You can improve your chances for success with data intensive large scale applications (AI, machine learning and analytics) in production. This keynote presentation from Big Data London shows you how.

1. ELLEN
FRIEDMAN
BIG
DATA
LONDON
Principal
Technologist
14
November
2018
7
Successful
Habits
for
Data-­‐Intensive
ApplicaBons
in
ProducBon

2. 2 © 2018 MapR Technologies, Inc.
Ellen
Friedman,
PhD
Principal
Technologist,
MapR
Technologies
CommiBer
Apache
Drill
&
Apache
Mahout
projects
O’Reilly
author
Email
efriedman@mapr.com
ellenf@apache.org
TwiBer
@Ellen_Friedman
Contact
InformaBon

3. 3 © 2018 MapR Technologies, Inc.
.
Images
©
Friedman
&
Dunning
Image
courtesy
Mtell
used
with
permission
Big
Value
from
Big
Data
in
ProducBon
across
Diverse
Businesses
TelecommunicaQons
Smart-­‐Metered
UQliQes
Heavy
Industry
Financial
TransportaQon
Agritech

4. 4 © 2018 MapR Technologies, Inc.
“The
future
is
already
here
–
it’s
just
not
evenly
distributed.”
-­‐
William
Gibson

5. 5 © 2018 MapR Technologies, Inc.
.
Some
organizaQons
more
successful
at
geWng
large
scale
systems
into
producQon
• 2018
Gartner
report
stated
only
17%
of
Hadoop-­‐based
systems
were
in
producQon
vs
• Over
90%
of
MapR
customers
have
large
scale
producQon
systems
Why?
Value
from
Data-­‐Intensive
ApplicaBons
in
ProducBon

6. 6 © 2018 MapR Technologies, Inc.
1
Data
may
be
in
production
long
before
code
is
complete

7. 7 © 2018 MapR Technologies, Inc.
.
Labs
in
Canada
froze
blood
samples
for
years
in
case
they
contain
valuable
informaQon.
• Modern
geneQc
techniques
revealed
key
disease
data
• Correlated
with
outcomes
for
the
donor
paQents
• The
data
was
preserved
before
the
analysis
was
even
Real
World
Example:
Biological
Samples
Contain
Data
Image
©
2003
Ellen
Friedman

8. 8 © 2018 MapR Technologies, Inc.
Are
you
asking
the
right
quesBon?
tnow
Hourly
clicks at
A B C
Which
markeQng
email
is
most
effecQve?
• Looks
like
C
–
Blue
is
best
when
you
base
the
quesQon
on
data
for
highest
hourly
response
rate
at
a
current
point
in
Qme
tnow
• But
this
is
misleading.
Why?

9. 9 © 2018 MapR Technologies, Inc.
BeRer
way
to
frame
the
quesBon:
Which
markeQng
email
is
most
effecQve
based
on
response
rate
at
Qme
t
ader
launch?
• Collect
and
retain
data
across
same
Qme
interval
relaQve
to
launch
• Looks
like
B
–
Green
is
best
performer
and
C-­‐
Blue
is
least
effecQve
for
response
rate
Click
rate tnow
t
Click
rate
Time after launch
Launch Measure
A
B
C
Measuring performance at a
constant time after launch gives
consistent comparison

10. 10 © 2018 MapR Technologies, Inc.
BeRer
way
to
frame
the
quesBon:
Which
markeQng
email
is
most
effecQve
based
on
response
rate
at
Qme
t
ader
launch?
• Collect
and
retain
data
across
same
Qme
interval
relaQve
to
launch
• Looks
like
B
–
Green
is
best
performer
and
C-­‐
Blue
is
least
effecQve
for
response
rate
Click
rate tnow
t
Click
rate
Time after launch
Launch Measure
A
B
C
But that’s hard after we have over-
written the old data with current data

11. 11 © 2018 MapR Technologies, Inc.
Spot
the
Difference?
GET photo.jpg HTTP/1.1
Host: lh4.googleusercontent
User-agent: Mozilla/5.0 (Ma
Accept: image/png,image/*
Accept-language: en-US,en
Accept-encoding: gzip, defl
Referer: https://www.google
Connection: keep-alive
If-none-match: "v9”
Cache-control: max-age=0
GET cc/borken.json HTTP/1.1
host: c.qrs.my
user-agent: Mozilla/4.0 (co
accept: application/json, t
accept-language: en-US,en
accept-encoding: gzip, defl
referer: none
connection: keep-alive
if-none-match: "v9”
cache-control: max-age=0
Attacker requestReal request

12. 12 © 2018 MapR Technologies, Inc.
Spot
the
Difference?
GET photo.jpg HTTP/1.1
Host: lh4.googleusercontent
User-agent: Mozilla/5.0 (Ma
Accept: image/png,image/*
Accept-language: en-US,en
Accept-encoding: gzip, defl
Referer: https://www.google
Connection: keep-alive
If-none-match: "v9”
Cache-control: max-age=0
GET cc/borken.json HTTP/1.1
host: c.qrs.my
user-agent: Mozilla/4.0 (co
accept: application/json, t
accept-language: en-US,en
accept-encoding: gzip, defl
referer: none
connection: keep-alive
if-none-match: "v9”
cache-control: max-age=0
Attacker requestReal request

13. 13 © 2018 MapR Technologies, Inc.
Security
expert
at
a
bank
preserved
headers
for
web
site
requests
Detected
anomaly
in
headers
for
the
aBackers
vs
normal
(real)
requests
But
how
would
you
know
what
data
to
preserve?
• PaBern
of
behavior
for
aBackers
was
allowable
for
headers
• It
was
not
predictable:
but
it
was
different
Domain
Knowledge
MaRers:
DetecBng
Security
ARacks

14. 14 © 2018 MapR Technologies, Inc.
2
End-­‐to-­‐end
design
for
production

15. 15 © 2018 MapR Technologies, Inc.
Do
you
have
a
good
fit
between
applicaQon,
its
SLAs
and
pracQcal
business
goal?
Is
there
a
way
to
take
ac2on
on
the
output
of
the
applicaQon?
! Note:
a
report
is
not
an
acQon
Do
you
have
real
advance
planning
for
producQon?
! Note:
slapping
an
SLA
onto
a
complex
applicaQon
&
tossing
it
to
IT
with
hazily
defined
criQcal
tasks
is
not
producQon
planning
The
missing
bits
usually
aren’t
in
the
applicaBon
itself

16. 16 © 2018 MapR Technologies, Inc.
.
Remember:
IT
doesn’t
have
a
magic
wand…

17. 17 © 2018 MapR Technologies, Inc.
.
Remember:
IT
doesn’t
have
a
magic
wand…

18. 18 © 2018 MapR Technologies, Inc.
Build
a
Data
Fabric
Flexibility
&
agility
to
respond
as
life
changes

19. 19 © 2018 MapR Technologies, Inc.
A
Global
Data
Fabric:
Edge
to
On-­‐Premises
to
Cloud
Data
where
you
want
it,
compute
power
where
you
need
it.

20. 20 © 2018 MapR Technologies, Inc.
.
Comprehensive
View
of
Data
vs
Isolated
Services
Commonality
IsolaQon
Both
are
needed,
for
different
purposes.
Best
pracQce
achieves
a
balance.

21. 21 © 2018 MapR Technologies, Inc.
3
Orchestration
of
applications
and
orchestration
of
data

22. 22 © 2018 MapR Technologies, Inc.
Containerized
applicaQons
run
in
different
environments
on
same
cluster
at
same
Qme
Kubernetes
is
emerging
as
the
leader
in
orchestraQon
of
containerized
applicaQons
• You
specify
what
needs
to
be
done,
Kubernetes
arranges
it
by
running
containers
• Kubernetes
allows
access
to
services
by
name
ContainerizaBon:
flexibility,
convenience,
predictability

23. 23 © 2018 MapR Technologies, Inc.
CNCF
reported
• nearly
70%
of
organizaQons
surveyed
use
Kubernetes
to
manage
containers
• but
the
#1
issue
for
Kubernetes
users
is
storage
You
don’t
want
to
store
state
in
containers
–
defeats
flexibility
Biggest
Challenge
with
Kubernetes
is
Data
Persistence

24. 24 © 2018 MapR Technologies, Inc.
CNCF
reported
• nearly
70%
of
organizaQons
surveyed
use
Kubernetes
to
manage
containers
• but
the
#1
issue
for
Kubernetes
users
is
storage
You
don’t
want
to
store
state
in
containers
–
defeats
flexibility
How
do
you
get
the
benefits
of
containeriza2on
without
being
limited
to
stateless
applica2ons?
Biggest
Challenge
with
Kubernetes
is
Data
Persistence

25. 25 © 2018 MapR Technologies, Inc.
App 1 App 2 App 3
Kubernetes

26. 26 © 2018 MapR Technologies, Inc.
App 1 App 2 App 3
Kubernetes
rpc
stream
LogFile

27. 27 © 2018 MapR Technologies, Inc.
App 1 App 2 App 3
Kubernetes
rpc
stream
LogFile
Data Platform

28. 28 © 2018 MapR Technologies, Inc.
Kubernetes
for
orchestraQon
of
applicaQons
+
Dataware
for
orchestraQon
of
data
A
Powerful
CombinaBon

29. 29 © 2018 MapR Technologies, Inc.
4
Simplicity
is
golden.
You
should
not
need
an
army
to
administer
a
cluster.

30. 30 © 2018 MapR Technologies, Inc.
.
If
your
system
has
lots
of
work-­‐arounds,
think
again…

31. 31 © 2018 MapR Technologies, Inc.
.
If
your
system
has
lots
of
work-­‐arounds,
think
again…

32. 32 © 2018 MapR Technologies, Inc.
Much
of
logisQcs
and
many
processes
should
be
handled
by
the
plaoorm,
not
by
developers
at
the
applicaQon
level
• SeparaQon
of
concerns
for
system
administrators
vs
developers/data
scienQsts
• More
efficient,
less
risk
of
error
• Ease
of
administraQon
Data
Pla[orm
Should
Simplify

33. 33 © 2018 MapR Technologies, Inc.
Orchestrate
Data
with
Dataware
Legacy
ApplicaBons
Big
Data
1.0
ApplicaBons
Next-­‐Gen
ApplicaBons
MapR
Converged
Data
Plaoorm
High
Availability
Real
Time
Unified
Security
MulB-­‐tenancy
Disaster
Recovery
Global
Namespace
Real-­‐Time
NoQL
Database
Stream
Transport
Web-­‐Scale
Storage
MapR
is
more
than
just
data
storage
–
it’s
like
Kubernetes
for
data.
MapR
is
dataware.

34. 34 © 2018 MapR Technologies, Inc.
MapR
Volume:
Directory
with
Special
Management
CapabiliBes
Cluster
Volume mount point
Directories
Files
Streams
Table
Volumes
used
for
easy
control
of
access,
mulQtenancy,
data
locality
&
DR

35. 35 © 2018 MapR Technologies, Inc.
MapR
can
run
huge
numbers
of
applicaQons
on
same
cluster
• Great
performance,
opQmized
resources
• Comprehensive
view
of
data
&
beBer
collaboraQon
• Ease
of
administraQon
Is
your
system
easy
to
administer?
We’ve
seen
a
large
retail
customer
manage
a
cluster
of
>1000
nodes
with
just
3
administrators

36. 36 © 2018 MapR Technologies, Inc.
Open
APIs
Multicloud & Hybrid Cloud Strategy with MapR:
Application
• Unified
Security
Model
• Data
access
decoupled
from
physical
storage
location.
Globally.
• No
lock-­‐in
to
proprietary
APIs
• Full
openness
• Data
made
portable
API
Connector
✓
GLOBAL
DATA
MANAGEMENT
Edge
Private
Cloud
On
Premise
Public
Cloud
Public
Cloud
Public
Cloud
Uniform computing environment
everywhere

37. 37 © 2018 MapR Technologies, Inc.
5Build
real
multitenancy

38. 38 © 2018 MapR Technologies, Inc.
MapR
Volumes:
Easy
Data
Management
• MulQple
volumes
span
a
cluster
• Files,
tables,
streams
in
same
volume
• Fine-­‐grained
control
of
who
has
access
• Basis
for
mirroring,
snapshots
• Great
advantage
for
mulQtenancy

39. 39 © 2018 MapR Technologies, Inc.
MapR
Volumes:
Control
Data
Locality
• Place
data
on
specialized
hardware
(such
as
GPUs)
• Meet
compliance
requirements
• BeBer
opQmizaQon
of
resources
• Great
advantage
for
AI/
machine
learning

40. 40 © 2018 MapR Technologies, Inc.
A
simpler
system
is
more
cost
effecQve,
with
beBer
performance
• Remember:
Every
HBase
commit
requires
a
round
trip
to
the
namenode
if
run
on
HDFS
MapR
has
no
namenode
–
avoids
the
problem
• It
isn’t
magic.
It’s
just
real
mulQtenancy.
Does
your
system
avoid
unnecessary
sprawl?
We’ve
seen
a
customer
collapse
17
HBase
clusters
in
AWS
to
1
cluster
running
five
nodes
HBase
on
MapR
with
great
performance

41. 41 © 2018 MapR Technologies, Inc.
6
Streaming
architecture
provides
flexibility

42. 42 © 2018 MapR Technologies, Inc.
Stream
Transport
to
Decouple
Producers
&
Consumers
P
P
P
C
C
C
Transport Processing
Kafka /
MapR Streams
“Streaming
Microservices”
by
Ted
Dunning
&
Ellen
Friedman,
in
Encyclopedia
of
Big
Data
Technologies,
Sherif
Sakr
and
Albert
Zomaya,
editors,
©
2018
(Springer
InternaQonal
Publishing)
ebook
Streaming
Architecture
by
Ted
Dunning
&
Ellen
Friedman
©
2016
(
O’Reilly
Media),
chapter
3:
hBps://mapr.com/ebooks/streaming-­‐architecture/chapter-­‐03-­‐streaming-­‐plaoorm-­‐for-­‐microservices.html

43. 43 © 2018 MapR Technologies, Inc.
Stream-­‐first
Architecture
Supports
Microservices
Real-time
analytics
EMR
Patient Facilities
management
Insurance
audit
A
B
Medical tests
C
Medical test
results
With
the
right
messaging
tool
you
support
mulQple
classes
of
use
cases

44. 44 © 2018 MapR Technologies, Inc.
ResultsRendezvous
Rendezvous
Architecture
is
based
on
streaming
microservices
Scores
ArchiveDecoy
m1
m2
m3
Features /
profiles
InputRaw
Rendezvous Architecture described in:
- Machine Learning Logistics book by Ted Dunning & Ellen Friedman, 2018 (O’Reilly)
-­‐ “Rendezvous
Architecture”
by
Ted
Dunning
&
Ellen
Friedman,
chapter
in
Encyclopedia
of
Big
Data
Technologies.
Sherif
Sakr
and
Albert
Zomaya,
editors.
Springer
InternaQonal
Publishing,
2018.

45. 45 © 2018 MapR Technologies, Inc.
7Build
a
data-­‐aware
culture.
That
also
gives
you
a
production-­‐ready
culture.

46. 46 © 2018 MapR Technologies, Inc.
DataOps:
Brings
Flexibility
&
Focus
• Cross-functional teams cut
across skill guilds
• Better communication &
shared goal keep effort
focused and flexible
Refs:
“DevOps, DataOps & Machine Learning”
by Terry McCann
http://www.hyperbi.co.uk/devops-dataops-
and-machine-learning/
“How to Manage a DataOps Team” by E.
Friedman in RTInsights
https://www.rtinsights.com/how-manage-
dataops-team/
Developer
Data
Scientist /
Data
Engineer
Ops NetworkSite
Reliability
QA
Security
Project 1
Project 2
Project 3
Kubernetes
Data platform
Storage
Required team skill
Occasional help

47. 47 © 2018 MapR Technologies, Inc.
AI
&
Analy)cs
in
Produc)on:
How
to
Make
It
Work
By
Ted
Dunning
&
Ellen
Friedman
Download
FREE
pdf
of
this
book
courtesy
MapR:
https://mapr.com/ebook/ai-and-analytics-in-production/

48. 48 © 2018 MapR Technologies, Inc.
Please support women in tech – help build
girls’ dreams of what they can accomplish
© Ellen Friedman 2015#womenintech #datawomen

49. 49 © 2018 MapR Technologies, Inc.
Thank
you
!

50. 50 © 2018 MapR Technologies, Inc.
Ellen
Friedman,
PhD
Principal
Technologist,
MapR
Technologies
CommiBer
Apache
Drill
&
Apache
Mahout
projects
O’Reilly
author
Email
efriedman@mapr.com
ellenf@apache.org
TwiBer
@Ellen_Friedman
Contact
InformaBon