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Scott Donaldson – Senior Director, FINRA

Vincent Saulys – Senior Director, FINRA

November 2016

BDM203

FINRABuilding a Secure Data Science Platform on AWS

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DATA SCIENCE NEEDS

• Data discovery & exploration

• Bring disparate sources of data together

• Semantic understanding of the data sets

• Ease of use: enable users without having to understand underlying data

infrastructure

• Safeguard information with high degree of security and least privileges access

• Model migration from research to prototype to production

• Avoid time spent on environment administration

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SEPARATE INFRASTRUCTURE SERVICES

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SCALE THE DATA PLANT

Considerations

• Scale compute and storage separately.

• Resiliency and disaster recovery

• Flexibility of instance types

• Data discovery through an enterprise data catalog

Security

• Virtual private cloud (VPC) & encryption

• Separation of duties

• DevOps: Automate everything

• Least privileges and no catch-all rules

• Centralized monitoring for total transparency

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SCALE THE DATA PLANT

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CENTRALIZED DATA MANAGEMENT

http://finraos.github.io/herd

Unified catalog

• Schemas

• Versions

• Encryption type

• Storage policies

Lineage and Usage

• Track publishers & consumers

• Easily identify jobs and derived data sets

Shared Metastore

• Common definition of tables & partitions

• Use with Spark, Presto, Hive, etc.

• Faster instantiation of clusters

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EFFECTS OF CLOUD CHANGE

• Gold source of all the data in S3

• Separated data and compute

• Easily spin up compute with unlimited query engine

capacity

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REMAINING PAIN POINTS

• Data scientists still relied on SQL to query the data

• Data science continued to be done on local machines

• No standard setup

• Everyone administered their own machines

• The data was too big for local machines

• More people doing advanced analytics

• Easy collaboration was still not addressed

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DATA SCIENCE TOOLING: BEFORE UDSP

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SOLUTION: UNIVERSAL DATA SCIENCE

PLATFORM

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UDSP V1Secure

• Technology controls and curates content

Self-Service

• Users manage their machines

Scalable Compute

• Size machines to your needs

Turnkey

• Libraries pre-built and installed

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NO USERS, WHY?

Needs driven by technology

• IT: Reduce costs

• Users: need more compute

Secure but inflexible

• Local machines where more flexible

• Install any package and experiment

Data availability

• On-premises databases not reachable

Setup still required

• Driver configuration to connect to databases

Technology in the way

• Technology required to install any new package

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UDSP V2Flexible

• Download/Install any package

Data Availability

• No additional setup necessary

• On-premises and cloud data accessible

Ownership

• Changes proposed and vetted through

the data science forum

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ADOPTION METRICS

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INVENTORY

R 3.2.5, Python (2.7.12 and 3.4.3)

Packages

• R: 300+ Python: 100+

Tools for Building Packages

• gcc, gfortran, make, java, maven,

ant…

IDEs

• Jupyter, RStudio Server

Deep Learning

• CUDA, CuDNN (if GPU present)

• Theano, Caffe, Torch

• TensorFlow

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SELF SERVICE

Completely self service, no technology administration

• Users select UDSP version and machine capacity

Users associated to groups (AWS billing tags and machine selection choices)

Users manage their instances

• Create, Stop, Terminate (delete)

Managers can administer their team’s instances

Dashboard to monitor resource usage

• Stop instances from the dashboard

Reports for historical usage

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USDP: CREATE AND LAUNCH

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UDSP: MONITOR RUNNING INSTANCES

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UDSP: USE TOOLS WITH BROWSER

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MAINTAINING THE USDP

Community Driven Experimentation

• Data scientists can install any package to try it out

• No technologist necessary to administer installation

New library (or version) is proposed for next release

• Releases have been monthly

• Envision quarterly releases

Philosophy: Support last major release (most recent

patch)

• R 3.3.1 is available and still releasing patches, UDSP

has 3.2.5

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THE ROAD AHEAD

Clusters for Advanced Analytics

Surveillance Platform

• Facilitate surveillance development on spark

• Data Framework for accessing and manipulating data

• ML Framework standardizes algorithms, diagnostics and

best practices

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SURVEILLANCE PLATFORM

Spark as the processing platform

Cluster based data processing cluster based data science

Frameworks will speed data engineering and data science

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RECAP

• Each improvement brought pressures to legacy ways of working

• Flexibility of platform key to adoption

• Groups do what they are best at (administer setups, do analytics)

• Technology get out of the way!

• Full visibility to administer costs

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Other FINRA Sessions:

• BDM203 – Building a Secure Data Science Platform

• DAT302 – Best Practices for Migrating to RDS / Aurora

• ENT313 – FINRA in the Cloud, Big Data Enterprise

• CMP316 – Aligning Billions of Time Ordered Events with Spark

• STG308 – Analytics Without Limits. FINRA’s Scalable Big Data Architecture on S3

RELATED SESSIONS

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ABOUT THE PRESENTERS

Scott Donaldson

• Senior Director, FINRA

• Data Analytics and Surveillance Systems

• Scott.Donaldson@finra.org

• https://www.linkedin.com/in/scottdonaldson

Vincent Saulys

• Senior Director, FINRA

• Advanced Surveillance Development

• Vincent.Saulys@finra.org

• www.linkedin.com/in/vincentsaulys

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QUESTIONS?

Learn more at

http://technology.finra.org

FINRA Technology is hiring

http://technology.finra.org/careers.html

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Thank you!