Forrester predicts, CIOs who are late to the Hadoop game will finally make the platform a priority in 2015. Hadoop has evolved as a must-to-know technology and has been a reason for better career, salary and job opportunities for many professionals.
Hadoop Demystified + MapReduce (Java and C#), Pig, and Hive DemosLester Martin
A walk-thru of core Hadoop, the ecosystem tools, and Hortonworks Data Platform (HDP) followed by code examples in MapReduce (Java and C#), Pig, and Hive.
Presented at the Atlanta .NET User Group meeting in July 2014.
Hadoop is commonly used for processing large swaths of data in batch. While many of the necessary building blocks for data processing exist within the Hadoop ecosystem – HDFS, MapReduce, HBase, Hive, Pig, Oozie, and so on – it can be a challenge to assemble and operationalize them as a production ETL platform. This presentation covers one approach to data ingest, organization, format selection, process orchestration, and external system integration, based on collective experience acquired across many production Hadoop deployments.
This is the basis for some talks I've given at Microsoft Technology Center, the Chicago Mercantile exchange, and local user groups over the past 2 years. It's a bit dated now, but it might be useful to some people. If you like it, have feedback, or would like someone to explain Hadoop or how it and other new tools can help your company, let me know.
Hadoop is an open source software framework that supports data-intensive distributed applications. Hadoop is licensed under the Apache v2 license. It is therefore generally known as Apache Hadoop. Hadoop has been developed, based on a paper originally written by Google on MapReduce system and applies concepts of functional programming. Hadoop is written in the Java programming language and is the highest-level Apache project being constructed and used by a global community of contributors. Hadoop was developed by Doug Cutting and Michael J. Cafarella. And just don't overlook the charming yellow elephant you see, which is basically named after Doug's son's toy elephant!
The topics covered in presentation are:
1. Big Data Learning Path
2.Big Data Introduction
3. Hadoop and its Eco-system
4.Hadoop Architecture
5.Next Step on how to setup Hadoop
Hadoop Demystified + MapReduce (Java and C#), Pig, and Hive DemosLester Martin
A walk-thru of core Hadoop, the ecosystem tools, and Hortonworks Data Platform (HDP) followed by code examples in MapReduce (Java and C#), Pig, and Hive.
Presented at the Atlanta .NET User Group meeting in July 2014.
Hadoop is commonly used for processing large swaths of data in batch. While many of the necessary building blocks for data processing exist within the Hadoop ecosystem – HDFS, MapReduce, HBase, Hive, Pig, Oozie, and so on – it can be a challenge to assemble and operationalize them as a production ETL platform. This presentation covers one approach to data ingest, organization, format selection, process orchestration, and external system integration, based on collective experience acquired across many production Hadoop deployments.
This is the basis for some talks I've given at Microsoft Technology Center, the Chicago Mercantile exchange, and local user groups over the past 2 years. It's a bit dated now, but it might be useful to some people. If you like it, have feedback, or would like someone to explain Hadoop or how it and other new tools can help your company, let me know.
Hadoop is an open source software framework that supports data-intensive distributed applications. Hadoop is licensed under the Apache v2 license. It is therefore generally known as Apache Hadoop. Hadoop has been developed, based on a paper originally written by Google on MapReduce system and applies concepts of functional programming. Hadoop is written in the Java programming language and is the highest-level Apache project being constructed and used by a global community of contributors. Hadoop was developed by Doug Cutting and Michael J. Cafarella. And just don't overlook the charming yellow elephant you see, which is basically named after Doug's son's toy elephant!
The topics covered in presentation are:
1. Big Data Learning Path
2.Big Data Introduction
3. Hadoop and its Eco-system
4.Hadoop Architecture
5.Next Step on how to setup Hadoop
Transformation Processing Smackdown; Spark vs Hive vs PigLester Martin
Compare and contrast using Spark, Hive and Pig for transformation processing requirements. Video of my "talk" at https://www.youtube.com/watch?v=36_MayK5eU4.
Conference page for the talk is at https://devnexus.com/s/devnexus2017/presentations/17533.
Presentation slides of the workshop on "Introduction to Pig" at Fifth Elephant, Bangalore, India on 26th July, 2012.
http://fifthelephant.in/2012/workshop-pig
Efficient processing of large and complex XML documents in HadoopDataWorks Summit
Many systems capture XML data in Hadoop for analytical processing. When XML documents are large and have complex nested structures, processing such data repeatedly would be inefficient as parsing XML becomes CPU intensive, not to mention the inefficiency of storing XML in its native form. The problem is compounded in the Big Data space, when millions of such documents have to be processed and analyzed within a reasonable time. In this talk an efficient method is proposed by leveraging the Avro storage and communication format, which is flexible, compact and specifically built for Hadoop environments to model complex data structures. XML documents may be parsed and converted into Avro format on load, which can then be accessed via Hive using a SQL-like interface, Java MapReduce or Pig. A concrete use-case is provided that validates this approach along with variations of the same and their relative trade-offs.
Transformation Processing Smackdown; Spark vs Hive vs PigLester Martin
Compare and contrast using Spark, Hive and Pig for transformation processing requirements. Video of my "talk" at https://www.youtube.com/watch?v=36_MayK5eU4.
Conference page for the talk is at https://devnexus.com/s/devnexus2017/presentations/17533.
Presentation slides of the workshop on "Introduction to Pig" at Fifth Elephant, Bangalore, India on 26th July, 2012.
http://fifthelephant.in/2012/workshop-pig
Efficient processing of large and complex XML documents in HadoopDataWorks Summit
Many systems capture XML data in Hadoop for analytical processing. When XML documents are large and have complex nested structures, processing such data repeatedly would be inefficient as parsing XML becomes CPU intensive, not to mention the inefficiency of storing XML in its native form. The problem is compounded in the Big Data space, when millions of such documents have to be processed and analyzed within a reasonable time. In this talk an efficient method is proposed by leveraging the Avro storage and communication format, which is flexible, compact and specifically built for Hadoop environments to model complex data structures. XML documents may be parsed and converted into Avro format on load, which can then be accessed via Hive using a SQL-like interface, Java MapReduce or Pig. A concrete use-case is provided that validates this approach along with variations of the same and their relative trade-offs.
La plateforme OpenData 3.0 pour libérer et valoriser les données Excelerate Systems
Une plateforme pour l’OpenData en mode SaaS,
pour Collecter, Enrichir, Publier et Partager rapidement les données.
La plateforme de mutualisation de services OpenData 3.0 permet aux entreprises publiques et privées de publier leurs données afin de créer de nouvelles possibilités d’usages et de permettre le développement de nouveaux services basés sur l’ouverture des données.
Friction-free ETL: Automating data transformation with Impala | Strata + Hado...Cloudera, Inc.
Speaker: Marcel Kornacker
As data is ingested into Apache Hadoop at an increasing rate from a diverse range of data sources, it is becoming more and more important for users that new data be accessible for analysis as quickly as possible—because “data freshness” can have a direct impact on business results.
In the traditional ETL process, raw data is transformed from the source into a target schema, possibly requiring flattening and condensing, and then loaded into an MPP DBMS. However, this approach has multiple drawbacks that make it unsuitable for real-time, “at-source” analytics—for example, the “ETL lag” reduces data freshness, and the inherent complexity of the process makes it costly to deploy and maintain, and reduces the speed at which new analytic applications can be introduced.
In this talk, attendees will learn about Impala’s approach to on-the-fly, automatic data transformation, which in conjunction with the ability to handle nested structures such as JSON and XML documents, addresses the needs of at-source analytics—including direct querying of your input schema, immediate querying of data as it lands in HDFS, and high performance on par with specialized engines. This performance level is attained in spite of the most challenging and diverse input formats, which are addressed through an automated background conversion process into Parquet, the high-performance, open source columnar format that has been widely adopted across the Hadoop ecosystem.
In this talk, attendees will learn about Impala’s upcoming features that will enable at-source analytics: support for nested structures such as JSON and XML documents, which allows direct querying of the source schema; automated background file format conversion into Parquet, the high-performance, open source columnar format that has been widely adopted across the Hadoop ecosystem; and automated creation of declaratively-specified derived data for simplified data cleansing and transformation.
Webinar: Ways to Succeed with Hadoop in 2015Edureka!
The webinar on Big Data and Hadoop titled " Ways to Succeed with Hadoop in 2015 " conducted by Edureka in association with TechGig.com on 29th December 2014
This was a presentation on my book MapReduce Design Patterns, given to the Twin Cities Hadoop Users Group. Check it out if you are interested in seeing what my my book is about.
This talk was prepared for the November 2013 DataPhilly Meetup: Data in Practice ( http://www.meetup.com/DataPhilly/events/149515412/ )
Map Reduce: Beyond Word Count by Jeff Patti
Have you ever wondered what map reduce can be used for beyond the word count example you see in all the introductory articles about map reduce? Using Python and mrjob, this talk will cover a few simple map reduce algorithms that in part power Monetate's information pipeline
Bio: Jeff Patti is a backend engineer at Monetate with a passion for algorithms, big data, and long walks on the beach. Prior to working at Monetate he performed software R&D for Lockheed Martin, where he worked on projects ranging from social network analysis to robotics.
Forrester predicts, CIOs who are late to the Hadoop game will finally make the platform a priority in 2015. Hadoop has evolved as a must-to-know technology and has been a reason for better career, salary and job opportunities for many professionals.
Forrester predicts, CIOs who are late to the Hadoop game will finally make the platform a priority in 2015. Hadoop has evolved as a must-to-know technology and has been a reason for better career, salary and job opportunities for many professionals.
Dache - a data aware cache system for big-data applications using the MapReduce framework.
Dache aim-extending the MapReduce framework and provisioning a cache layer for efficiently identifying and accessing cache items in a MapReduce job.
LARGE-SCALE DATA PROCESSING USING MAPREDUCE IN CLOUD COMPUTING ENVIRONMENTijwscjournal
The computer industry is being challenged to develop methods and techniques for affordable data processing on large datasets at optimum response times. The technical challenges in dealing with the increasing demand to handle vast quantities of data is daunting and on the rise. One of the recent processing models with a more efficient and intuitive solution to rapidly process large amount of data in parallel is called MapReduce. It is a framework defining a template approach of programming to perform large-scale data computation on clusters of machines in a cloud computing environment. MapReduce provides automatic parallelization and distribution of computation based on several processors. It hides the complexity of writing parallel and distributed programming code. This paper provides a comprehensive systematic review and analysis of large-scale dataset processing and dataset handling challenges and
requirements in a cloud computing environment by using the MapReduce framework and its open-source implementation Hadoop. We defined requirements for MapReduce systems to perform large-scale data processing. We also proposed the MapReduce framework and one implementation of this framework on Amazon Web Services. At the end of the paper, we presented an experimentation of running MapReduce
system in a cloud environment. This paper outlines one of the best techniques to process large datasets is MapReduce; it also can help developers to do parallel and distributed computation in a cloud environment.
In this session, we will introduce “Knitting Boar”, an open-source Java library for performing distributed online learning on a Hadoop cluster under YARN. We will give an overview of how Woven Wabbit works and examine the lessons learned from YARN application construction.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Hadoop Mapreduce Performance Enhancement Using In-Node Combinersijcsit
While advanced analysis of large dataset is in high demand, data sizes have surpassed capabilities of
conventional software and hardware. Hadoop framework distributes large datasets over multiple
commodity servers and performs parallel computations. We discuss the I/O bottlenecks of Hadoop
framework and propose methods for enhancing I/O performance. A proven approach is to cache data to
maximize memory-locality of all map tasks. We introduce an approach to optimize I/O, the in-node
combining design which extends the traditional combiner to a node level. The in-node combiner reduces
the total number of intermediate results and curtail network traffic between mappers and reducers.
While advanced analysis of large dataset is in high demand, data sizes have surpassed capabilities of
conventional software and hardware. Hadoop framework distributes large datasets over multiple
commodity servers and performs parallel computations. We discuss the I/O bottlenecks of Hadoop
framework and propose methods for enhancing I/O performance. A proven approach is to cache data to
maximize memory-locality of all map tasks. We introduce an approach to optimize I/O, the in-node
combining design which extends the traditional combiner to a node level. The in-node combiner reduces
the total number of intermediate results and curtail network traffic between mappers and reducers.
What to learn during the 21 days Lockdown | EdurekaEdureka!
Register Here: https://resources.edureka.co/21-days-learning-plan-webinar/
In light of the complete national lockdown for 21 days, we invite you to join a FREE webinar by renowned Mentor and Advisor, Nitin Gupta as he helps you create a 21-day learning gameplan to maximize returns for your career.
The webinar will help freshers and experienced professionals to capitalize on these 21 days and figure out the best technologies to learn while confined to home.
You will also get all your questions and doubts resolved in real-time.
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Top 10 Dying Programming Languages in 2020 | EdurekaEdureka!
YouTube Link: https://youtu.be/LSM7hD6GM4M
Get Edureka Certified in Trending Programming Languages: https://www.edureka.co
In this highly competitive IT industry, everyone wants to learn programming languages that will keep them ahead of the game. But knowing what to learn so you gain the most out of your knowledge is a whole other ball game. So, we at Edureka have prepared a list of Top 10 Dying Programming Languages 2020 that will help you to make the right choice for your career. Meanwhile, if you ever wondered about which languages are slated for continuing uptake and possible greatness, we have a list for that, too.
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Top 5 Trending Business Intelligence Tools | EdurekaEdureka!
YouTube Link: https://youtu.be/eEwq_mPd1iI
Edureka BI Certification Training Courses: https://www.edureka.co/bi-and-visualization-certification-courses
Receiving insights and finding trends is absolutely critical for businesses to scale and adapt as the years go on. This is exactly what business intelligence does and the best thing about these software solutions is that their potential uses are practically unlimited.
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Tableau Tutorial for Data Science | EdurekaEdureka!
YouTube Link:https://youtu.be/ZHNdSKMluI0
Edureka Tableau Certification Training: https://www.edureka.co/tableau-certification-training
This Edureka's PPT on "Tableau for Data Science" will help you to utilize Tableau as a tool for Data Science, not only for engagement but also comprehension efficiency. Through this PPT, you will learn to gain the maximum amount of insight with the least amount of effort.
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YouTube Link:https://youtu.be/CVv8zhYEjUE
Edureka Python Certification Training: https://www.edureka.co/data-science-python-certification-course
This Edureka PPT on 'Python Programming' will help you learn Python programming basics with the help of interesting hands-on implementations.
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YouTube Link:https://youtu.be/LvgqSMlIXFs
Get Edureka Certified in Trending Project Management Certifications: https://www.edureka.co/project-management-and-methodologies-certification-courses
Whether you want to scale up your career or are trying to switch your career path, Project Management Certifications seems to be a perfect choice in either case. So, we at Edureka have prepared a list of Top 5 Project Management Certifications that you must check out in 2020 for a major career boost.
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Top Maven Interview Questions in 2020 | EdurekaEdureka!
YouTube Link: https://youtu.be/5iTcAR4fScM
**DevOps Certification Courses - https://www.edureka.co/devops-certification-training***
This video on 'Maven Interview Questions' discusses the most frequently asked Maven Interview Questions. This PPT will help give you a detailed explanation of the topics which will help you in acing the interviews.
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** Linux Administration Certification Training - https://www.edureka.co/linux-admin **
Linux Mint is the first operating system that people from Windows or Mac are drawn towards when they have to switch to Linux in their work environment. Linux Mint has been around since the year 2006 and has grown and matured into a very user-friendly OS. Do watch the PPT till the very end to see all the demonstrations.
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How to Deploy Java Web App in AWS| EdurekaEdureka!
YouTube Link:https://youtu.be/Ozc5Yu_IcaI
** Edureka AWS Architect Certification Training - https://www.edureka.co/aws-certification-training**
This Edureka PPT shows how to deploy a java web application in AWS using AWS Elastic Beanstalk. It also describes the advantages of using AWS for this purpose.
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*** Edureka Digital Marketing Course: https://www.edureka.co/post-graduate/digital-marketing-certification***
This Edureka PPT on "Top 10 Reasons to Learn Digital Marketing" will help you understand why you should take up Digital Marketing
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** RPA Training: https://www.edureka.co/robotic-process-automation-training**
This PPT on RPA in 2020 will provide a glimpse of the accomplishments and benefits provided by RPA. Also, it will list out the new changes and technologies that will collaborate with RPA in 2020.
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**DevOps Certification Courses - https://www.edureka.co/devops-certification-training **
This PPT shows how to configure Jenkins to receive email notifications. It also includes a demo that shows how to do it in 6 simple steps in the Windows machine.
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EA Algorithm in Machine Learning | EdurekaEdureka!
YouTube Link: https://youtu.be/DIADjJXrgps
** Machine Learning Certification Training: https://www.edureka.co/machine-learning-certification-training **
This Edureka PPT on 'EM Algorithm In Machine Learning' covers the EM algorithm along with the problem of latent variables in maximum likelihood and Gaussian mixture model.
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PGP in AI and Machine Learning (9 Months Online Program): https://www.edureka.co/post-graduate/machine-learning-and-ai
This Edureka PPT on "Cognitive AI" explains cognitive computing and how it helps in making better human decisions at work. Also, it explains the differences between cognitive computing and artificial intelligence.
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Edureka AWS Architect Certification Training - https://www.edureka.co/aws-certification-training
This Edureka PPT on AWS Cloud Practitioner will provide a complete guide to your AWS Cloud Practitioner Certification exam. It will explain the exam details, objectives, why you should get certified and also how AWS certification will help your career.
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Blue Prism Top Interview Questions | EdurekaEdureka!
YouTube Link: https://youtu.be/ykbRdUNIbyQ
** RPA Training: https://www.edureka.co/robotic-process-automation-certification-courses**
This PPT on Blue Prism Interview Questions will cover the Top 50 Blue Prism related questions asked in your interviews.
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AWS Architect Certification Training: https://www.edureka.co/aws-certification-training
This PPT will help you in understanding how AWS deals smartly with Big Data. It also shows how AWS can solve Big Data challenges with ease.
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A star algorithm | A* Algorithm in Artificial Intelligence | EdurekaEdureka!
YouTube Link: https://youtu.be/amlkE0g-YFU
** Artificial Intelligence and Deep Learning: https://www.edureka.co/ai-deep-learni... **
This Edureka PPT on 'A Star Algorithm' teaches you all about the A star Algorithm, the uses, advantages and disadvantages and much more. It also shows you how the algorithm can be implemented practically and has a comparison between the Dijkstra and itself.
Check out our playlist for more videos: http://bit.ly/2taym8X
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Kubernetes Installation on Ubuntu | EdurekaEdureka!
YouTube Link: https://youtu.be/UWg3ORRRF60
Kubernetes Certification: https://www.edureka.co/kubernetes-certification
This Edureka PPT will help you set up a Kubernetes cluster having 1 master and 1 node. The detailed step by step instructions is demonstrated in this PPT.
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DevOps Training: https://www.edureka.co/devops-certification-training
This Edureka DevOps Tutorial for Beginners talks about What is DevOps and how it works. You will learn about several DevOps tools (Git, Jenkins, Docker, Puppet, Ansible, Nagios) involved at different DevOps stages such as version control, continuous integration, continuous delivery, continuous deployment, continuous monitoring.
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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/
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
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.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
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.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
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.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
2. Slide 2 www.edureka.co/big-data-and-hadoop
Objectives
Analyze different use-cases where MapReduce is used
Differentiate between Traditional way and MapReduce way
Learn about Hadoop 2.x MapReduce architecture and components
Understand execution flow of YARN MapReduce application
Implement basic MapReduce concepts
Run a MapReduce Program
At the end of this module, you will be able to
3. Slide 3 www.edureka.co/big-data-and-hadoop
Where MapReduce is Used?
Weather Forecasting
HealthCare
Problem Statement:
» De-identify personal health information.
Problem Statement:
» Finding Maximum temperature recorded in a year.
4. Slide 4 www.edureka.co/big-data-and-hadoop
Where MapReduce is Used?
MapReduce
FeaturesLarge Scale
Distributed Model
Used in
Function
Design Pattern
Parallel
Programming
A Program Model
Classification
Analytics
Recommendation
Index and Search
Map
Reduce
Classification
Eg: Top N records
Analytics
Eg: Join, Selection
Recommendation
Eg: Sort
Summarization
Eg: Inverted Index
Implemented
Google
Apache Hadoop
HDFS
Pig
Hive
HBase
For
5. Slide 5 www.edureka.co/big-data-and-hadoop
The Traditional Way
Very
Big
Data
Split Data matches
All
matches
grep
grep
grep cat
grep
:
matches
matches
matches
Split Data
Split Data
Split Data
7. Slide 7 www.edureka.co/big-data-and-hadoop
MapReduce Paradigm
The Overall MapReduce Word Count Process
Input Splitting Mapping Shuffling Reducing Final Result
List(K3,V3)
Deer Bear River
Dear Bear River
Car Car River
Deer Car Bear
Bear, 2
Car, 3
Deer, 2
River, 2
Deer, 1
Bear, 1
River, 1
Car, 1
Car, 1
River, 1
Deer, 1
Car, 1
Bear, 1
K2,List(V2)List(K2,V2)
K1,V1
Car Car River
Deer Car Bear
Bear, 2
Car, 3
Deer, 2
River, 2
Bear, (1,1)
Car, (1,1,1)
Deer, (1,1)
River, (1,1)
9. Slide 9 www.edureka.co/big-data-and-hadoop
Why MapReduce?
Two biggest Advantages:
» Taking processing to the data
» Processing data in parallel
a
b
c
Map Task
HDFS Block
Data Center
Rack
Node
10. Slide 10 www.edureka.co/big-data-and-hadoop
ApplicationMaster
» One per application
» Short life
» Coordinates and Manages MapReduce Jobs
» Negotiates with Resource Manager to
schedule tasks
» The tasks are started by NodeManager(s)
Job History Server
» Maintains information about submitted
MapReduce jobs after their ApplicationMaster
terminates
Client
» Submits a MapReduce Job
Resource Manager
» Cluster Level resource manager
» Long Life, High Quality Hardware
Node Manager
» One per Data Node
» Monitors resources on Data Node
Hadoop 2.x MapReduce Components
Container
» Created by NM when requested
» Allocates certain amount of resources
(memory, CPU etc.) on a slave node
14. Slide 14 www.edureka.co/big-data-and-hadoop
HDFS
Application Job Object
Client JVM
Client
Resource
Manager
Management Node
Run Job
2. Get New Application ID
4. Submit Application Context
3. Prepare the
Application submit
context
3.1 App Jar
3.2 Job Resources(Block
locations)
3.3 User Information
1. Notify Start Application
YARN MR Application Execution Flow
15. Slide 15 www.edureka.co/big-data-and-hadoop
HDFS
3. Prepare the
Application submit
context
3.1 App Jar
3.2 Job Resources(Block
locations)
3.3 User Information
Node Manager
5. Start AppMaster container /
Allocate Context for AppMaster
App Master
6.Alloate
Container for
AppMaster
7.Request
Resources
8.Notify with resources
Availability
Data Node
YARN MR Application Execution Flow
Application Job Object
Client JVM
Client
Resource
Manager
Management Node
Run Job
2. Get New Application ID
4. Submit Application Context
1. Notify Start Application
16. Slide 16 www.edureka.co/big-data-and-hadoop
HDFS
Resource
Manager
3. Prepare the Application
submit context
3.1 App Jar
3.2 Job Resources(Block
locations)
3.3 User Information
Management Node
Node Manager
5. Start AppMaster container / Allocate
Context for AppMaster
App Master
6. Allocate
Container for
AppMaster
7.Request
Resources
8.Notify with resources
Availability
Data Node
Client
Node Manager
Data node-1
Node Manager
Map Block
9.Start Container
in the worker node
Data node-2
Node Manager
Map Block
10.NM allocate
Container
10.NM allocate
Container
2. Get New Application
4. Submit Application
1. Notify Start Application
9.Start Container
in the worker
node
YARN MR Application Execution Flow
17. Slide 17 www.edureka.co/big-data-and-hadoop
YARN MR Application Execution Flow
11.Task get Executed.
12.If any reducer in a Job Reducer, again AppMaster Request the Node Manager to start the and Allocate
Container
13.Output of All the Maps given to reducer and Reducer get executed
14.Once Job finished, Application Master notify the Resource Manager and Client Library
15.Application Master closed.
21. Slide 21 www.edureka.co/big-data-and-hadoop
Summary: Application Workflow
Execution Sequence :
1. Client submits an application
2. RM allocates a container to start AM
3. AM registers with RM
Client RM NM AM
1
2
3
22. Slide 22 www.edureka.co/big-data-and-hadoop
Summary: Application Workflow
Execution Sequence :
1. Client submits an application
2. RM allocates a container to start AM
3. AM registers with RM
4. AM asks containers from RM
Client RM NM AM
1
2
3
4
23. Slide 23 www.edureka.co/big-data-and-hadoop
Summary: Application Workflow
Execution Sequence :
1. Client submits an application
2. RM allocates a container to start AM
3. AM registers with RM
4. AM asks containers from RM
5. AM notifies NM to launch containers
Client RM NM AM
1
2
3
4
5
24. Slide 24 www.edureka.co/big-data-and-hadoop
Summary: Application Workflow
Execution Sequence :
1. Client submits an application
2. RM allocates a container to start AM
3. AM registers with RM
4. AM asks containers from RM
5. AM notifies NM to launch containers
6. Application code is executed in container
Client RM NM AM
1
2
3
4
5
6
25. Slide 25 www.edureka.co/big-data-and-hadoop
Summary: Application Workflow
Execution Sequence :
1. Client submits an application
2. RM allocates a container to start AM
3. AM registers with RM
4. AM asks containers from RM
5. AM notifies NM to launch containers
6. Application code is executed in container
7. Client contacts RM/AM to monitor application’s status
Client RM NM AM
1
2
3
4
5
7 6
26. Slide 26 www.edureka.co/big-data-and-hadoop
Summary: Application Workflow
Execution Sequence :
1. Client submits an application
2. RM allocates a container to start AM
3. AM registers with RM
4. AM asks containers from RM
5. AM notifies NM to launch containers
6. Application code is executed in container
7. Client contacts RM/AM to monitor application’s status
8. AM unregisters with RM
Client RM NM AM
1
2
3
4
5
7
8
6
28. Slide 28 www.edureka.co/big-data-and-hadoop
Relation Between Input Splits and HDFS Blocks
1 2 3 4 5 6 7 8 9 10 11
Logical records do not fit neatly into the HDFS blocks.
Logical records are lines that cross the boundary of the blocks.
First split contains line 5 although it spans across blocks.
File
Lines
Block
Boundary
Block
Boundary
Block
Boundary
Block
Boundary
Split Split Split
31. Slide 31 www.edureka.co/big-data-and-hadoop
MapReduce Job Submission Flow
Input data is distributed to nodes
Each map task works on a “split” of data
Mapper outputs intermediate data
Map
Node 1
Map
Node 2
INPUT DATA
32. Slide 32 www.edureka.co/big-data-and-hadoop
MapReduce Job Submission Flow
Input data is distributed to nodes
Each map task works on a “split” of data
Mapper outputs intermediate data
Data exchange between nodes in a “shuffle” process
Map
Node 1
Map
Node 2
Node 1 Node 2
INPUT DATA
33. Slide 33 www.edureka.co/big-data-and-hadoop
MapReduce Job Submission Flow
Input data is distributed to nodes
Each map task works on a “split” of data
Mapper outputs intermediate data
Data exchange between nodes in a “shuffle” process
Intermediate data of the same key goes to the same reducer
Map
Node 1
Map
Node 2
Reduce
Node 1
Reduce
Node 2
INPUT DATA
34. Slide 34 www.edureka.co/big-data-and-hadoop
MapReduce Job Submission Flow
Input data is distributed to nodes
Each map task works on a “split” of data
Mapper outputs intermediate data
Data exchange between nodes in a “shuffle” process
Intermediate data of the same key goes to the same reducer
Reducer output is stored
Map
Node 1
Map
Node 2
Reduce
Node 1
Reduce
Node 2
INPUT DATA
40. Slide 40 www.edureka.co/big-data-and-hadoop
Input file
Input Split Input Split Input Split
Record
Reader
Record
Reader
Record
Reader
Mapper Mapper Mapper
(Intermediates) (Intermediates) (Intermediates)
InputFormat
Input Split
Record
Reader
Mapper
Input file
(Intermediates)
Input Format
41. Slide 41 www.edureka.co/big-data-and-hadoop
Combine File
Input Format<K,V>
Text Input Format
Key Value Text
Input Format
Nline Input Format
Sequence File
Input Format<K,V>
File Input Format
<K,V>
Input Format<K,V>
org.apache.hadoop.mapreduce
<<interface>>
Composable
Input Format
<K,V>
Composite Input Format
<K,V>
DB Input
Format<T>
Sequence File As
Binary Input Format
Sequence File As
Text Input Format
Sequence File Input
Filter<K,V>
Input Format – Class Hierarchy
43. Slide 43 www.edureka.co/big-data-and-hadoop
Text Output Format
<K,V>
Sequence File
Output Format<K,V>
Output Format <K,V>
org.apache.hadoop.mapreduce
DB Output Format
<K,V>
File Output Format
<K,V>
Null Output Format
<K,V>
Filter Output Format
<K,V>
Sequence File As Binary
Output Format
Lazy Output Format
<K,V>
Output Format – Class Hierarchy