This presentation includes an intro to bioinformatics with an emphasis on human genome re-sequencing and how Hadoop and Neo4j can be used together to open striking possibilities.
Python has been one of the premier, flexible, and powerful open-source language that is easy to learn, easy to use, and has powerful libraries for data manipulation and analysis. It’s easy to learn simple syntax is very accessible to new programmers and is similar to Matlab, C/C++, Java, or Visual Basic. Python is general purpose and comparatively easy to learn with an increased adoption for analytical and quantitative computing. For over a decade, Python has been used in scientific computing and highly quantitative domains such as finance, oil and gas, physics, and signal processing.
As companies like Facebook and Google have introduced us to Graph Search and the Knowledge Graph, developers are learning the benefits of graph database architectures. Graph databases, like Neo4j, have increased in popularity by nearly 250% from last year - the highest among all other DBMS categories, according to db-engines.com. Join Kenny Bastani as we look at the benefits of using a graph database, explore various use cases and walkthrough creating a movie recommendation app on Neo4j 2.0.
The majority of NoSQL meetups in London are hosted on meetup.com and luckily for us meetup.com has an API that allows us to extract all the corresponding data - groups, events, venues, members and RSVPs.
In this talk Mark will show how we can use R to gain quick insights into the data using tools like dplyr and ggplot2. We'll also do some social network analysis of the attendees of London's meetup scene using igraph.
Finally we'll look at how we could bring together all these insights into a brand new Clojure front end for the meetup website.
Python has been one of the premier, flexible, and powerful open-source language that is easy to learn, easy to use, and has powerful libraries for data manipulation and analysis. It’s easy to learn simple syntax is very accessible to new programmers and is similar to Matlab, C/C++, Java, or Visual Basic. Python is general purpose and comparatively easy to learn with an increased adoption for analytical and quantitative computing. For over a decade, Python has been used in scientific computing and highly quantitative domains such as finance, oil and gas, physics, and signal processing.
As companies like Facebook and Google have introduced us to Graph Search and the Knowledge Graph, developers are learning the benefits of graph database architectures. Graph databases, like Neo4j, have increased in popularity by nearly 250% from last year - the highest among all other DBMS categories, according to db-engines.com. Join Kenny Bastani as we look at the benefits of using a graph database, explore various use cases and walkthrough creating a movie recommendation app on Neo4j 2.0.
The majority of NoSQL meetups in London are hosted on meetup.com and luckily for us meetup.com has an API that allows us to extract all the corresponding data - groups, events, venues, members and RSVPs.
In this talk Mark will show how we can use R to gain quick insights into the data using tools like dplyr and ggplot2. We'll also do some social network analysis of the attendees of London's meetup scene using igraph.
Finally we'll look at how we could bring together all these insights into a brand new Clojure front end for the meetup website.
Applied Data Science: Building a Beer Recommender | Data Science MD - Oct 2014Austin Ogilvie
Applied Data Science: Building a Beer Recommender | Data Science MD - Oct 2014
-----------
Slides from a talk by Greg Lamp, CTO of Yhat, about building recommendation systems using Python and deploying them to production.
Building a Distributed Build System at Google ScaleAysylu Greenberg
It’s hard to imagine a modern developer workflow without a sufficiently advanced build system: Make, Gradle, Maven, Rake, and many others. In this talk, we’ll discuss the evolution of build systems that leads to distributed build systems, like Google's BuildRabbit. Then, we’ll dive into how we can build a scalable system that is fast and resilient, with examples from Google. We’ll conclude with the discussion of general challenges of migrating systems from one architecture to another.
JSON and Oracle Database: A Brave New WorldDaniel McGhan
A world of apps built in JavaScript, using JSON as their data exchange format, relying on APIs to get the job done - does Oracle Database have a place in this world? Can it offer UI developers what they need to get their job done as productively and successfully as possible? Absolutely! In this session, attendees will explore the new support for JSON in Oracle Database SQL and PL/SQL and learn how to help front-end developers build secure, high-performance applications.
Creando microservicios con Java, Microprofile y TomEE - Baranquilla JUGCésar Hernández
En esta sesión los asistentes presenciaron la base teórica y práctica para la creación de micro servicios con Java, JakartaEE, MicroProfile utilizando TomEE como servidor de aplicaciones.
Developing in R - the contextual Multi-Armed Bandit editionRobin van Emden
Attached, the slides of my presentation on how to create R packages, illustrated with lessons learned in developing "contextual": a package that enables you to easily simulate and analyze contextual multi-armed bandit algorithms.
Code: https://github.com/Nth-iteration-labs/contextual
Data Science Amsterdam - Massively Parallel Processing with Procedural LanguagesIan Huston
The goal of in-database analytics is to bring the calculations to the data, reducing transport costs and I/O bottlenecks. With Procedural Languages such as PL/Python and PL/R data parallel queries can be run across terabytes of data using not only pure SQL but also familiar Python and R packages. The Pivotal Data Science team have used this technique to create fraud behaviour models for each individual user in a large corporate network, to understand interception rates at customs checkpoints by accelerating natural language processing of package descriptions and to reduce customer churn by building a sentiment model using customer call centre records.
http://www.meetup.com/Data-Science-Amsterdam/events/178974942/
Monitoring Big Data Systems Done "The Simple Way" - Codemotion Milan 2017 - D...Demi Ben-Ari
Once you start working with distributed Big Data systems, you start discovering a whole bunch of problems you won’t find in monolithic systems.
All of a sudden to monitor all of the components becomes a big data problem itself.
In the talk we’ll mention all of the aspects that you should take in consideration when monitoring a distributed system once you’re using tools like:
Web Services, Apache Spark, Cassandra, MongoDB, Amazon Web Services.
Not only the tools, what should you monitor about the actual data that flows in the system?
And we’ll cover the simplest solution with your day to day open source tools, the surprising thing, that it comes not from an Ops Guy.
Demi Ben-Ari - Monitoring Big Data Systems Done "The Simple Way" - Codemotion...Codemotion
Once you start working with Big Data systems, you discover a whole bunch of problems you won’t find in monolithic systems. Monitoring all of the components becomes a big data problem itself. In the talk we’ll mention all of the aspects that you should take in consideration when monitoring a distributed system using tools like: Web Services,Spark,Cassandra,MongoDB,AWS. Not only the tools, what should you monitor about the actual data that flows in the system? We’ll cover the simplest solution with your day to day open source tools, the surprising thing, that it comes not from an Ops Guy.
Fully Tested: From Design to MVP In 3 WeeksSmartBear
In this presentation Daniel Giordano, Product Marketing Manager at SmartBear, will cover how to speed up your development with a design first mind set, virtualizing services and dependencies to enhance collaboration between developers & testers, & end-to-End testing strategies for an immature product.
The Briefing Room with Dr. Robin Bloor and SYSTAP
Live Webcast June 30, 2015
Watch the Archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=0ff3889293f6c090483295fd7362c5a4
There's a reason why the biggest Web companies these days leverage graph technology: it is incredibly powerful for revealing a wide range of insights. Unlike other analytical databases, graph can very quickly identify the kinds of patterns that lead to better business decisions. Though relatively nascent in existing data centers, graph databases are proving to be well-suited for all kinds of business use cases, from clustering and hypothesis generation to failure detection and cyber analytics.
Register for this episode of The Briefing Room to learn from veteran Analyst Dr. Robin Bloor as he discusses how semantic technology fits in the spectrum of database and discovery solutions. He’ll be briefed by Brad Bebee of SYSTAP, who will showcase his company’s Blazegraph products and Mapgraph technology. He will explain how SYSTAP’s approach overcomes the challenge of scalability, and how graph technology’s powerful data management capabilities can deliver better enterprise performance and analytics using GPUs and other approaches.
Visit InsideAnalysis.com for more information.
Massively Parallel Processing with Procedural Python by Ronert Obst PyData Be...PyData
The Python data ecosystem has grown beyond the confines of single machines to embrace scalability. Here we describe one of our approaches to scaling, which is already being used in production systems. The goal of in-database analytics is to bring the calculations to the data, reducing transport costs and I/O bottlenecks. Using PL/Python we can run parallel queries across terabytes of data using not only pure SQL but also familiar PyData packages such as scikit-learn and nltk. This approach can also be used with PL/R to make use of a wide variety of R packages. We look at examples on Postgres compatible systems such as the Greenplum Database and on Hadoop through Pivotal HAWQ. We will also introduce MADlib, Pivotal’s open source library for scalable in-database machine learning, which uses Python to glue SQL queries to low level C++ functions and is also usable through the PyMADlib package.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
More Related Content
Similar to Hadoop and Neo4j: A Winning Combination for Bioinformatics
Applied Data Science: Building a Beer Recommender | Data Science MD - Oct 2014Austin Ogilvie
Applied Data Science: Building a Beer Recommender | Data Science MD - Oct 2014
-----------
Slides from a talk by Greg Lamp, CTO of Yhat, about building recommendation systems using Python and deploying them to production.
Building a Distributed Build System at Google ScaleAysylu Greenberg
It’s hard to imagine a modern developer workflow without a sufficiently advanced build system: Make, Gradle, Maven, Rake, and many others. In this talk, we’ll discuss the evolution of build systems that leads to distributed build systems, like Google's BuildRabbit. Then, we’ll dive into how we can build a scalable system that is fast and resilient, with examples from Google. We’ll conclude with the discussion of general challenges of migrating systems from one architecture to another.
JSON and Oracle Database: A Brave New WorldDaniel McGhan
A world of apps built in JavaScript, using JSON as their data exchange format, relying on APIs to get the job done - does Oracle Database have a place in this world? Can it offer UI developers what they need to get their job done as productively and successfully as possible? Absolutely! In this session, attendees will explore the new support for JSON in Oracle Database SQL and PL/SQL and learn how to help front-end developers build secure, high-performance applications.
Creando microservicios con Java, Microprofile y TomEE - Baranquilla JUGCésar Hernández
En esta sesión los asistentes presenciaron la base teórica y práctica para la creación de micro servicios con Java, JakartaEE, MicroProfile utilizando TomEE como servidor de aplicaciones.
Developing in R - the contextual Multi-Armed Bandit editionRobin van Emden
Attached, the slides of my presentation on how to create R packages, illustrated with lessons learned in developing "contextual": a package that enables you to easily simulate and analyze contextual multi-armed bandit algorithms.
Code: https://github.com/Nth-iteration-labs/contextual
Data Science Amsterdam - Massively Parallel Processing with Procedural LanguagesIan Huston
The goal of in-database analytics is to bring the calculations to the data, reducing transport costs and I/O bottlenecks. With Procedural Languages such as PL/Python and PL/R data parallel queries can be run across terabytes of data using not only pure SQL but also familiar Python and R packages. The Pivotal Data Science team have used this technique to create fraud behaviour models for each individual user in a large corporate network, to understand interception rates at customs checkpoints by accelerating natural language processing of package descriptions and to reduce customer churn by building a sentiment model using customer call centre records.
http://www.meetup.com/Data-Science-Amsterdam/events/178974942/
Monitoring Big Data Systems Done "The Simple Way" - Codemotion Milan 2017 - D...Demi Ben-Ari
Once you start working with distributed Big Data systems, you start discovering a whole bunch of problems you won’t find in monolithic systems.
All of a sudden to monitor all of the components becomes a big data problem itself.
In the talk we’ll mention all of the aspects that you should take in consideration when monitoring a distributed system once you’re using tools like:
Web Services, Apache Spark, Cassandra, MongoDB, Amazon Web Services.
Not only the tools, what should you monitor about the actual data that flows in the system?
And we’ll cover the simplest solution with your day to day open source tools, the surprising thing, that it comes not from an Ops Guy.
Demi Ben-Ari - Monitoring Big Data Systems Done "The Simple Way" - Codemotion...Codemotion
Once you start working with Big Data systems, you discover a whole bunch of problems you won’t find in monolithic systems. Monitoring all of the components becomes a big data problem itself. In the talk we’ll mention all of the aspects that you should take in consideration when monitoring a distributed system using tools like: Web Services,Spark,Cassandra,MongoDB,AWS. Not only the tools, what should you monitor about the actual data that flows in the system? We’ll cover the simplest solution with your day to day open source tools, the surprising thing, that it comes not from an Ops Guy.
Fully Tested: From Design to MVP In 3 WeeksSmartBear
In this presentation Daniel Giordano, Product Marketing Manager at SmartBear, will cover how to speed up your development with a design first mind set, virtualizing services and dependencies to enhance collaboration between developers & testers, & end-to-End testing strategies for an immature product.
The Briefing Room with Dr. Robin Bloor and SYSTAP
Live Webcast June 30, 2015
Watch the Archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=0ff3889293f6c090483295fd7362c5a4
There's a reason why the biggest Web companies these days leverage graph technology: it is incredibly powerful for revealing a wide range of insights. Unlike other analytical databases, graph can very quickly identify the kinds of patterns that lead to better business decisions. Though relatively nascent in existing data centers, graph databases are proving to be well-suited for all kinds of business use cases, from clustering and hypothesis generation to failure detection and cyber analytics.
Register for this episode of The Briefing Room to learn from veteran Analyst Dr. Robin Bloor as he discusses how semantic technology fits in the spectrum of database and discovery solutions. He’ll be briefed by Brad Bebee of SYSTAP, who will showcase his company’s Blazegraph products and Mapgraph technology. He will explain how SYSTAP’s approach overcomes the challenge of scalability, and how graph technology’s powerful data management capabilities can deliver better enterprise performance and analytics using GPUs and other approaches.
Visit InsideAnalysis.com for more information.
Massively Parallel Processing with Procedural Python by Ronert Obst PyData Be...PyData
The Python data ecosystem has grown beyond the confines of single machines to embrace scalability. Here we describe one of our approaches to scaling, which is already being used in production systems. The goal of in-database analytics is to bring the calculations to the data, reducing transport costs and I/O bottlenecks. Using PL/Python we can run parallel queries across terabytes of data using not only pure SQL but also familiar PyData packages such as scikit-learn and nltk. This approach can also be used with PL/R to make use of a wide variety of R packages. We look at examples on Postgres compatible systems such as the Greenplum Database and on Hadoop through Pivotal HAWQ. We will also introduce MADlib, Pivotal’s open source library for scalable in-database machine learning, which uses Python to glue SQL queries to low level C++ functions and is also usable through the PyMADlib package.
Similar to Hadoop and Neo4j: A Winning Combination for Bioinformatics (20)
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
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- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
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This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Securing your Kubernetes cluster_ a step-by-step guide to success !
Hadoop and Neo4j: A Winning Combination for Bioinformatics
1. {GraphConnect NYC}
Hadoop and Graph Databases
(Neo4j): Winning Combination for
Bioinformatics
Jonathan Freeman
@freethejazz
{Open Software Integrators} { www.osintegrators.com} {@osintegrators}
2. Hadoop + Neo4j = Bioanalytics Win
Open Software Integrators
●
Jonathan Freeman
@freethejazz
Founded January 2008 by Andrew C. Oliver
○ Durham, NC
Revenue and staff has at least doubled every year since
2009.
●
New office (2012) in Chicago, IL
○ We're hiring associate to senior level as well as UI Developers
(JQuery, Javascript, HTML, CSS)
○ Up to 50% travel (probably less), salary + bonus, 401k, health,
etc etc
○ Preferred: Java, Tomcat, JBoss, Hibernate, Spring, RDBMS,
JQuery
○ Nice to have: Hadoop, Neo4j, MongoDB, Ruby a/o at least one
Cloud platform
{Open Software Integrators} { www.osintegrators.com} {@osintegrators}
3. Hadoop + Neo4j = Bioinformatics Win
Questions to answer
●
●
●
●
uhh, bioinformatics?
What is Hadoop? Why is it a good fit?
And Neo4j? Why the combination?
I want this now! How do I do it?!?!
{Open Software Integrators} { www.osintegrators.com} {@osintegrators}
Jonathan Freeman
@freethejazz
5. Hadoop + Neo4j = Bioinformatics Win
Jonathan Freeman
@freethejazz
“
dynamic
information processing
system
{Open Software Integrators} { www.osintegrators.com} {@osintegrators}
6. Hadoop + Neo4j = Bioinformatics Win
Jonathan Freeman
@freethejazz
Life
http://www.labtimes.org/labtimes/issues/lt2011/lt07/lt_2011_07_26_29.pdf
{Open Software Integrators} { www.osintegrators.com} {@osintegrators}
7. Hadoop + Neo4j = Bioinformatics Win
Jonathan Freeman
@freethejazz
● Storing/Retrieving Biological Data
● Organizing Biological Data
● Analyzing Biological Data
{Open Software Integrators} { www.osintegrators.com} {@osintegrators}
8. Hadoop + Neo4j = Bioinformatics Win
Jonathan Freeman
@freethejazz
Biological Data
● amino acid sequences
● nucleotide sequences
● protein structures
{Open Software Integrators} { www.osintegrators.com} {@osintegrators}
9. Hadoop + Neo4j = Bioinformatics Win
Jonathan Freeman
@freethejazz
●
●
●
●
●
Genetic sequence analysis
Tracing biological evolution
Analysis of gene expression
Studying mutations in cancer
Predicting protein structure and
function
● Molecular Interaction
{Open Software Integrators} { www.osintegrators.com} {@osintegrators}
10. Hadoop + Neo4j = Bioinformatics Win
Jonathan Freeman
@freethejazz
●
●
●
●
●
Genetic sequence analysis
Tracing biological evolution
Analysis of gene expression
Studying mutations in cancer
Predicting protein structure and
function
● Molecular Interaction
{Open Software Integrators} { www.osintegrators.com} {@osintegrators}
11. Hadoop + Neo4j = Bioinformatics Win
Jonathan Freeman
@freethejazz
Full Human Genome Sequencing Then
13 Years
$2,700,000,000
{Open Software Integrators} { www.osintegrators.com} {@osintegrators}
12. Hadoop + Neo4j = Bioinformatics Win
Jonathan Freeman
@freethejazz
Full Human Genome Sequencing Then
1 Day
{Open Software Integrators} { www.osintegrators.com} {@osintegrators}
$5,000
14. Hadoop + Neo4j = Bioinformatics Win
Jonathan Freeman
@freethejazz
So what are we
waiting for?
{Open Software Integrators} { www.osintegrators.com} {@osintegrators}
25. Hadoop + Neo4j = Bioinformatics Win
Jonathan Freeman
@freethejazz
Infrastructure for distributed computing
HDFS
MapReduce
A distributed file system.
An implementation of a
programming model for
processing very large data sets.
{Open Software Integrators} { www.osintegrators.com} {@osintegrators}
29. Hadoop + Neo4j = Bioinformatics Win
Jonathan Freeman
@freethejazz
Infrastructure for distributed computing
HDFS
MapReduce
A distributed file system.
An implementation of a
programming model for
processing very large data sets.
{Open Software Integrators} { www.osintegrators.com} {@osintegrators}