This is a deck of slides from a recent meetup of AWS Usergroup Greece, presented by Ioannis Konstantinou from the National Technical University of Athens.
The presentation gives an overview of the Map Reduce framework and a description of its open source implementation (Hadoop). Amazon's own Elastic Map Reduce (EMR) service is also mentioned. With the growing interest on Big Data this is a good introduction to the subject.
So you want to get started with Hadoop, but how. This session will show you how to get started with Hadoop development using Pig. Prior Hadoop experience is not needed.
Thursday, May 8th, 02:00pm-02:50pm
More about Hadoop
www.beinghadoop.com
https://www.facebook.com/hadoopinfo
This PPT Gives information about
Complete Hadoop Architecture and
information about
how user request is processed in Hadoop?
About Namenode
Datanode
jobtracker
tasktracker
Hadoop installation Post Configurations
This is a deck of slides from a recent meetup of AWS Usergroup Greece, presented by Ioannis Konstantinou from the National Technical University of Athens.
The presentation gives an overview of the Map Reduce framework and a description of its open source implementation (Hadoop). Amazon's own Elastic Map Reduce (EMR) service is also mentioned. With the growing interest on Big Data this is a good introduction to the subject.
So you want to get started with Hadoop, but how. This session will show you how to get started with Hadoop development using Pig. Prior Hadoop experience is not needed.
Thursday, May 8th, 02:00pm-02:50pm
More about Hadoop
www.beinghadoop.com
https://www.facebook.com/hadoopinfo
This PPT Gives information about
Complete Hadoop Architecture and
information about
how user request is processed in Hadoop?
About Namenode
Datanode
jobtracker
tasktracker
Hadoop installation Post Configurations
Avaliable at: https://github.com/dbsmasters/bdsmasters
The current project is implemented in the context of the course "Big Data Management Systems" taught by Prof. Chatziantoniou in the Department of Management Science and Technology (AUEB). The aim of the project is to familiarize the students with big data management systems such as Hadoop, Redis, MongoDB and Azure Stream Analytics.
Here is our most popular Hadoop Interview Questions and Answers from our Hadoop Developer Interview Guide. Hadoop Developer Interview Guide has over 100 REAL Hadoop Developer Interview Questions with detailed answers and illustrations asked in REAL interviews. The Hadoop Interview Questions listed in the guide are not "might be" asked interview question, they were asked in interviews at least once.
Speaking of big data analysis, what comes to mind is possibly using HDFS and MapReduce within Hadoop. But to write a MapReduce program, one must face the problem of learning how to write native java. One might wonder is it possible to use R, the most popular language adapted by data scientist, to implement MapReduce program? And through the integration or R and Hadoop, is it truly one can unleash the power of parallel computing and big data analysis?
This slide introduces how to install RHadoop step by step, and introduces how to write a MapReduce program through R. What is more, this slide will discuss whether RHadoop is really a light for big data analysis, or just another method to write MapReduce Program.
Please mail me if you found any problem toward the slide. EMAIL: tr.ywchiu@gmail.com
談到巨量資料,通常大家腦海中聯想到的就是使用Hadoop 的 MapReduce 和HDFS,但是撰寫MapReduce,則就必須要學會撰寫Java 或透過Thrift 接口才能撰寫。但R是否有辦法運行在Hadoop 上呢 ? 而使用R + Hadoop,是否就真的能結合R強大的分析功能,分析巨量資料呢 ?
本次講題將介紹如何Step by step 在Hadoop 上安裝RHadoop相關套件,並介紹如何撰寫R的MapReduce 程式。更重要的是,此次將探討使用RHadoop 是否為巨量資料分析找到一盞明燈? 或者只是另一套實作方法而已?
This presentation will give you Information about :
1. Map/Reduce Overview and Architecture Installation
2. Developing Map/Red Jobs Input and Output Formats
3. Job Configuration Job Submission
4. Practicing Map Reduce Programs (atleast 10 Map Reduce
5. Algorithms )Data Flow Sources and Destinations
6. Data Flow Transformations Data Flow Paths
7. Custom Data Types
8. Input Formats
9. Output Formats
10. Partitioning Data
11. Reporting Custom Metrics
12. Distributing Auxiliary Job Data
Developing and Deploying Apps with the Postgres FDWJonathan Katz
I couldn't wait to use the Postgres Foreign Data Wrapper (postgres_fdw) in a project; imagine being able to read and write data to many databases all from a single database! I finally found a project where it made sense to use this amazing technology.
I mapped out my architecture and began to code, and realized there were some things that did not work as expected: I could not call remote functions or insert into a table with a serial primary key and have it autoupdate. I found workarounds (which I will share), so the project went on.
We tested the setup, everything seemed to work well, and then we went to deploy to production. And then the real fun began.
Despite the title, I still love the Postgres FDW but wanted to provide some cautionary tales from a hybrid developer/DBA perspective on how to properly use them in your working environment. This talk will cover:
* Basic Postgres FDW setup in a development environment vs. production environment
* Handling some common FDW uses case that you think are trivial but are not
* Working with advanced Postgres constructs such as schemas and sequences with FDWs
* Putting it all together to make sure your production application is safe with your FDWs
* ...and when you really, really need to make a remote call and it is not supported by a FDW, how to do that too!
A tutorial presentation based on hadoop.apache.org documentation.
I gave this presentation at Amirkabir University of Technology as Teaching Assistant of Cloud Computing course of Dr. Amir H. Payberah in spring semester 2015.
Avaliable at: https://github.com/dbsmasters/bdsmasters
The current project is implemented in the context of the course "Big Data Management Systems" taught by Prof. Chatziantoniou in the Department of Management Science and Technology (AUEB). The aim of the project is to familiarize the students with big data management systems such as Hadoop, Redis, MongoDB and Azure Stream Analytics.
Here is our most popular Hadoop Interview Questions and Answers from our Hadoop Developer Interview Guide. Hadoop Developer Interview Guide has over 100 REAL Hadoop Developer Interview Questions with detailed answers and illustrations asked in REAL interviews. The Hadoop Interview Questions listed in the guide are not "might be" asked interview question, they were asked in interviews at least once.
Speaking of big data analysis, what comes to mind is possibly using HDFS and MapReduce within Hadoop. But to write a MapReduce program, one must face the problem of learning how to write native java. One might wonder is it possible to use R, the most popular language adapted by data scientist, to implement MapReduce program? And through the integration or R and Hadoop, is it truly one can unleash the power of parallel computing and big data analysis?
This slide introduces how to install RHadoop step by step, and introduces how to write a MapReduce program through R. What is more, this slide will discuss whether RHadoop is really a light for big data analysis, or just another method to write MapReduce Program.
Please mail me if you found any problem toward the slide. EMAIL: tr.ywchiu@gmail.com
談到巨量資料,通常大家腦海中聯想到的就是使用Hadoop 的 MapReduce 和HDFS,但是撰寫MapReduce,則就必須要學會撰寫Java 或透過Thrift 接口才能撰寫。但R是否有辦法運行在Hadoop 上呢 ? 而使用R + Hadoop,是否就真的能結合R強大的分析功能,分析巨量資料呢 ?
本次講題將介紹如何Step by step 在Hadoop 上安裝RHadoop相關套件,並介紹如何撰寫R的MapReduce 程式。更重要的是,此次將探討使用RHadoop 是否為巨量資料分析找到一盞明燈? 或者只是另一套實作方法而已?
This presentation will give you Information about :
1. Map/Reduce Overview and Architecture Installation
2. Developing Map/Red Jobs Input and Output Formats
3. Job Configuration Job Submission
4. Practicing Map Reduce Programs (atleast 10 Map Reduce
5. Algorithms )Data Flow Sources and Destinations
6. Data Flow Transformations Data Flow Paths
7. Custom Data Types
8. Input Formats
9. Output Formats
10. Partitioning Data
11. Reporting Custom Metrics
12. Distributing Auxiliary Job Data
Developing and Deploying Apps with the Postgres FDWJonathan Katz
I couldn't wait to use the Postgres Foreign Data Wrapper (postgres_fdw) in a project; imagine being able to read and write data to many databases all from a single database! I finally found a project where it made sense to use this amazing technology.
I mapped out my architecture and began to code, and realized there were some things that did not work as expected: I could not call remote functions or insert into a table with a serial primary key and have it autoupdate. I found workarounds (which I will share), so the project went on.
We tested the setup, everything seemed to work well, and then we went to deploy to production. And then the real fun began.
Despite the title, I still love the Postgres FDW but wanted to provide some cautionary tales from a hybrid developer/DBA perspective on how to properly use them in your working environment. This talk will cover:
* Basic Postgres FDW setup in a development environment vs. production environment
* Handling some common FDW uses case that you think are trivial but are not
* Working with advanced Postgres constructs such as schemas and sequences with FDWs
* Putting it all together to make sure your production application is safe with your FDWs
* ...and when you really, really need to make a remote call and it is not supported by a FDW, how to do that too!
A tutorial presentation based on hadoop.apache.org documentation.
I gave this presentation at Amirkabir University of Technology as Teaching Assistant of Cloud Computing course of Dr. Amir H. Payberah in spring semester 2015.
React in Native Apps - Meetup React - 20150409Minko3D
Minko is a platform to display, share and interact with 3D content from anywhere, whether you're on a web browser, a workstation, or a device that fits in your pocket. In order to reach those targets with the team we have, we had to go with a cross-platform, hybrid solution that would enable fast UI development with native 3D performances. So we built one, on top of many open source projects, using C++. This talk will discuss our approach for building cross-platform HTML5 interfaces, with a C++/JS bridge to bind DOM APIs, and the tricks we use to make them responsive (spoiler: React is one of them).
Ionic adventures - Hybrid Mobile App Development rocksJuarez Filho
Ionic frameworks is the new kid on the block related to Hybrid Mobile Apps created by Drifty and rapidly growth with a variety of tools like ionic lab, ionic creator, ionic view, ionic crosswalk integration and other exciting tools is coming this year like ionic PUSH.
Check this presentation to have a short getting start in this amazing framework.
Let's create amazing apps with Ionic. \o/
An overview of MongoDB's GridFS system for storing dynamic assets without the filesystem. I show the problems with the filesystem, the advantages of GridFS and how it compares to using AWS S3.
MongoDB and Hadoop: Driving Business InsightsMongoDB
MongoDB and Hadoop can work together to solve big data problems facing today's enterprises. We will take an in-depth look at how the two technologies complement and enrich each other with complex analyses and greater intelligence. We will take a deep dive into the MongoDB Connector for Hadoop and how it can be applied to enable new business insights with MapReduce, Pig, and Hive, and demo a Spark application to drive product recommendations.
MongoDB Days Silicon Valley: MongoDB and the Hadoop ConnectorMongoDB
Presented by Luke Lovett, Software Engineer, MongoDB
Experience level: Introductory
MongoDB and Hadoop work powerfully together as complementary technologies. Learn how the Hadoop connector allows you to leverage the power of MapReduce to process data sourced from your MongoDB cluster.
Mongo db and hadoop driving business insights - finalMongoDB
MongoDB and Hadoop can work together to solve big data problems facing today's enterprises. We will take an in-depth look at how the two technologies complement and enrich each other with complex analyses and greater intelligence. We will take a deep dive into the MongoDB Connector for Hadoop and how it can be applied to enable new business insights with MapReduce, Pig, and Hive, and demo a Spark application to drive product recommendations.
Sharding allows you to distribute load across multiple servers and keep your data balanced across those servers. This session will review MongoDB’s sharding support, including an architectural overview, design principles, and automation.
Introduction to Hadoop Developer Training WebinarCloudera, Inc.
Are you new to Hadoop and need to start processing data fast and effectively? Have you been playing with CDH and are ready to move on to development supporting a technical or business use case? Are you prepared to unlock the full potential of all your data by building and deploying powerful Hadoop-based applications?
If you're wondering whether Cloudera's Developer Training for Apache Hadoop is right for you and your team, then this presentation is right for you. You will learn who is best suited to attend the live training, what prior knowledge you should have, and what topics the course covers. Cloudera Curriculum Manager, Ian Wrigley, will discuss the skills you will attain during the course and how they will help you become a full-fledged Hadoop application developer.
During the session, Ian will also present a short portion of the actual Cloudera Developer course, discussing the difference between New and Old APIs, why there are different APIs, and which you should use when writing your MapReduce code. Following the presentation, Ian will answer your questions about this or any of Cloudera’s other training courses.
Visit the resources section of cloudera.com to view the on-demand webinar.
Hadoop Online training by certified Lead Architect. This a a very real time training with lots of real time discussion and live troubleshooting sessions. Ideal for Admins and Developers. Basic Java session is included for those not familiar with Java terms.
-Sessions Recordings are available .
- 50 + Exercises session covering HDFS, MR , Pig, HBase, Hive and AWS-EMR.
- Free Virtual Machine provided
- Join Linked In session by Trainer to stay in touch even after the training.
Introduction to Sqoop Aaron Kimball Cloudera Hadoop User Group UKSkills Matter
In this talk of Hadoop User Group UK meeting, Aaron Kimball from Cloudera introduces Sqoop, the open source SQL-to-Hadoop tool. Sqoop helps users perform efficient imports of data from RDBMS sources to Hadoop's distributed file system, where it can be processed in concert with other data sources. Sqoop also allows users to export Hadoop-generated results back to an RDBMS for use with other data pipelines.
After this session, users will understand how databases and Hadoop fit together, and how to use Sqoop to move data between these systems. The talk will provide suggestions for best practices when integrating Sqoop and Hadoop in your data processing pipelines. We'll also cover some deeper technical details of Sqoop's architecture, and take a look at some upcoming aspects of Sqoop's development roadmap.
Using GPUs to Handle Big Data with JavaTim Ellison
A copy of the slides presented at JavaOne conference 2014.
Learn how Java can exploit the power of graphics processing units (GPUs) to optimize high-performance enterprise and technical computing applications such as big data and analytics workloads. This presentation covers principles and considerations for GPU programming from Java and looks at the software stack and developer tools available. It also presents a demo showing GPU acceleration and discusses what is coming in the future.
EuroPython 2015 - Big Data with Python and HadoopMax Tepkeev
Big Data - these two words are heard so often nowadays. But what exactly is Big Data ? Can we, Pythonistas, enter the wonder world of Big Data ? The answer is definitely “Yes”.
This talk is an introduction to the big data processing using Apache Hadoop and Python. We’ll talk about Apache Hadoop, it’s concepts, infrastructure and how one can use Python with it. We’ll compare the speed of Python jobs under different Python implementations, including CPython, PyPy and Jython and also discuss what Python libraries are available out there to work with Apache Hadoop.
Processing massive amount of data with Map Reduce using Apache Hadoop - Indi...IndicThreads
Session presented at the 2nd IndicThreads.com Conference on Cloud Computing held in Pune, India on 3-4 June 2011.
http://CloudComputing.IndicThreads.com
Abstract: The processing of massive amount of data gives great insights into analysis for business. Many primary algorithms run over the data and gives information which can be used for business benefits and scientific research. Extraction and processing of large amount of data has become a primary concern in terms of time, processing power and cost. Map Reduce algorithm promises to address the above mentioned concerns. It makes computing of large sets of data considerably easy and flexible. The algorithm offers high scalability across many computing nodes. This session will introduce Map Reduce algorithm, followed by few variations of the same and also hands on example in Map Reduce using Apache Hadoop.
Speaker: Allahbaksh Asadullah is a Product Technology Lead from Infosys Labs, Bangalore. He has over 5 years of experience in software industry in various technologies. He has extensively worked on GWT, Eclipse Plugin development, Lucene, Solr, No SQL databases etc. He speaks at the developer events like ACM Compute, Indic Threads and Dev Camps.
Hadoop became the most common systm to store big data.
With Hadoop, many supporting systems emerged to complete the aspects that are missing in Hadoop itself.
Together they form a big ecosystem.
This presentation covers some of those systems.
While not capable to cover too many in one presentation, I tried to focus on the most famous/popular ones and on the most interesting ones.
Big Data Europe: Simplifying Development and Deployment of Big Data ApplicationsBigData_Europe
Presentation at MSD IT Global Innovation Center in Prague, Czech Republic. Covers the technical outcomes of horizon2020 BigDataEurope project and provides and example of a component integration into the BDI platform.
GraphQL can be one of the best ways to make your product development more fun and productive. In this presentation I talk about how GraphQL makes your life simpler, and how to write and deploy a GraphQL API with Apollo Server 2.0 and serverless deployment via Netlify Functions.
Introduction to Cloud Computing with Google Cloudwesley chun
This is a 20-30 minute technical talk introducing developers to cloud computing including an overview of Google Cloud computing products. There is a special focus on serverless tools as a convenient way for developers to run code. The talk ends with several inspirational apps showcasing what is possible with Google Cloud tools meant to plant a seed as to consider what is possible.
Hadoop became the most common systm to store big data.
With Hadoop, many supporting systems emerged to complete the aspects that are missing in Hadoop itself.
Together they form a big ecosystem.
This presentation covers some of those systems.
While not capable to cover too many in one presentation, I tried to focus on the most famous/popular ones and on the most interesting ones.
Datascience Training with Hadoop, Python Machine Learning & Scala, SparkSequelGate
Hadoop Data Science Training
Microsoft consulted data scientists and the companies that employ them to identify the core skills they need to be successful. This informed the curriculum used to teach key functional and technical skills, combining highly rated online courses with hands-on labs, concluding in a final capstone project.
Similar to MongoDB & Hadoop: Flexible Hourly Batch Processing Model (20)
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
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.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
42. m = function(){
this.tags.forEach{
function(z) {
emit(z, {count: 1});
}
};
};
r = function(key, values) {
var total=0;
for (i=0, i<values.length, i++)
total += values[i].count;
return { count : total };
}
res=db.things.mapReduce(m,!r);
# finalize
43.
44.
45.
46.
47.
48.
49. Examples
Conclusions and Future Work
Party Solutions
Motivation
Architecture
Examples
Conclusions and Future Work
ummary of Features
Hadoop-based: same limitations as Streaming (Dumbo) and
Streaming Jython Pydoop
Jython (Happy), except for ease of use
C/C++ Ext Yes No Yes
Other implementations: good if you have your own cluster
Standard Lib Full Partial Full
Hadoop is the most widespread implementation
MR API No* Full Partial
Java-like FW No Yes Yes
HDFS No
Leo, Zanetti
Yes Yes
Pydoop: a Python MapReduce and HDFS API for Hadoop
(*) you can only write the map and reduce parts as executable scripts.
50. Motivation
Architecture
Examples
Conclusions and Future Work
Hadoop Pipes
Communication with Java
framework via persistent
sockets
The C++ app provides a
factory used by the framework
to create MR components
Providing Mapper and
Reducer is mandatory
Leo, Zanetti Pydoop: a Python MapReduce and HDFS API for Hadoop
51. Motivation
Architecture
Examples
Conclusions and Future Work
Integration of Pydoop with C++
Integration with Pipes:
Method calls flow from the
framework through the C++ and the
Pydoop API, ultimately reaching
user-defined methods
Results are wrapped by Boost and
returned to the framework
Integration with HDFS:
Function calls initiated by Pydoop
Results wrapped and returned as
Python objects to the app