The data-driven innovations of data scientists can be game-changing. But the siloed efforts and custom-crafted prototypes of individual data scientists can be difficult to scale, reproduce, and share across multiple users. What works for an ad-hoc model in development may not work in production; what works as a one-off prototype on a laptop might not work as a consistent and repeatable process for large-scale distributed data science. What’s needed is an approach that brings the agility, automation, and collaboration of DevOps to your data science teams. In this presentation at our Big-Data-as-a-Service (BDaaS) meetup in Santa Clara, Nanda Vijaydev (data scientist and director of solutions management at BlueData) discussed how to bring DevOps agility to large-scale distributed data science operations — with Big-Data-as-a-Service, powered by Docker containers. Nanda’s presentation covered how to: • Create on-demand environments with your users’ preferred data science tools (e.g. Spark, Zeppelin, Jupyter, RStudio) • Deploy environments once and run them anywhere – on-premises or in the public cloud – using Docker • Improve agility, collaboration, and productivity for your data science and engineering teams More info on this BDaaS meetup can be found at: https://www.meetup.com/Big-Data-as-a-Service/events/239936007

1. Twi$er: @BDaaSmeetup
Hashtag: #BDaaS

2. Our Sponsor
Big-Data-as-a-Service.
On-Prem, Cloud, or Hybrid. It’s BDaaS.

3. BDaaS Meetup
• Welcome and IntroducCons
• PresentaCon by Nanda Vijaydev
— Distributed Data Science, DevOps, and Docker
• Q&A and Discussion
Twi$er: @BDaaSmeetup
Hashtag: #BDaaS

4. Nanda Vijaydev
• Data scienCst and director of soluCons at BlueData
• Prior to BlueData, was a soluCons architect at Silicon Valley
Data Science
• More than 10 years experience in data management and data
science
• Has worked with dozens of organizaCons to deploy Hadoop,
Spark, & data science environments using Docker containers

5. with
Distributed Data Science
DevOps, and Docker
BDaaS Meetup
June 9, 2017
Nanda Vijaydev
@NandaVijaydev

6. Outline
• Evolu>on of Data Science Opera>ons
• Distributed Data Science on Docker
• Challenges and Key Requirements
• Demo
• Key Takeaways
• Q & A

7. Understand
Business Problem
Acquire/Collect
Analyze/Model Reflect/Evaluate
Deploy/
Disseminate
A pre$y picture
(ideal workflow)
A not so pre$y picture
(workflow in reality)
Data Science Tasks and Roles
Data
Engineer / Data
Scientist
Core Data
Scientist
Statisticians / Data
Scientists
Data AnalystData Analyst
Data
Engineer / Data
Scientist

8. Which do you
prefer to use:
SAS, R, or
Python?
Source: “SAS, R, or Python Survey 2016: Which Tool Do Analy>cs Pros Prefer?”, Burtch Works, July 2016
Preferred Language of Analytics Pros

9. Evolution of Data Science Operations
Sampling
Modeling &
Tuning
Reports
(e.g. credit
card offer)
TradiConal Data Science & AnalyCcs
Distributed
Systems
Acquire
Data
Model
Tune
Deploy
Distributed Data Science & AnalyCcs

10. What Often Happens …
Faulty AssumpCons
• IT team thinks they understand
requirements and use cases
• Assumes infrastructure & systems
will work for most use cases
• Assumes all data scien>sts will use
similar toolsets
• Build the infrastructure first, then
onboard the data scien>sts …

11. A More Realistic Journey
Onboarding data scien>sts
Con>nuous infrastructure provisioning
Base-R, SQL,
Python, Java
Established use
cases
Need to analyze
higher data
volumes
SparkR, PySpark,
spark-sql, H2O,
Zeppelin
Numpy,
Scipy, NLTK,
JupyterHub,
with Spark
AddiConal
modules for
Python users
R user base is
adopCng more
Big Data
R-Studio,
Shiny Server
with Spark +
H2O
Use cases, requirements, & tools
will con>nue to evolve over >me

12. DevOps for Data Science Operations
Source: Rob Nendorf, Allstate, “DevOps for Data Science”

13. What Data Science Teams Need:
• Access to data with full fidelity
• Ability to quickly iterate & validate findings
• Access to necessary tools and models
• Ability to scale environments on-demand
• Ability to share models and code
• Ability to deploy and integrate the solu>on

14. Data Science – Usage Scenarios
1. End-to-end analysis on local laptops /
worksta>ons using RStudio, Jupyter
2. Preprocess on Hadoop/Spark, download
and analyze locally using RStudio/Jupyter
3. Preprocess and analyze on Hadoop/Spark

15. Single node laptops / worksta>ons:
• Using single node instances with more resources
• Projects like ff, bigmemory for R
Distributed processing:
• SparkR/sparklyr with RStudio and Spark cluster
• Jupyter/Zeppelin notebook with PySpark and Spark cluster
• Hadoop clusters
• Sandbox that can be scaled on demand
Scaling Options for Data Science

16. Accessing Aggregate Data from HDFS
• Preprocessed or par>>oned data can be stored in HDFS
• Can be accessed directly from RStudio/Jupyter using RHadoop client for
aggrega>on/modeling

17. Distributed Data Science
on Docker
with

18. R Environment with RStudio Server

19. • Install local Spark if
not already available
• Connect to Spark
cluster
• Set appropriate
Spark configura>ons
for op>mal
performance
Spark with sparklyr from RStudio

20. Python Environment with Jupyter

21. • Users work in their
familiar notebooks
• BlueData provisions
mul>-node Spark
clusters
PySpark with Anaconda from Jupyter

22. Environments: Scale Up vs. Scale Out
R Packages,
Python, SQL
UI /
Notebooks
Scale Up
Frameworks
Compute
Data
Local Compute
(Laptops)
Spark (SQL, Scala, Python, Java, MLlib) + H2O
Spark (SQL, SparkR,
Scala, Java, MLlib) +
H2O
Scale Out
RStudio + R + Spark
+ sparklyr
Jupyter + Python +
Spark
Zeppelin + R +
Python + Spark
Spark
R
Spark
R
Spark
R
Spark
R
Spark
Spark
Spark
Spark
Spark
Spark
Spark
Spark

23. Scalable Data Science: Challenges
• How do you keep up with the constant evoluCon of new
versions and tools?
– The data science ecosystem is evolving very quickly (e.g. rapid pace of new Spark versions)
– Related tools (e.g. RStudio, Jupyter, Zeppelin) have to keep pace to support new features
– New versions of Spark and other tools require different versions of libraries and packages
• One monolothic cluster won’t cut it … how do you support
the variaCons?
– Different use cases & users need different op>ons, versions, packages
– Workloads change … adding new packages or scaling clusters up and down is cumbersome

24. Scalable Data Science: Challenges
• How do you make it easy for your data scienCsts to get what
they need?
– Data scien>sts are comfortable with their desktop tools, not distributed compu>ng
– They need on-demand environments with instant access to their preferred tools and data
• How do you manage user access for IDEs / notebooks and
data sources?
– Given the different layers of the stack, this can be complex and challenging for enterprises
• And more … repeatability, elasCcity, scalability, security,
performance ...

25. IOBoost™ - Extreme performance and scalability
Elas>cPlane™ - Self-service, mul>-tenant clusters
DataTap™ - In-place access to data on-prem or in the cloud
Blue Data EPIC™ Soaware Plaborm
Data Scien>sts Developers Data Engineers
Data Analysts
BI/Analy>cs Tools
NFS HDFS
Platform for Scalable Data Science
Compute
Storage
On-Premises Public Cloud
EC2
S3
Bring-Your-Own

26. Multi-Tenant Environments
Distributed clusters with
Jupyter & Zeppelin
notebooks
Links to available services
and notebooks

27. Pre-Integrated Docker-Based Images
DOCKER-BASED
IMAGES OF
YOUR CHOICE:
SAME FOR ON-
PREM, AWS, OR
ANY CLOUD

28. On-Demand Spark + R Environments
Just a few mouse
clicks to a fully
configured
cluster (e.g. with
Spark + RStudio
Server)

29. Scalable Data Science with R & Python
Deploy on-demand Spark clusters with
RStudio (sparklyr), Zeppelin, or Jupyter

30. Spark (via Zeppelin Notebook)
Turnkey Spark clusters on Docker,
with Zeppelin, Jupyter, and SparkR
pre-integrated

31. Scale to Production (Compute + Data)
Compute: Add worker nodes
Data: Point analy>cs to storage (HDFS, S3, NFS)

32. Distributed Data Science Operations
Data Scien>sts
Spark 2.0 +
Jupyter
Notebook
Spark 1.6.1
+ Zeppelin
Notebook
JupyterHub RStudio
BRING
YOUR OWN
TensorFlow
Hadoop
(Hive, M/R)
Datameer
Launch Launch Launch Launch
Launch Launch Launch Launch
Shared Data, Code, and Results
Users & Security
Orchestra>on & Mgmt
Data Analysts Data Engineers
Comprehensive management of secure, scalable, & reproducible data science environments
ON-PREMISES CLOUD

33. DEMO

34. Distributed Data Science: Takeaways
• Opera>onalizing distributed data science is hard work
– Unique requirements for access to data, models, tools, etc.
• Need to bring a DevOps approach to data science opera>ons
– Support for fast, itera>ve prototyping and reproducibility
– Requires ul>mate flexibility as tools evolve and new op>ons emerge
• Leverage a turnkey purpose-built plalorm (e.g. BlueData EPIC)
– Bring DevOps agility to distributed data science, powered by Docker
– Provide ability to share code, models, & data with secure mul>-tenancy
– Enable on-demand environments with a choice of data science tools

35. Thank You
TRY BLUEDATA EPIC ON AWS
For more informa>on:
www.bluedata.com
sales@bluedata.com
www.bluedata.com/aws

36. Q&A
www.bluedata.com

37. Wrap-Up
• We’ll share the slides and video recording
• SuggesCons for future meetups?
• Next meeCng TBD – we’ll keep you posted

38. Thank you
for a$ending!
Thanks to our sponsor:
www.bluedata.com
Twi$er: @BDaaSmeetup
Hashtag: #BDaaS