's
it's.
It's
very
interesting
to
have
this.
B
Okay,
let's
get
back
to
the
slides
after
we
saw
a
little
bit
of
this
demo
and
let's
do
a
close-up
for
for
this.
So
I
was
mentioning
we
have
these
camel
connectors
for
kafka
and
they're
part
of
the.
There
are
soft
projects
of
the
of
the
camel
umbrella
project.
It
allows
you
to
combine
the
power
of
all
these
big
projects
like
kafka
and
camo,
to
work
together
and
then
broader
the
good
system
and
simplify
the
way
to
build
your
data
pipelines
using
kafka.
B
So
in
this
way,
if
you're
a
kafka
user,
then
you
can
get
now
all
these
big
set
of
components
and
transformations
available
is
up
to
you.
You
are
able
to
then
create
your
own
without
having
to
code
at
all
no
java,
as
you
can
see
in
the
examples
you
can
just
define
your
flows,
how
your
information
is
going
to
be
handled,
how
your
transformation
is
not
going
to
be
managed,
take
decisions,
choice,
implement
your
patterns
without
coding
and
getting
your
custom
integrations
and
for
the
existing
camel
users.
B
You
can
easily
reuse
all
your
skills
that
you
have
as
a
camel
developer.
As
a
camel
user
and
then
get
into
the
kafka
world
very
easily,
be
using
this
information
without
having
to
relearn,
apis
or
or
the
way
to
connect
using
the
the
the
the
kafka
component.
So
that's
a
very
easy
way
to
to
change
your
your
skills,
so
there's
some
useful
links.
B
B
You
can
also
please
join
the
the
camel
sleep
chat,
so
you
are
able
to
join
the
community
share
with
with
us.
What's
what
you
think
about
camel
k,
and
obviously
you
can
also
follow
them
on
twitter.
You
can
also
start
the
github
repos.
I
encourage
you
to
do
that
so
you're
able
to
participate
and
give
us
your
feedback,
and
I
think
that's
that's
all
on
my
on
my
site.
I
don't
know
if
there's
any
questions
comments,
something
like.
A
Yeah,
so
I
would
thank
you
so
much
for
go
walking
through
all
this,
and
I'm
really
intrigued
actually
personally
by
the
camelettes
end,
the
catalog.
If
you
could
go
back
and
show
us
what's
in
there
for
besides
the
one
that
you
demoed
I'd,
be
really
interested
in
getting
your
perspective
on.
What's
in
there
what's
coming
there,
and
if
someone
wanted
to
create
a
custom
camelet,
how
would
they
go
about
doing
that
or
is
there
in
the
documentation?
Maybe
there's
a
little
bit
around
that?
A
B
B
There's
there's
a
link
to
the
developer
guide,
so
you
can
check
the
information
on
how
to
develop
your
own,
your
own
components
and,
as
I
was
mentioning
at
the
beginning,
you
can
either
reuse
the
ones
that
are
in
the
in
the
community
or
if
you
develop
locally,
as
I
I
did
show
you
when
with
the
ones
that
I
had
in
my
in
my
own
local
file
system,
that
I
add
them
to
my
cluster.
Those
those
are
the
ways
to
to
those.
B
You
can
have
your
own
repo,
where
you
can
have
them
private
or
you
can
oh
here's
there
or
you
can
go
over
them.
So
if
you
go
to
the
camelot
catalog,
you
have
this
list
on
the
left
side,
you
can
see
the
we
have
a
lot
of
for
aws
services
assure
services,
so
those
are
the
ones
that
the
community
has
already
ready
created
for
you
and
they're
available
like
for
free.
You
can
just
go
to
the
to
the
repo
and
then
just
reuse
them.
B
B
What
is
the
single
jumbo
file
that
you
need
to
work
on,
creating
a
simple
camellia,
so
you
can
go
over
this
guide
and
helps
you
to
define
how
to
create
and
what
are
the
different
components
that
need
to
create
for
for
having
your
own
hamlets,
and
now
that
you
have
the
final
xml
file
a
jumbo
file.
Sorry,
you
can
then
either
install
that
in
your
cluster
or
if
you
want-
and
please
do
that
if
it's
possible
share
with
the
with
the
camel
community,
and
so
we
are
able
to
publish
them
into
the
camera
catalog.
A
A
Yeah,
we'll
definitely
dig
up
that,
because
I
think
this
you
know
the
the
slack
channel
that
the
apache
camera
one
is
a
probably
a
really
good
place
to
go
to
to
ask
questions
and
stuff
like
that
and
find
it
out,
but
also
interested
in
joining
the
community.
This
would
be
a
a
great
time
and
a
great
way
to
get
started.
I
think
and
easy.
B
Yeah
I
mean
you:
can
you
can
join
to
the
to
the
to
the
camera
github?
If
you
have
issues
and
so
on,
you
can
follow
discussions
there,
but
I
think
most
of
the
activity
really
happens
in
the
clip
channel
and
and
that's
where
you
will
find-
certainly
everyone
and
there's
where
the
the
agendas
and
and
the
meetings
are
being
published.
It's
a
very
vibrant
community.
So
it's
I
do
recommend
you
to
to
reach
them
over
there
and
you
will
get
certainly
pointers
on
on
how
to
work
with
them.
A
B
A
B
B
A
B
A
There
you
go
all
right,
well,
hugo!
Thank
you
very
much
again,
we'll
put
this
all
together
in
a
blog
post
on
openshift.com
or
on
redhat
developers.com
very
shortly
and
get
it
out
there
and
circulate
it,
and
you
can
always
reach
hugo
on
twitter
and
hanging
around
in
that
zulu
channel.
I'm
sure
so,
thank
you
again
and
thanks
to
our
producer,
chris
short
for
hanging
in
there
with
us
today
and
making
this
happen
so
take
care
all.