Public Sector Case Studies - AWS Summit 2012 - NYC
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This document summarizes presentations from a public sector cloud innovation luncheon. It discusses how several organizations are using AWS cloud services to realize efficiencies, share large datasets, develop new testing models quickly, and store/analyze large amounts of video evidence. Attendees can discuss their own cloud innovations and challenges and how AWS can help further during a post-luncheon technical session.
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These are the slides from a keynote I gave at the
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AWS Enterprise Day | Closing Keynote, Singapore - Dr Werner Vogels
The document discusses how connected data and cloud computing are enabling new types of data-driven science and applications. It provides examples across several domains: (1) genomics research has progressed from sequencing individual genomes to large-scale comparisons using petabytes of data stored in the cloud, (2) consumer applications like home security cameras generate petabytes of data per month for computer vision analysis, (3) retail and travel companies use vast amounts of customer, product, and location data to personalize experiences, (4) industrial research leverages cloud-based materials data from global partners, (5) sports and location-based applications also generate large volumes of real-time data for analysis in the cloud. The cloud is enabling data to be
Beyond the Fridge, The World of Connected Data - Dr Werner Vogels
The document discusses how connected data and cloud computing are enabling new types of data-driven science and applications. It provides examples across different domains like genomics (analyzing genomes and genetic variations), consumer services (processing video and social media data), retail (understanding customers through analytics), and industrial/location data. The amount of data being generated is growing exponentially and cloud infrastructure allows scaling to process and analyze vast datasets for new insights.
The pulse of cloud computing with bioinformatics as an example
The document discusses how cloud computing can enable large-scale genomic analysis by providing on-demand access to computational resources and petabytes of reference data. It describes how tools like Galaxy and CloudMan allow researchers to perform genomic analysis in the cloud through a web browser by automating the provisioning and configuration of cloud resources. This approach makes genomic research more accessible and enables the elastic scaling of analysis as needed.
Aktuelle Referenzen im Life Sciences Umfeld
Durch den Druck, schneller und effizienter am Markt zu reagieren, ist die Public Cloud Adoption in den letzten Monaten erneut stark gewachsen. In dieser Präsentation erfahren Sie, welche Projekte namenhafter Unternehmen aus dem Life Science (LS) Bereich AWS umgesetzt hat und welche Verbesserungen hinsichtlich Agilität, Kosten und Time-to-Market erreicht wurden.
Anwendungsbeispiele von Unternehmen im LS Bereich sind unter anderem High Performance Computing (HPC) im Scientific Computing Umfeld, Big Data, Aufbau digitaler Plattformen, Internet of Things (IOT), Machine Learning und große Migrationen von Rechenzentren zu AWS.
Accelerating Time to Science: Transforming Research in the Cloud
Researchers working on projects ranging from work at individual labs to some of the world's largest scientific investigations are using AWS to accelerate the pace of scientific discovery and ask questions that were previously impossible to explore. This talk explains why scientists are using Amazon Web Services and showcases a range of real-word examples.
#EarthOnAWS: How the Cloud Is Transforming Earth Observation | AWS Public Sec...
Making earth observation data available in the cloud is accelerating scientific discovery and enabling the creation of new products. Attend and learn how the cloud lets earth scientists, researchers, startups, and GIS professionals gather and analyze earth observation data without worrying about limitations of bandwidth, storage, memory, or processing power. Join us and learn how earth science data projects are becoming more scalable, agile, and efficient with AWS on-demand IT infrastructure.
Cloud computing in biomedicine intel talk
This document discusses cloud computing and its potential applications in life sciences and healthcare. It defines cloud computing and describes different cloud deployment models including public, private, and hybrid clouds. It also outlines key cloud computing characteristics like elasticity, on-demand provisioning, and resource pooling. The document then provides examples of how pharmaceutical and biotech companies like Novartis and Schrodinger are using public cloud infrastructure for large-scale computational modeling and virtual screening to accelerate drug discovery. Regulatory compliance considerations for healthcare cloud adoption are also discussed.
AWS Paris Summit 2014 - Closing Keynote Werner Vogels - Beyond the fridge
The document discusses the growing amount of data being generated from various sources such as genomics research, consumer devices, retail analytics, and industrial and sports applications. It describes how data is increasingly being collected, stored, organized, analyzed and shared in new ways using cloud-based services that can scale elastically to handle vast amounts of connected data with terabytes to petabytes in size. This allows for real-time analysis and insights that were previously not possible.
Physion.PDF
Physion offers a cloud-based data management solution called Ovation that allows scientists to easily store, organize, manage, and share their experimental research data in order to maximize collaboration. Ovation uses IBM Cloudant Dedicated Cluster as its database to provide scalable storage and synchronization between cloud and local systems. This enables scientists to have offline access to their data while also benefiting from distributed collaboration and data sharing across organizational silos.
Emerging Forms of Data and Analytics
Opening talk at the "Interdisciplinary Data Resources to Address the Challenges of Urban Living” Workshop at the Urban Big Data Centre, University of Glasgow, 4 April 2016
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This session demonstrates how cloud can accelerate breakthroughs in scientific research by providing on-demand access to powerful computing. You will gain insight into how scientific researchers are using the cloud to solve complex science, engineering, and business problems that require high bandwidth, low latency networking and very high compute capabilities. You will hear how leveraging the cloud reduces the costs and time to conduct large scale, worldwide collaborative research. Researchers can then access computational power, data storage, and supercomputing resources, and data sharing capabilities in a cost-efficient manner without implementation delays. Disease research can be accomplished in a fraction of the time, and innovative researchers in small schools or distant corners of the world have access to the same computing power as those at major research institutions by leveraging Amazon EC2, Amazon S3, optimizing C3 instances and more to increase collaboration. This session will provide best practices and insight from UC Berkeley AMP Lab on the services used to connect disparate sets of data to drive meaningful new insight and impact.
Analyzing Big Data in Medicine with Virtual Research Environments and Microse...
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AWS Summit Sydney 2014 | Closing Keynote - Dr Werner Vogels, VP & CTO, Amazon...
This document discusses the rise of connected data across various domains such as science, consumers, retail, industrial, sports, location, and multi-sensor applications. It describes how the amount of data generated today is exponentially larger than what could be stored in the Library of Congress. Various examples are provided on how connected data is being used for genomics research, video surveillance, recommendations engines, materials research, weather forecasting, and real-time analytics. The document argues that cloud computing provides the necessary infrastructure to collect, process and collaborate on massive connected data sets without limits.
Big Data
This document provides an introduction to big data, including:
- Big data is characterized by its volume, velocity, and variety, which makes it difficult to process using traditional databases and requires new technologies.
- Technologies like Hadoop, MongoDB, and cloud platforms from Google and Amazon can provide scalable storage and processing of big data.
- Examples of how big data is used include analyzing social media and search data to gain insights, enabling personalized experiences and targeted advertising.
- As data volumes continue growing exponentially from sources like sensors, simulations, and digital media, new tools and approaches are needed to effectively analyze and make sense of "big data".
As next-generation technology ratchets the price of sequen.docx
A
s next-generation technology ratchets the price of
sequencing lower and lower, users from aca-
demic labs to Big Pharma are fi nding themselves
drowning in data. What used to be gigabytes
worth of information has become terabytes or
petabytes. At the same time, the cost crunch
brought on by the global recession has made
researchers leery of unnecessary capital spending.
The result is more and more users moving their data management
to the cloud or outsourcing it entirely.
Whereas large pharma companies may have the funds and
infrastructure to maintain dedicated servers for storage and analysis
of sequencing data, small companies—especially those that don’t
sequence continuously—are leading the migration to the cloud, and
service providers are springing up to meet the demand. Cost, secu-
rity, and convenience top the list of concerns for researchers looking
for a place to unload reams of data. However, once that transition is
made, features like collaborative data sharing, access to third-party
analysis apps, and patient privacy become more important.
Expression Analysis, a Quintiles Co., Durham, N.C., provides
genomic services to the pharma and biotech industry, as well as
academic, government, and foundation laboratories doing research
in molecular biology and genetics. It provides cloud computing
services through a partnership with Golden Helix Inc., Bozeman,
Mont. Its clients require computation-intensive services for gener-
ating the initial RNA or DNA sequence and also for cleaning up,
aligning, and analyzing the sequence.
According to Expression Analysis, a typical sequencing project
for 100 RNA samples would generate 300 to 400 GB worth of com-
pressed data, or 700 GB to 1 TB worth of data in total; and that’s
just for one experiment. For multiple experiments, the amount of
data can add up to astronomical quantities quite quickly.
Some applications, such as analyzing cancer samples, are even
more data intensive, because of the depth of coverage and the need
to sample multiple cells in the tumor.
“The cloud offers a full environment in order to do analysis on
a large number of samples simultaneously,” said Wendell Jones,
PhD, vice president of statistics and bioinformatics for Expression
Analysis.
That computing power becomes a commodity for the customer,
replacing expensive, on-site, server infrastructure. The data is
instead accessed through a browser, and there is no need to upload
or download huge fi les. “You can leave them on the cloud and
access in a streaming fashion via the cloud,” Jones said.
For small companies, the cloud-based service offers additional
advantages beyond saving on hardware and real estate. Startup
companies may not have the structure in place to operate Linux-
based genome software applications. A cloud-based storage and
analysis service allows those companies to use their own local
Windows or Macintosh desktop operating systems.
There are some advantages to .
EMBL Australian Bioinformatics Resource AHM - Data Commons
This document discusses the development of the NIH Data Commons, which aims to create a shared framework and infrastructure for biomedical data. It notes the increasing amounts of data being generated and the need for data sharing and interoperability. The Data Commons framework treats data, tools, and publications as digital objects that are findable, accessible, interoperable and reusable. Current pilots include deploying reference datasets in the cloud, indexing data and tools, and a credits system for cloud resources. Challenges discussed include metrics, costs, standards, incentives and sustainability. The framework's relevance for supporting open data in Australia is also addressed.
Internet2 Bio IT 2016 v2
The document discusses Internet2, an advanced networking consortium that operates a 15,000 mile fiber optic network for research and education. It provides very high speed connectivity and collaboration technologies to facilitate large data sharing and frictionless research. Examples are given of life sciences projects utilizing Internet2's high-speed network for genomic research and agricultural applications involving terabytes of satellite and sensor data. The network is expanding to include cloud computing resources and supercomputing centers to enable global-scale distributed scientific computing and collaboration.
Why Libraries Virtualize
1) Libraries are increasingly adopting cloud computing to reduce costs, improve scalability and services, and focus resources on strategic priorities rather than technical infrastructure.
2) Cloud computing offers libraries opportunities to improve resources and services through efficiency gains, but also poses challenges regarding network complexity, sustainability, security and contingency planning.
3) For libraries to fully benefit from cloud computing, they need expertise in technical, legal and operational issues; the ability to experiment and identify appropriate services; and support from administrators to explore new IT models and their impacts.
Similar to Public Sector Case Studies - AWS Summit 2012 - NYC (20)
Scaling Ideas: Accelerating Research with AWS - Technical 301
Scaling Ideas: Accelerating Research with AWS - Technical 301
Big Data, The Community and The Commons (May 12, 2014)
Big Data, The Community and The Commons (May 12, 2014)
AWS Enterprise Day | Closing Keynote, Singapore - Dr Werner Vogels
AWS Enterprise Day | Closing Keynote, Singapore - Dr Werner Vogels
Beyond the Fridge, The World of Connected Data - Dr Werner Vogels
Beyond the Fridge, The World of Connected Data - Dr Werner Vogels
The pulse of cloud computing with bioinformatics as an example
The pulse of cloud computing with bioinformatics as an example
Accelerating Time to Science: Transforming Research in the Cloud
Accelerating Time to Science: Transforming Research in the Cloud
#EarthOnAWS: How the Cloud Is Transforming Earth Observation | AWS Public Sec...
#EarthOnAWS: How the Cloud Is Transforming Earth Observation | AWS Public Sec...
AWS Paris Summit 2014 - Closing Keynote Werner Vogels - Beyond the fridge
AWS Paris Summit 2014 - Closing Keynote Werner Vogels - Beyond the fridge
Time to Science/Time to Results: Transforming Research in the Cloud
Time to Science/Time to Results: Transforming Research in the Cloud
Analyzing Big Data in Medicine with Virtual Research Environments and Microse...
Analyzing Big Data in Medicine with Virtual Research Environments and Microse...
AWS Summit Sydney 2014 | Closing Keynote - Dr Werner Vogels, VP & CTO, Amazon...
AWS Summit Sydney 2014 | Closing Keynote - Dr Werner Vogels, VP & CTO, Amazon...
As next-generation technology ratchets the price of sequen.docx
As next-generation technology ratchets the price of sequen.docx
EMBL Australian Bioinformatics Resource AHM - Data Commons
EMBL Australian Bioinformatics Resource AHM - Data Commons
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We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
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Overview
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Key Topics Covered
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2. Understanding Edge (IoT)
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6. Viewing Kafka Messages in the Data Lake
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