Date: 14th November 2018 Location: Keynote Theatre Time: 11:50 - 12:20 Speaker: Ellen Friedman Organisation: MapR About: We’ve seen that over 90% of our customers have large scale projects successfully in production. What are they doing right? And how can you adapt their effective habits to your own business? Value comes from big data when you have successful production deployments of data-intensive AI and analytics applications tied to practical business goals. Doing this well can be difficult on many levels. Each business presents its own challenges, but we’ve observed a number of habits that are common to many of the organizations who are getting value from their production deployments. This presentation will explore 7 key habits that can make a difference and use real world examples to show you why. From architecture to technology to organizational culture, you’ll learn practical approaches that can improve your likelihood of success in production.

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

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Ellen Friedman, PhD
Principal Technologist, MapR Technologies
Committer Apache Drill & Apache Mahout projects
O’Reilly author
Email efriedman@mapr.com ellenf@apache.org
Twitter @Ellen_Friedman
Contact Information

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Images © Friedman & Dunning
Image courtesy Mtell used with permission
Big Value from Big Data in Production across Diverse Businesses
Telecommunications
Smart-Metered Utilities
Heavy Industry
Financial
Transportation
Agritech

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“The future is already here – it’s just
not evenly distributed.”
- William
Gibson

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Some organizations more successful at getting large scale systems into production
• 2018 Gartner report stated only 17% of Hadoop-based systems were in production
vs
• Over 90% of our customers have large scale production systems
Why?
Value from Data-Intensive Applications in Production
Image © 2003 Ellen Friedman

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1Data may be in production long before
code is complete

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Labs in Canada froze blood samples for years in case they contain valuable information.
• Modern genetic techniques revealed key disease data
• Correlated with outcomes for the donor patients
• The data was preserved before the analysis was even
Real World Example: Biological Samples Contain Data
Image © 2003 Ellen Friedman

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Are you asking the right question?
tnow
Hourly
clicks at
A B C
Which marketing email is most effective?
• Looks like C – Blue is best when you base the
question on data for highest hourly response
rate at a current point in time tnow
• But this is misleading. Why?

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Better way to frame the question:
Which marketing email is most effective based
on response rate at time t after launch?
• Collect and retain data across same time
interval relative to launch
• Looks like B – Green is best performer and
C- Blue is least effective 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

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Better way to frame the question:
Which marketing email is most effective based
on response rate at time t after launch?
• Collect and retain data across same time
interval relative to launch
• Looks like B – Green is best performer and
C- Blue is least effective 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

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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

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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

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Security expert at a bank preserved headers for web site requests
Detected anomaly in headers for the attackers vs normal (real) requests
But how would you know what data to preserve?
• Pattern of behavior for attackers was allowable for headers
• It was not predictable: but it was different
Domain Knowledge Matters: Detecting Security Attacks

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2End-to-end design for production

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Do you have a good fit between application, its SLAs and practical business goal?
Is there a way to take action on the output of the application?
§ Note: a report is not an action
Do you have real advance planning for production?
§ Note: slapping an SLA onto a complex application & tossing it to IT with hazily
defined critical tasks is not production planning
The missing bits usually aren’t in the application itself

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Remember: IT doesn’t have a magic wand…

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Remember: IT doesn’t have a magic wand…

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Build a Data Fabric
Flexibility & agility to respond as life changes

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A Global Data Fabric: Edge to On-Premises to Cloud
Data where you want it, compute power where you need it.

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Comprehensive View of Data vs Isolated Services
Commonality
Isolation
Both are needed, for different purposes. Best practice achieves a balance.

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3Orchestration of applications and
orchestration of data

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Containerized applications run in different environments on same cluster at same time
Kubernetes is emerging as the leader in orchestration of containerized applications
• You specify what needs to be done, Kubernetes arranges it by running containers
• Kubernetes allows access to services by name
Containerization: flexibility, convenience, predictability

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CNCF reported
• nearly 70% of organizations surveyed use Kubernetes to manage containers
• but the #1 issue Kubernetes users have is storage
You don’t want to store state in containers – defeats flexibility
Biggest Challenge with Kubernetes is Data Persistence

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CNCF reported
• nearly 70% of organizations surveyed use Kubernetes to manage containers
• but the #1 issue Kubernetes users have is storage
You don’t want to store state in containers – defeats flexibility
How do you get the benefits of containerization without being limited to
stateless applications?
Biggest Challenge with Kubernetes is Data Persistence

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App 1 App 2 App 3
Kubernetes

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App 1 App 2 App 3
Kubernetes
rpc
stream
LogFile

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App 1 App 2 App 3
Kubernetes
rpc
stream
LogFile
Data Platform

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Kubernetes for orchestration of applications
+
Dataware for orchestration of data
A Powerful Combination

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4Simplicity is golden
You should not need an army to administer
a cluster

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If your system has lots of work-arounds, think again…

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If your system has lots of work-arounds, think again…

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Many logistics and processes should be handled by the platform, not by developers at
the application level
• Separation of concerns for system administrators vs developers/data scientists
• More efficient, less risk of error
• Ease of administration
Data Platform Should Simplify

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Orchestrate Data with Dataware
Legacy Applications Big Data 1.0 Applications Next-Gen Applications
MapR Converged Data Platform
High Availability Real Time Unified Security Multi-tenancy Disaster Recovery Global Namespace
Real-Time NoQL Database Stream TransportWeb-Scale Storage
MapR is more than just data storage – it’s like Kubernetes for data.
It’s dataware.

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MapR Volume: Directory with Special Management Capabilities
Cluster
Volume mount point
Directories
Files
Streams
Table
Volumes used for easy control of access, multitenancy, data locality & DR

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MapR can run huge numbers of applications on same cluster
• Great performance, optimized resources
• Comprehensive view of data & better collaboration
• Ease of administration
Is your system easy to administer?
We’ve seen a large retail customer manage a cluster >1000 nodes with
just 3 administrators

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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

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5Build real multitenancy

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MapR Volumes: Easy Data Management
• Multiple volumes span a cluster
• Files, tables, streams in same volume
• Fine-grained control of who has access
• Basis for mirroring, snapshots
• Great advantage for multi-tenancy

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MapR Volumes: Control Data Locality
• Place data on specialized hardware
(such as GPUs)
• Meet compliance requirements
• Better optimization of resources
• Great advantage for AI/ machine
learning

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A simpler system is more cost effective, with better 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 multitenancy.
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

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6Streaming architecture provides
flexibility

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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 International Publishing)
ebook Streaming Architecture by Ted Dunning & Ellen Friedman © 2016 ( O’Reilly Media), chapter 3:
https://mapr.com/ebooks/streaming-architecture/chapter-03-streaming-platform-for-microservices.html

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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 multiple classes of use cases

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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 International Publishing, © 2018 in press.

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7Build a data-aware culture.
That also gives you a production-ready culture.

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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

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AI & Analytics in Production:
How to Make It Work
Free book signing with authors Ted Dunning & Ellen Friedman
MapR stand #145
Wed 1:00 pm – 1:45 pm
Download free pdf via MapR:
https://mapr.com/ebook/ai-and-analytics-in-production/

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Please support women in tech – help build
girls’ dreams of what they can accomplish
© Ellen Friedman 2015#womenintech #datawomen

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Thank you !

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