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Cloudera Data Engineering and Data

Science

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We are in the age of machine learning

Data has never been

more plentiful

Open source data science and

machine learning libraries are

rapidly evolving

Flexible commodity storage and

compute make scalable

production machine learning

affordable

Data Analytics Deployment

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But there are practical challenges

Most data science done at

small scale, individually,

and is difficult to replicate

Very few models

reach production

Teams have different,

conflicting requests for

languages & libraries

Native formats and

security can make data

access challenging

Data Analytics Deployment

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Open data science in the enterprise

ITdrive adoption while maintaining compliance

Data Scientistexplore, experiment, iterate

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• Team: Data scientists and analysts• Goal: Understand data, develop and improve models,

share insights

• Data: New and changing; often sampled• Environment: Local machine, sandbox cluster• Tools: R, Python, SAS/SPSS, SQL; notebooks; data

wrangling/discovery tools, …• End State: Reports, dashboards, PDF, MS Office

• Team: Data engineers, developers, SREs• Goal: Build and maintain applications, improve

model performance, manage models in production

• Data: Known data; full scale• Environment: Production clusters• Tools: Java/Scala, C++; IDEs; continuous

integration, source control, …• End State: Online/production applications

Types of data science

Exploratory(discover and quantify opportunities)

Operational(deploy production systems)

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Typical data science workflow

Data Engineering Data Science (Exploratory) Production (Operational)

Data Wrangling

Visualization and Analysis

Model Training & Testing

ProductionData Pipelines Batch Scoring

Online Scoring

Serving

Data GovernanceGovernance

Processing

Acquisition

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Apache Spark

Apache Spark is at the core of our data science

experience

• Libraries for common machine learning

• Trusted in production by our customers

• Delivered with expert support and training

• A requirement for our Data Science Workbench

Apache Spark is a huge driver for machine

learning

• Native language development tools

• Reliable operation at big data scale

• Native access to Hadoop data for testing and training

Spark 2.0 is here

• Separate parcel for easy implementation for multiple Spark instances

• Better Streaming Performance

• Machine Learning Persistence

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Spark Use Cases

Top Use Cases Data Processing (55%), Real-Time Stream Processing (44%), Exploratory Data Science (33%) and Machine Learning (33%).

3 out of 8 are employing Spark in data science research

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Deep learning in Cloudera with Apache Spark

• Two packages:

• CaffeOnSpark

• TensorFlowOnSpark

• Developed by Yahoo

• Python and Scala APIs

• All DL architectures

• Integrated pipeline

• Run on existing clusters

• Training and inference

• Open source DL library

• Developed by Skymind

• Built on JVMs

• Supports CPUs and

GPUs

• Java, Scala, Python APIs

• Training and inference

• Imports models from:

• TensorFlow

• Caffe

• Torch

• Theano

• Runs on existing clusters

• Deep learning framework

• Developed by Intel

• Supports CPUs only

• Leverages Intel MKL

• Scala, Python APIs

• Imports models from:

• TensorFlow

• Caffe

• Torch

• Runs on existing clusters

Spark Packages DL4J BigDL

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Solving Data Science is a Full-Stack Problem

• Leverage Big Data

• Enable real-time use cases

• Provide sufficient toolset for the Data Analysts

• Provide sufficient toolset for the Data Scientists + Data Engineers

• Provide standard data governance capabilities

• Provide standard security across the stack

• Provide flexible deployment options

• Integrate with partner tools

• Provide management tools that make it easy for IT to deploy/maintain

✓Hadoop

✓Kafka, Spark Streaming

✓Spark, Hive, Hue

✓Data Science Workbench

✓Navigator + Partners

✓Kerberos, Sentry, Record Service, KMS/KTS

✓Cloudera Director

✓Rich Ecosystem

✓Cloudera Manager/Director

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Data Science WorkbenchSelf-service data science for the enterprise

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Our Goal: Bring more data science users to Hadoop

Help more data scientistsuse the power of Hadoop

Use a powerful, familiar environment with direct access to

Hadoop data and compute

Data ScientistData Engineer

Make it easy and secure to add new users, use cases

Offer secure self-service analytics and a faster path to production on common, affordable infrastructure

Enterprise ArchitectHadoop Admin

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Accelerates data science from

development to production with:

●Secure self-service data access

●On-demand compute

●Support for Python, R, and Scala

●Project dependency isolation for

multiple library versions

●Workflow automation, version

control, collaboration and sharing

Cloudera Data Science WorkbenchSelf-service data science for the enterprise

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A modern data science architecture

CDH CDH

Cloudera Manager

gateway nodes CDH nodes

●Built on Docker and Kubernetes

●Runs on dedicated gateway nodes

●User sessions run in isolated

“engine” containers which:

○Host Kerberos-authenticated

Python/R/Scala runtimes

○ Interact with Spark via YARN

client mode (Driver runs in

container, workers on CDH)

●Single-cluster only (for now)

Hive, HDFS, ...

CDSW CDSW

...

Master

...

Engine

EngineEngine

EngineEngine

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“Our data scientists want GPUs, but we can’t find a way to deliver multi-tenancy.If they go to the cloud on their own, it’s expensive and we lose governance.”

●Extend existing CDSW benefits to GPU-optimized deep learning tools

●Schedule & share GPU resources

●Train on GPUs, deploy on CPUs

●Works on-premises or cloud

Accelerated deep learning on-demand with GPUs

Data Science Workbench

GPUCPU

CDH

CPU

CDH

CPU

single-node

training

distributed

training, scoring

Multi-tenant GPU support on-premises or

cloud

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What is the Data Science Workbench?

WorkbenchIntegrated development environment for

Python, R, and Scala with support for

Spark 2 and connectivity to secured

Hadoop clusters.

ProjectsCollaborative hub for enterprise data

science with isolated projects, secure

collaboration, and simple dependency

management.

JobsLightweight job and pipeline system for data science workloads that supports real-time monitoring, results tracking, and email alerting.

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Demo

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Key BenefitsHow is Cloudera Data Science different?

Works with fully secured clusters

One tool for multiple languages (Python, R, Scala)

Multi-tenant Architecture

Common Platform

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Thank you

Jason Hubbardjason.hubbard@cloudera.com