One Hadoop, Multiple Clouds - NYC Big Data Meetup

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The slide deck I presented at NYC Big Data Meetup just before Strata + Hadoop World 2015. It goes into details on what's different about running Hadoop in the cloud, main use case and some lessons learned from working with customers.

Software

1© Cloudera, Inc. All rights reserved.
One Hadoop, Multiple Clouds
Andrei Savu | Tech Lead, Cloudera Director

2© Cloudera, Inc. All rights reserved.
About me
Tech Lead on Cloudera Director
Previously founder of axemblr.com
Contributed to Apache Whirr (PMC) & jclouds.
Twitter: https://twitter.com/andreisavu
LinkedIn: https://www.linkedin.com/in/sandrei

3© Cloudera, Inc. All rights reserved.
Cloudera Director
cloudera.com/director
Deploy and manage
enterprise-grade
Hadoop in the cloud
AWS & Google Cloud
Extensible via plugins

5© Cloudera, Inc. All rights reserved.
Do you use a public or
private cloud?
How do you run and
manage Hadoop?

6© Cloudera, Inc. All rights reserved.
What is this talk
about?
State of the World
Architectural Patterns
Imagine the Future

7© Cloudera, Inc. All rights reserved.
Gartner's 2015 Hype
Cycle for Emerging
Technologies (source)
Advanced Analytics
Hybrid Cloud
Internet of Things

8© Cloudera, Inc. All rights reserved.
Hybrid Clouds
Cloud Exchange
Application Portability
Private-Public
Public-Public

9© Cloudera, Inc. All rights reserved.
Cloud Wars
AWS
Microsoft Azure
Google Cloud
VMWare
Openstack
etc.

10© Cloudera, Inc. All rights reserved.
Data has Mass and
Gravity

11© Cloudera, Inc. All rights reserved.
Hadoop Environments
On-Premise versus Cloud
On-Premise Cloud
Storage Direct Attached Direct Attached or Object Store
Data Not shared across clusters Shared across multiple clusters
Sizing Fixed-size Dynamic based on load
Usage Model All users share cluster Clusters created as needed for apps/users
Resource Management (YARN)
HDFS
Process Discover Model Serve
Industry Standard Servers
(CPU, Memory, & Direct Attached Storage)
Resource Management (YARN)
HDFS
Process Discover Model Serve
Industry Standard Servers
(CPU & Memory)
Object
Storage

12© Cloudera, Inc. All rights reserved.
Cloud providers
shipping distributions
of Hadoop
Integration
Unlock Query Engines
Migration workloads
Is that a sustainable
advantage? Or just a
temporary stop gap?

13© Cloudera, Inc. All rights reserved.
Maturity level
On-prem vs. Cloud
Monitoring
Dev / Test / Prod
Availability
Durability

14© Cloudera, Inc. All rights reserved.
Common Architectural Patterns in the Cloud
Object Storage
Source Data Seed Data Backup/DR
ETL/MODELING
(Spark, MapReduce)
• Short-running clusters
• Elastic workload
• No local storage
necessary
|WASB |SWIFT |BLOB
• Long-running clusters
• Sized to demand
• Some local storage
BI/ANALYTICS
(Impala, Solr)
• Fixed clusters
• Periodic sync
• Default to local
storage
APP DELIVERY
(HBase, Kudu)

15© Cloudera, Inc. All rights reserved.
Cluster lifecycle
management
Create / Terminate
Discovery
Metadata
Monitoring

16© Cloudera, Inc. All rights reserved.
Work Queue
Workflows
Dispatch
Tracking
Decoupled
Fault Tolerant

17© Cloudera, Inc. All rights reserved.
Common Architectural Patterns in the Cloud
Object Storage
Source Data Seed Data Backup/DR
ETL/MODELING
(Spark, MapReduce)
• Short-running clusters
• Elastic workload
• No local storage
necessary
|WASB |SWIFT |BLOB
• Long-running clusters
• Sized to demand
• Some local storage
BI/ANALYTICS
(Impala, Solr)
• Fixed clusters
• Periodic sync
• Default to local
storage
APP DELIVERY
(HBase, Kudu)

18© Cloudera, Inc. All rights reserved.
Multi-user
Secure
Isolated
Friendly

19© Cloudera, Inc. All rights reserved.
Elastic
Grow or shrink
Business hours
Number of users
Storage vs. Compute
Cost efficient

20© Cloudera, Inc. All rights reserved.
Common Architectural Patterns in the Cloud
Object Storage
Source Data Seed Data Backup/DR
ETL/MODELING
(Spark, MapReduce)
• Short-running clusters
• Elastic workload
• No local storage
necessary
|WASB |SWIFT |BLOB
• Long-running clusters
• Sized to demand
• Some local storage
BI/ANALYTICS
(Impala, Solr)
• Fixed clusters
• Periodic sync
• Default to local
storage
APP DELIVERY
(HBase, Kudu)

21© Cloudera, Inc. All rights reserved.
Advanced Monitoring
Latency
Resource utilization
Consistent performance

22© Cloudera, Inc. All rights reserved.
High availability and
failure domains
Data durability
Repair within SLA
Host-to-instance

23© Cloudera, Inc. All rights reserved.
Backup and disaster
recovery
Object store centric
Active-Standby

24© Cloudera, Inc. All rights reserved.
Imagine the Future
Portable Experience
Self-service
Self-healing
Granular Security
Advanced Governance
Complete Management
What’s your vision?

26© Cloudera, Inc. All rights reserved.
Thank you!
asavu@cloudera.com

27© Cloudera, Inc. All rights reserved.
Resources
Cloudera Director: http://www.cloudera.com/director
Interested in API level integration and scripting?
https://github.com/cloudera/director-sdk
https://github.com/cloudera/director-scripts
Interested in integration with another cloud platform?
https://github.com/cloudera/director-spi
https://github.com/cloudera/director-google-plugin

28© Cloudera, Inc. All rights reserved.
What’s new in Cloudera Director 1.5?
http://blog.cloudera.com/blog/2015/08/whats-new-in-
cloudera-director-1-5/
Get Started
AWS Reference Guide
GCP Reference Guide
Try It Out
AWS Quickstart
Resources

© 2014 Cloudera, Inc. All rights reserved.

45© Cloudera, Inc. All rights reserved.
Thank you!
asavu@cloudera.com

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Foundation models are machine learning models which are easily capable of performing variable tasks on large and huge datasets. FMs have managed to get a lot of attention due to this feature of handling large datasets. It can do text generation, video editing to protein folding and robotics. In case we believe that FMs can help the hospitals and patients in any way, we need to perform some important evaluations, tests to test these assumptions. In this review, we take a walk through Fms and their evaluation regimes assumed clinical value. To clarify on this topic, we reviewed no less than 80 clinical FMs built from the EMR data. We added all the models trained on structured and unstructured data. We are referring to this combination of structured and unstructured EMR data or clinical data.

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In the realm of real-time applications, Large Language Models (LLMs) have long dominated language-centric tasks, while tools like OpenCV have excelled in the visual domain. However, the future (maybe) lies in the fusion of LLMs and deep learning, giving birth to the revolutionary concept of Large Action Models (LAMs). Imagine a world where AI not only comprehends language but mimics human actions on technology interfaces. For example, the Rabbit r1 device presented at CES 2024, driven by an AI operating system and LAM, brings this vision to life. It executes complex commands, leveraging GUIs with unprecedented ease. In this presentation, join me on a journey as a software engineer tinkering with WebRTC, Janus, and LLM/LAMs. Together, we’ll evaluate the current state of these AI technologies, unraveling the potential they hold for shaping the future of real-time applications.

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2. 2© Cloudera, Inc. All rights reserved. About me Tech Lead on Cloudera Director Previously founder of axemblr.com Contributed to Apache Whirr (PMC) & jclouds. Twitter: https://twitter.com/andreisavu LinkedIn: https://www.linkedin.com/in/sandrei

4. Journey to the Cloud

11. 11© Cloudera, Inc. All rights reserved. Hadoop Environments On-Premise versus Cloud On-Premise Cloud Storage Direct Attached Direct Attached or Object Store Data Not shared across clusters Shared across multiple clusters Sizing Fixed-size Dynamic based on load Usage Model All users share cluster Clusters created as needed for apps/users Resource Management (YARN) HDFS Process Discover Model Serve Industry Standard Servers (CPU, Memory, & Direct Attached Storage) Resource Management (YARN) HDFS Process Discover Model Serve Industry Standard Servers (CPU & Memory) Object Storage

12. 12© Cloudera, Inc. All rights reserved. Cloud providers shipping distributions of Hadoop Integration Unlock Query Engines Migration workloads Is that a sustainable advantage? Or just a temporary stop gap?

14. 14© Cloudera, Inc. All rights reserved. Common Architectural Patterns in the Cloud Object Storage Source Data Seed Data Backup/DR ETL/MODELING (Spark, MapReduce) • Short-running clusters • Elastic workload • No local storage necessary |WASB |SWIFT |BLOB • Long-running clusters • Sized to demand • Some local storage BI/ANALYTICS (Impala, Solr) • Fixed clusters • Periodic sync • Default to local storage APP DELIVERY (HBase, Kudu)

17. 17© Cloudera, Inc. All rights reserved. Common Architectural Patterns in the Cloud Object Storage Source Data Seed Data Backup/DR ETL/MODELING (Spark, MapReduce) • Short-running clusters • Elastic workload • No local storage necessary |WASB |SWIFT |BLOB • Long-running clusters • Sized to demand • Some local storage BI/ANALYTICS (Impala, Solr) • Fixed clusters • Periodic sync • Default to local storage APP DELIVERY (HBase, Kudu)

20. 20© Cloudera, Inc. All rights reserved. Common Architectural Patterns in the Cloud Object Storage Source Data Seed Data Backup/DR ETL/MODELING (Spark, MapReduce) • Short-running clusters • Elastic workload • No local storage necessary |WASB |SWIFT |BLOB • Long-running clusters • Sized to demand • Some local storage BI/ANALYTICS (Impala, Solr) • Fixed clusters • Periodic sync • Default to local storage APP DELIVERY (HBase, Kudu)

25.

27. 27© Cloudera, Inc. All rights reserved. Resources Cloudera Director: http://www.cloudera.com/director Interested in API level integration and scripting? https://github.com/cloudera/director-sdk https://github.com/cloudera/director-scripts Interested in integration with another cloud platform? https://github.com/cloudera/director-spi https://github.com/cloudera/director-google-plugin

28. 28© Cloudera, Inc. All rights reserved. What’s new in Cloudera Director 1.5? http://blog.cloudera.com/blog/2015/08/whats-new-in- cloudera-director-1-5/ Get Started AWS Reference Guide GCP Reference Guide Try It Out AWS Quickstart Resources

29. Cloudera Director Screenshots

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