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From YouTube: Databases and Data Analytics on Red Hat OpenShift
Description
Why deploy databases and data analytics workloads on containers, Kubernetes, and Red Hat OpenShift.
Learn more at openshift.com/data
A
A
Next,
we
will
talk
about
the
challenges
with
operationalizing
databases
and
data
analytics
workloads
on
containers
and
kubernetes.
Finally,
we
will
provide
an
overview
of
why
red
hat
open
shift
and
how
red
hat
openshift
can
help.
You
accelerate
cloud
native
app
development
by
deploying
databases
and
data
analytics
workloads
on
it.
A
Databases
and
data
analytics
are
crucial
to
cloud
native
development.
They
are
an
integral
part
of
modern
cloud
native
applications.
These
workloads
are
being
deployed
by
organizations
globally
for
mission.
Critical
applications
such
as
developing
mobile
apps,
ecommerce
apps
for
processing
online
transactions
for
data
and
business
intelligence
and
aiml
containers
and
kubernetes
are,
in
the
critical
part
to
succeed
with
cloud
native
development
for
successful
and
efficient
cloud
native
app,
dev
containers
and
kubernetes
are
used
to
deploy
all
varieties
of
workloads.
A
A
Openshift
is
the
container
and
kubernetes
platform
of
choice
for
databases
and
data
analytics
workloads
it
automates
and
simplifies
operations.
Redhat
openshift
enables
a
broad
spectrum
of
databases
and
data
analytics
softwares
to
be
automatically
deployed,
and
lifecycle
managed
empowering
the
system
administrators
to
focus
on
more
strategic
tasks
that
are
important
to
the
business.
A
A
A
There
are
two
types
of
databases-
relational,
for
example,
microsoft,
sequel,
server,
2019
and
non-relational,
for
example,
mongodb,
couch
base,
etc.
Data
analytics
includes
tools
such
as
apache
kafka,
redhat
data
grid,
redhat
amq
streams,
and
they
are
used
to
gather
and
analyze
data
from
multiple
sources
to
achieve
valuable
insights.
A
A
We
are
seeing
an
emerging
trend
of
databases
and
data
analytics
being
deployed
on
containers
in
kubernetes
based
hybrid
cloud
solutions.
So,
let's
talk
about
benefits
of
the
same
containers
and
kubernetes
help
accelerate
cloud
native,
app
dev
by
agile
deployment
of
databases
and
data
analytics
workloads
based
on
our
experience
of
helping
organizations
globally.
A
Due
to
the
agility
provided
by
containers
and
kubernetes
to
your
databases
and
data
analytics
workloads,
you
will
be
able
to
deploy
test
and
manage
databases
and
data
analytics
with
speed.
You
will
be
able
to
serve
your
customers
faster
and
better
with
agile
deployment
and
faster
response
times.
We
will
be
able
to
make
use
of
dynamic
scaling
of
compute
resources
to
meet
the
changing
needs
of
databases
and
data
analytics
and
scale
in
and
out,
as
per
changing
needs.
A
A
This
shows
that
a
significant
number
of
workloads
running
on
kubernetes
are
databases
and
data
analytics
workloads,
and
there
is
also
a
growing
trend
of
deploying
databases
and
data
analytics
in
containers
and
cooperators.
For
example,
there
is
a
50
increase
in
the
deployment
trend
of
postgres
in
kubernetes,
from
2018
to
2090.
A
Another
leading
company
says
deck
that
provides
a
powerful
way
to
observe
system.
Behavior,
troubleshoot
application
performance
and
secure
container
platforms
carried
out
a
survey
in
2019
that
incorporates
details
from
both
sas
and
on-prem
system
users
to
provide
a
snapshot
of
enterprise
usage
across
well.
Over
2
million
deployed
containers
database
such
as
redis
postgres,
sql
mongodb
and
data
analytics,
such
as
apache
elasticsearch,
make
up
for
nearly
50
of
the
workloads
being
run
in
containers.
A
A
This
is
a
desired
conceptual
architecture
for
databases
and
data
analytics
on
containers
in
kubernetes.
The
first
step
in
the
databases
and
data
analytics
life
cycle
is
to
ingest
and
aggregate
the
data
from
various
sources,
for
example,
sources
like
sensors
and
smart
devices,
social
media
streams,
banking
transactions
and
so
on.
Etl
operations
are
carried
out
in
various
on
various
forms
of
the
data.
For
example,
the
data
could
be
stream
stream
data,
batch
data
etc,
and
then
the
data
is
ingested.
A
A
There
are
a
few
implementation
challenges
that
come
with
deploying
databases
and
data
analytics
workloads
on
containers
and
kubernetes
data
laws.
Failures
and
downtime
is
a
major
concern
with
these
with
containerizing
databases
and
data
analytics.
So
is
the
operational
complexity
and
performance
tradeoff
that
might
be
involved.
A
A
Let's
explain
how
we
built
openshift
at
red
hat.
We
use
a
hundred
percent
open
source
development
model
to
deliver
enterprise
products.
Our
secret
source
is
our
ability
to
own
dozens
and
hundreds
of
open
source
projects
in
a
production
ready,
stable
and
secure
enterprise
products
that
we
support.
Over
many
years
we
have
been
contributing
to
kubernetes
and
many
other
projects
that
openshift
is
based
on
since
day.
One
red
hat
is
the
leading
enterprise
developer
of
kubernetes.
A
This
is
why
you
should
be
deploying
databases
and
data
analytics
workloads
in
openshift
with
openshift.
You
gain
access
to
automated
operations
with
kubernetes
operators,
consistency
and
portability
across
different
clouds
and
across
all
parts
of
the
app
development
life
cycle,
and
we
also
have
deep
partnerships
and
strategic
integrations
where
database
and
data
analytics
is
to
ensure
support
access
and
the
ease
of
analyzing
and
visualizing
the
data
easily.
A
A
With
openshift,
you
get
automated
operations
because
of
kubernetes
operators.
Operators
automate
a
lot
of
day
1
2
operations
such
as
provisioning,
backup,
scaling,
etc,
which
will
help
system
administrators
database
administrators
software
developers
to
focus
on
more
project-centric
and
strategic
tasks.
A
Operators
make
databases
more
accessible
and
supported
a
variety
of
database
and
data
analytics
rsvs
have
made
custom
operators
available
on
red
hat
openshift.
Red
hat
is
actively
working
with
database
and
data
analytics
isvs
to
ensure
that
the
operators
are
certified
on
openshift
are
up
to
date,
secure
and
have
regular
scans.
A
Openshift
your
secure
deployment
operations
and
portability
in
a
consistent
way
across
the
hybrid
cloud.
It
offers
a
unified,
consistent
manner
of
deploying
databases,
data
and
analytics
in
all
workloads
on
the
same
platform
across
different
phases
of
your
app
development,
life
cycle,
bait,
development,
test
or
deployment.
A
These
deployment
and
all
of
these
operations
are
secure
and
can
be
done
in
a
consistent
manner
across
the
hybrid
cloud,
because
red
hat
open
shift
offers
several
options.
The
fully
managed
option
it
you
have
red
hat
open
shift
on
azure
openshift
is
supported
on
aws
on
ibm
cloud
on
google
cloud
and
we
have
a
self-managed
option.
All
of
these
options
are
to
accelerate
deployments
by
based
on
whatever
you
choose,
based
on
your
need.
A
We
have
key,
we
have
strategic
partnerships
and
deep
integrations
with
key
isvs,
and
we
also
have
red
hat
products
in
the
data
analytics
ecosystem
space.
These
integrations
and
partnerships
ensure
that
customers
can
choose
from
a
portfolio
of
databases
in
their
kubernetes
operators
to
deploy
their
databases
and
data
analytics
workloads
easily.
A
Red
hat
portfolio
helps
provide
complementary
products
to
support
you
throughout
the
databases
and
data
analytics
lifecycle
in
openshift.
It
had
storage
portfolio,
includes
redhat,
openshift,
container
storage
and
attached
safe
storage
that
provides
software
defined
storage
capability
for
containers
and
helps
address
beta
byte
scale
storage
requirements.
A
We
have
a
broad
set
of
isp
ecosystem
and
strategic
integrations
that
will
help
you
simplify
and
manage
databases
and
data
analytics.
All
of
these
tools
can
be
run
and
are
enabled
by
red
hat
openshift.
It
also
supports
all
kinds
of
footprints
such
as
aws,
microsoft,
azure,
ibm
cloud,
google
cloud
and
redhat
openstack
platform.
A
A
So,
to
summarize,
databases
and
data
analytics
are
critical
to
cloud
native
development.
They
are
an
integral
part
of
modern
cloud
native
applications
and
they're
being
deployed
by
organizations
globally
for
a
variety
of
use.
Cases
such
as
developing
mobile
apps,
e-commerce,
apps,
iot,
apps,
aiml,
business,
intelligence,
etc,
and
containers
and
kubernetes
are,
in
the
critical
part
to
succeed
with
cloud
native
development.
A
Openshift
is
a
container
in
kubernetes
platform
of
choice
for
databases
and
data,
analytics
workloads
it
automates
and
simplifies
the
operations
by
providing
a
broad
spectrum
of
databases
and
data
analytics
operators
to
be
automatically
deployed
and
life
cycle
managed,
which
will
empower
the
system
administrators
to
focus
on
more
strategic
tasks.
It
gives
consistency
and
portability
across
hybrid
cloud.
A
It
offers
flexibility
by
providing
a
platform
across
the
hybrid
cloud,
including
edge,
and
provides
consistency
across
public
cloud,
private
cloud,
on-prem
and
consistency
throughout
your
app
development
life
cycle,
be
it
say,
development,
test
or
deployment.
Red
hat
also
has
partnerships
and
integrations
with
key
database
and
data
analytics
rsvs
to
help
make
our
mutual
customers
successful.
A
As
next
steps,
we
recommend
giving
us
an
opportunity
to
come
back
and
do
detailed
requirements,
discovery
sessions
to
help
us
develop
a
solution,
strategy
and
execution
roadmap
for
your
specific
goals.
You
can
also
learn
more
about
our
database
and
data
analytics
capabilities
and
see
success
stories
from
existing
customers
on
our
website.
A
You
can
also
watch
webinars
with
our
partners
to
discover
a
wide
range
of
videos.
Answering
all
your
database
and
data
analytics
related
questions.
Please
reach
out
to
our
sales
team.
If
you
want
to
learn
more
or
have
any
questions,
thank
you
so
much
for
listening.
I
hope
you
have
a
good
rest
of
the
day.