Cloudera's big data platform focuses on open-source solutions for machine learning and analytics, enhancing customer success by contributing to the Hadoop ecosystem and other Apache projects. The platform ensures compatibility across various Apache tools, offering features like high availability, enterprise-class management, and robust security. Cloudera aims to empower data scientists with a collaborative data science workbench and tools to facilitate efficient research-to-production workflows.

1. © Cloudera, Inc. All rights reserved.
DIE MODERNE,
OPENSOURCE-BASIERTE UND CLOUD-OPTIMIERTE
BIG DATA PLATTFORM
FÜR MACHINE LEARNING & ANALYTICS
Stefan Lipp & Frank Hereygers / Juni 2018

2. © Cloudera, Inc. All rights reserved. 2© Cloudera, Inc. All rights reserved.
CLOUDERA’S COMMITMENTS
Anything that stores your data
Any APIs your applications call
Uses open source code
Our contributions and fixes go back to
open source first
When possible, use projects
supported by multiple commercial vendors
Keeping your cluster running
Cloudera CDH edition
No limit to number of servers
Managing your applications
Employ* committers, if not PMC
members, on the projects we support
* People manage their own careers. Temporary gaps may exist
High availability features
Open
source
Subscription expiration won’t
stop the cluster
Free to use
forever
RBAC over your data

3. © Cloudera, Inc. All rights reserved. 3© Cloudera, Inc. All rights reserved.
OUR GOAL: CUSTOMER SUCCESS WITH OPEN SOURCE
By innovating in open source
Some vendors consume the open source community’s activity; others help drive it.
Cloudera leads in influencing the Hadoop platform's evolution by creating,
contributing, donating (Apache Sentry, Apache Impala, Apache Kudu) and supporting
new capabilities that meet customer requirements for security, scale, and usability.
By curating open standards
Cloudera has a long and proven track record of identifying, curating, and supporting
the open standards (including Apache HBase, Apache Solr, Apache Spark and
Apache Kafka) that provide the mainstream, long-term architecture upon which new
customer use cases are built.
By meeting the highest enterprise requirements
To ensure the best customer experience, Cloudera invests significant resources in
multi-dimensional testing on real workloads before releases, as well as in supportability
of the entire platform via extensive involvement in the open source community.

4. © Cloudera, Inc. All rights reserved. 4© Cloudera, Inc. All rights reserved.
CDH: CLOUDERA DISTRIBUTION of HADOOP
STRUCTURED
Sqoop
UNSTRUCTURED
Kafka, Flume
PROCESS, ANALYZE, SERVE
UNIFIED SERVICES
RESOURCE MANAGEMENT
YARN, Zookeeper
SECURITY
Sentry
FILESYSTEM
HDFS
RELATIONAL
Kudu
NoSQL
HBase
STORE
INTEGRATE
BATCH
Spark, Hive, Pig
MapReduce
STREAM
Spark
SQL
Impala
SEARCH
Solr
• Ensure that disparate
Apache projects work
together reliably
• Provide enterprise-class
capabilities initially not
addressed by Apache
• Create Sustainability
OPERATIONS
Cloudera Manager
“Express”

5. © Cloudera, Inc. All rights reserved. 5© Cloudera, Inc. All rights reserved.
CDH6: GIANT LEAP FORWARD
Hadoop 3 Hive 2.1 HBase 2 Spark 2.2 Parquet 1.9
Solr 7 Oozie 5 Sentry 2 Kafka 1 Avro 1.8
ZooKeeper 3.4 Flume 1.8 Sqoop 1.4 Pig 0.17
currently in Beta,
GA by mid year

6. © Cloudera, Inc. All rights reserved. 6© Cloudera, Inc. All rights reserved.
CLOUDERA SUBSCRIPTION EXTENDS ON THE EDGES
STRUCTURED
Sqoop
UNSTRUCTURED
Kafka, Flume
PROCESS, ANALYZE, SERVE
UNIFIED SERVICES
RESOURCE MANAGEMENT
YARN, Zookeeper
SECURITY
Sentry
FILESYSTEM
HDFS
RELATIONAL
Kudu
NoSQL
HBase
STORE
INTEGRATE
BATCH
Spark, Hive, Pig
MapReduce
STREAM
Spark
SQL
Impala
SEARCH
Solr
DATA
MANAGEMENT
Cloudera Navigator
Navigator Encrypt
Navigator Optimizer
OPERATIONS
Cloudera Manager
Cloudera Director
Cloudera Altus
DATASCIENCE ENABLEMENT
Cloudera Data Science Workbench enhancements based
on customers’ needs
24x7 support
Rolling upgrades
Data governance and lineage
Automated backup and recovery
Full disk encryption
hybrid & portable multicloud usage
Data Science Enablement
With partners: rigorous
testing and certification
cycles
#1 Goal: Maximum value with minimum risk

7. © Cloudera, Inc. All rights reserved.7 © Cloudera, Inc. All rights reserved.
BIG DATA MARKET EVOLUTION
BIG DATA
TECH
DATA
PLATFORM
CIO
& Data Admins
ML, ANALYTICS
& CLOUD
LOB
& Data Scientists
IT early adopters
& Developers
DIGITAL
TRANSFORMATION
powered by data
C-suite &
Boards

8. © Cloudera, Inc. All rights reserved. 8© Cloudera, Inc. All rights reserved.
EARLY STAGE: CHAIN OF BIG DATA TOOLS
Data Sources Data Ingest Data Storage & Processing
Serving, Analytics &
Machine Learning
Apache Kafka
Stream or batch ingestion of IoT data
Apache Sqoop
Ingestion of data from relational
sources
Apache Hadoop
Storage (HDFS) & deep batch
processing
Apache Kudu
Storage & serving for fast changing
data
Apache HBase
NoSQL data store for real time
applications
Apache Impala
MPP SQL for fast analytics
Cloudera Search
Real time searchConnected Things/ Data
Sources
Structured Data Sources
Apache Spark
Stream & iterative processing, ML

9. © Cloudera, Inc. All rights reserved. 9© Cloudera, Inc. All rights reserved.
EARLY STAGE: CHAIN OF CLOUD BIG DATA TOOLS

10. 10 © Cloudera, Inc. All rights reserved.
CLOUDERA
DIRECTOR
Infrastructure-
as-a-Service
Automate Cluster
Provisioning
OPERATIONA
L DATABASE
DATA
ENGINEERING
ANALYTIC
DATABASE
DATA
SCIENCE
Cloudera Director
(Cloud Provider API’s)

11. © Cloudera, Inc. All rights reserved.11 © Cloudera, Inc. All rights reserved.
WHAT IS A BIG DATA WORKLOAD?
Data + Compute + Data Context
Data Context:
• Schema definitions (HMS)
• Security authorizations (Sentry)
• Metadata (Navigator)
• Business glossary (Navigator)
• Data Lineage (Navigator)
• Audit logs (Navigator)

12. 13 © Cloudera, Inc. All rights reserved.
LIFT & SHIFT
CLOUDERA CLUSTER
(PERSISTENT)
COMPUTE DATA
CONTEXT
Data
Engineering
Analytics
Data
Science
Security
Metadata
Governance
STORAGE
HDFS
CLOUDERA CLUSTER
(PERSISTENT)
COMPUTE DATA
CONTEXT
Data
Engineering
Analytics
Data
Science
Security
Metadata
Governance
STORAGE
CLOUD OBJECT STORE
CUSTOMER VPC
ON PREMISES PUBLIC CLOUD

13. © Cloudera, Inc. All rights reserved.14 © Cloudera, Inc. All rights reserved.
EVOLUTION PHASE 1: DATA MANAGEMENT PLATFORM
Integrated data, workflows, metadata, security, governance, ...
Amazon
S3
Microsoft
ADLS HDFS KUDU
SECURITY GOVERNANCE
WORKLOAD
MANAGEMENT
INGEST &
REPLICATION
DATA CATALOG
Core
Services
Storage
Services
ANALYTIC
DATABASE
DATA
SCIENCE
EXTENSIBLE
SERVICES
OPERATIONAL
DATABASE
DATA
ENGINEERING

14. 15 © Cloudera, Inc. All rights reserved.
EVEN AVAILABLE AS PLATFORM AS A SERVICE
portable code, APIs, data, workflows, metadata, security, governance, ...
Customer Cloud
Compute
Storage
CLI
Web
SDK
ALTUS
ANALYTIC
DATABASE
ALTUS DATA
ENGINEERING
ALTUS
CONTROL
PLANE

15. © Cloudera, Inc. All rights reserved.16 © Cloudera, Inc. All rights reserved.
NOW: THE NEXT CHALLENGE
Balance these needs
DATA SCIENCE
• Access to granular data
• Flexibility - preferred open
source tools
• Elastic provisioning of
compute and storage
• Reproducible research
• Path to production
DATA MANAGEMENT
• Security
• Governance
• Standards
• Low maintenance
• Low cost
• Self-service access

16. © Cloudera, Inc. All rights reserved.17 © Cloudera, Inc. All rights reserved.
THE TYPICAL DATA SCIENTIST
“If I can’t use my favorite tools, I’ll…”
• Copy data to my laptop
• Copy data to a data science appliance
• Copy data to a cloud service
Why this is a problem:
• Complicates security
• Breaks data governance
• Adds latency to process
• Makes collaboration more difficult
• Complicates model management and
deployment
• No model governance

17. © Cloudera, Inc. All rights reserved.18 © Cloudera, Inc. All rights reserved.
DATA SCIENCE / MACHINE LEARNING AT CLOUDERA
Our philosophy
We empower our customers to
run their business on data with an
open platform:
● Your data
● Open algorithms
● Running anywhere
We accelerate enterprise data science.

18. © Cloudera, Inc. All rights reserved. 19© Cloudera, Inc. All rights reserved.
THE IMPORTANCE OF AN OPEN DATA SCIENCE
ECOSYSTEM
Open ecosystem Black box

19. © Cloudera, Inc. All rights reserved.20 © Cloudera, Inc. All rights reserved.
CURRENT INNOVATION: MACHINE LEARNING PLATFORM
Enable applied machine learning from research to production

20. © Cloudera, Inc. All rights reserved.21 © Cloudera, Inc. All rights reserved.
CLOUDERA DATA SCIENCE WORKBENCH
Accelerate Machine Learning from Research to Production
For data scientists
• Experiment faster
Use R, Python, or Scala with
on-demand compute and
secure CDH data access
• Work together
Share reproducible research
with your whole team
• Deploy with confidence
Get to production repeatably
and without recoding
For IT professionals
• Bring data science to the data
Give your data science team
more freedom while reducing
the risk and cost of silos
• Secure by default
Leverage common security
and governance across
workloads
• Run anywhere
On-premises or in the cloud

21. © Cloudera, Inc. All rights reserved.22 © Cloudera, Inc. All rights reserved.
PLATFORM FOR
DATA SCIENCE &
MACHINE LEARNING
• Open platform
• Complete lifecycle
• Team collaboration
• Enterprise ready
• Runs anywhere
RESEARCH | PRODUCTION
LOCAL | SPARK | IMPALA
DEPLOYMENT
COMPUTE
OPEN SOURCE ECOSYSTEMALGORITHM
S
SELF-SERVICE
TOOLS
SOLUTIONS | USE CASESAPPS
CLOUD ON-PREMISES
ADLSS3 HDFS KUDU
CATALOG | SECURITY |
GOVERNANCE

22. © Cloudera, Inc. All rights reserved.23 © Cloudera, Inc. All rights reserved.
A MODERN DATA SCIENCE ARCHITECTURE
Containerized environments with scalable, on-demand compute
• Built with Docker and Kubernetes
• Isolated, reproducible user environments
• Supports both big and small data
• Local Python, R, Scala runtimes
• Schedule & share GPU resources
• Run Spark, Impala, and other CDH services
• Secure and governed by default
• Easy, audited access to Kerberized clusters
• Leverages SDX platform services
• Deployed with Cloudera Manager
CDH CDH
Cloudera Manager
gateway node(s) CDH nodes
Hive, HDFS, ...
CDSW CDSW
...
Master
...
Engine
EngineEngine
EngineEngine

23. © Cloudera, Inc. All rights reserved.24 © Cloudera, Inc. All rights reserved.
ACCELERATED DEEP LEARNING WITH GPUs
Multi-tenant GPU support on-premises or cloud
• Extend CDSW to deep learning
• Schedule & share GPU resources
• Train on GPUs, deploy on CPUs
• Works on-premises or cloud
CDSW
GPUCPU
CDH
CPU
CDH
CPU
single-node
training
distributed
training, scoring
“Our data scientists want GPUs, but
we need multi-tenancy. If they go to
the cloud on their own, it’s
expensive and we lose
governance.”
GPU On CDH coming in C6

24. © Cloudera, Inc. All rights reserved.25 © Cloudera, Inc. All rights reserved.
SUMMARY
Cloudera helps with OpenSource Data Management AND Machine Learning
DATA MANAGEMENT MACHINE LEARNING
Enterprise Data Hub with SDX
provides a unified foundation.
Data Science Workbench
enables collaborative self-
service.
APPLIED RESEARCH
Fast Forward Labs
cuts through the hype.

25. © Cloudera, Inc. All rights reserved.26 © Cloudera, Inc. All rights reserved.
ONE MORE THING
https://www.cloudera.com/products/altus.html

26. 27 © Cloudera, Inc. All rights reserved.
ALTUS ARCHITECTURE
CLOUDERA CLUSTER
(TRANSIENT / PERSISTENT)
COMPUTE DATA
CONTEXT
Data
Engineering
Analytics
Data
Science
Security
Metadata
Governance
STORAGE
CLOUD OBJEcT STORE
Cloud IaaS Altus PaaS
CLOUDERA
CLUSTERS
(TRANSIENT–
ALTUS)
COMPUTE
Data
Engineering
CUSTOMER VPC
STORAGE
CLOUD OBJECT STORE
CLOUDERA CLUSTER
(PERSISTENT–DIRECTOR)
COMPUTE DATA
CONTEXT
CLOUDERA
CLUSTERS
(TRANSIENT–
ALTUS)
COMPUTE
Analytics
CUSTOMER VPC CLOUDERA VPC
CLOUDERA
ALTUS
CONTROL
PLANE
DATA
CONTEXT

27. © Cloudera, Inc. All rights reserved. 28