The critical thing to remember about Spark and Hadoop is they are not mutually exclusive or inclusive but they work well together and makes the combination strong enough for lots of big data applications.
It is just a basic slides which will give you normal point of view of the big data technologies and tools used in the hadoop technology
It is just a small start to share what I have to share
The critical thing to remember about Spark and Hadoop is they are not mutually exclusive or inclusive but they work well together and makes the combination strong enough for lots of big data applications.
It is just a basic slides which will give you normal point of view of the big data technologies and tools used in the hadoop technology
It is just a small start to share what I have to share
This presentation discusses the following features of Hadoop:
Open source
Fault Tolerance
Distributed Processing
Scalability
Reliability
High Availability
Economic
Flexibility
Easy to use
Data locality
Conclusion
This presentation discusses the follow topics
What is Hadoop?
Need for Hadoop
History of Hadoop
Hadoop Overview
Advantages and Disadvantages of Hadoop
Hadoop Distributed File System
Comparing: RDBMS vs. Hadoop
Advantages and Disadvantages of HDFS
Hadoop frameworks
Modules of Hadoop frameworks
Features of 'Hadoop‘
Hadoop Analytics Tools
Apache Hadoop software library is essentially a framework that
allows for the distributed processing of large data-sets across
clusters of computers using a simple programming model.
A short introduction to Apache Hadoop Hive, what is it and what can it do. How could we use it to connect a Hadoop cluster to business intelligence tools. Then create management reports from our Hadoop cluster data.
Quick Brief about " What is Hadoop"
I didn't explain in detail about hadoop, but reading this slides will give you insight of Hadoop and core product usage. This document will be more useful for PM, Newbies, Technical Architect entering into Cloud Computing.
There is a lot more to Hadoop than Map-Reduce. An increasing number of engineers and researchers involved in processing and analyzing large amount of data, regards Hadoop as an ever expanding ecosystem of open sources libraries, including NoSQL, scripting and analytics tools.
This presentation provides a comprehensive introduction to the Hadoop Distributed System, a powerful and widely used framework for distributed storage and processing of large-scale data. Hadoop has revolutionized the way organizations manage and analyze data, making it a crucial tool in the field of big data and data analytics.
In this presentation, we explore the key components and features of Hadoop, shedding light on the fundamental building blocks that enable its exceptional data processing capabilities. We cover essential topics, including the Hadoop Distributed File System (HDFS), MapReduce, YARN (Yet Another Resource Negotiator), and Hadoop Ecosystem components like Hive, Pig, and Spark.
This presentation discusses the following features of Hadoop:
Open source
Fault Tolerance
Distributed Processing
Scalability
Reliability
High Availability
Economic
Flexibility
Easy to use
Data locality
Conclusion
This presentation discusses the follow topics
What is Hadoop?
Need for Hadoop
History of Hadoop
Hadoop Overview
Advantages and Disadvantages of Hadoop
Hadoop Distributed File System
Comparing: RDBMS vs. Hadoop
Advantages and Disadvantages of HDFS
Hadoop frameworks
Modules of Hadoop frameworks
Features of 'Hadoop‘
Hadoop Analytics Tools
Apache Hadoop software library is essentially a framework that
allows for the distributed processing of large data-sets across
clusters of computers using a simple programming model.
A short introduction to Apache Hadoop Hive, what is it and what can it do. How could we use it to connect a Hadoop cluster to business intelligence tools. Then create management reports from our Hadoop cluster data.
Quick Brief about " What is Hadoop"
I didn't explain in detail about hadoop, but reading this slides will give you insight of Hadoop and core product usage. This document will be more useful for PM, Newbies, Technical Architect entering into Cloud Computing.
There is a lot more to Hadoop than Map-Reduce. An increasing number of engineers and researchers involved in processing and analyzing large amount of data, regards Hadoop as an ever expanding ecosystem of open sources libraries, including NoSQL, scripting and analytics tools.
This presentation provides a comprehensive introduction to the Hadoop Distributed System, a powerful and widely used framework for distributed storage and processing of large-scale data. Hadoop has revolutionized the way organizations manage and analyze data, making it a crucial tool in the field of big data and data analytics.
In this presentation, we explore the key components and features of Hadoop, shedding light on the fundamental building blocks that enable its exceptional data processing capabilities. We cover essential topics, including the Hadoop Distributed File System (HDFS), MapReduce, YARN (Yet Another Resource Negotiator), and Hadoop Ecosystem components like Hive, Pig, and Spark.
The Hadoop tutorial is a comprehensive guide on Big Data Hadoop that covers what is Hadoop, what is the need of Apache Hadoop, why Apache Hadoop is most popular, How Apache Hadoop works?
Presentation regarding big data. The presentation also contains basics regarding Hadoop and Hadoop components along with their architecture. Contents of the PPT are
1. Understanding Big Data
2. Understanding Hadoop & It’s Components
3. Components of Hadoop Ecosystem
4. Data Storage Component of Hadoop
5. Data Processing Component of Hadoop
6. Data Access Component of Hadoop
7. Data Management Component of Hadoop
8.Hadoop Security Management Tool: Knox ,Ranger
Introduction To Hadoop Administration - SpringPeopleSpringPeople
The Hadoop framework is used by major players including Google, Yahoo and IBM, largely for applications involving search engines and advertising. The popularity of Hadoop is juts increasing exponentially.
The Apache Hadoop software library is essentially a framework that allows for the distributed processing of large datasets across clusters of computers using a simple programming model. Hadoop can scale up from single servers to thousands of machines, each offering local computation and storage.
Survey on Performance of Hadoop Map reduce Optimization Methodspaperpublications3
Abstract: Hadoop is a open source software framework for storage and processing large scale of datasets on clusters of commodity hardware. Hadoop provides a reliable shared storage and analysis system, here storage provided by HDFS and analysis provided by MapReduce. MapReduce frameworks are foraying into the domain of high performance of computing with stringent non-functional requirements namely execution times and throughputs. MapReduce provides simple programming interfaces with two functions: map and reduce. The functions can be automatically executed in parallel on a cluster without requiring any intervention from the programmer. Moreover, MapReduce offers other benefits, including load balancing, high scalability, and fault tolerance. The challenge is that when we consider the data is dynamically and continuously produced, from different geographical locations. For dynamically generated data, an efficient algorithm is desired, for timely guiding the transfer of data into the cloud over time for geo-dispersed data sets, there is need to select the best data center to aggregate all data onto given that a MapReduce like framework is most efficient when data to be processed are all in one place, and not across data centers due to the enormous overhead of inter-data center data moving in the stage of shuffle and reduce. Recently, many researchers tend to implement and deploy data-intensive and/or computation-intensive algorithms on MapReduce parallel computing framework for high processing efficiency.
Big Data is a collection of large and complex data sets that cannot be processed using regular database management tools or processing applications. A lot of challenges such as capture, curation, storage, search, sharing, analysis, and visualization can be encountered while handling Big Data. On the other hand the Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Big Data certification is one of the most recognized credentials of today.
For more details Click http://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Presented By :- Rahul Sharma
B-Tech (Cloud Technology & Information Security)
2nd Year 4th Sem.
Poornima University (I.Nurture),Jaipur
www.facebook.com/rahulsharmarh18
AN OVERVIEW OF BIGDATA AND HADOOP . THE ARCHITECHTURE IT USES AND THE WAY IT WORKS ON THE DATA SETS. THE SIDES ALSO SHOW THE VARIOUS FIELDS WHERE THEY ARE MOSTLY USED AND IMPLIMENTED
Similar to WHAT IS HADOOP AND ITS COMPONENTS? (20)
Best Data Analytics Certification Course Training Institute in Malaysia: 360DigiTMG is the best Data Analytics using Python Training Institute In Malaysia providing Data Analytics Training Classes by real-time faculty with course material.
This is the basic endeavor performed by analyzing the information. Information assessment and collection are performed by qualified, arranged, and capable people called information analysts. The information examination methodology is used in various developments.
Best Data Analytics Certification Course Training Institute in Malaysia: 360DigiTMG is the best Data Analytics using Python Training Institute In Malaysia providing Data Analytics Training Classes by real-time faculty with course material.
The field of data analytics keeps on developing quickly as one of the most encouraging vocation ways for exceptionally gifted experts. Data investigators are exceptionally requested by each association as there is enormous measures of data to manage ordinary.
In the event that an individual is profoundly systematic, has solid scientific abilities and is interested and curious simultaneously, at that point he/she is totally appropriate for a profession in data analytics.
360DigiTMG Provides the Supply Chain Analytics Course Training in Malaysia for Working Professionals as well as for students. Learn Supply Chain Analytics from Scratch. Attend Practical Supply Chain Analytics Sessions with Real-Life Projects.
Machine learning has 24 hours of duration.Lecture with a blend of theoretical and practical exposure.
It has learned from the perspective of solving complex. Machine learning training using python programming and R programming.
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.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
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/
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.
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/
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Search and Society: Reimagining Information Access for Radical Futures
WHAT IS HADOOP AND ITS COMPONENTS?
1. WHAT IS HADOOP AND ITS
COMPONENTS?
WHAT IS HADOOP?
Hadoop is owned by the Apache. Hadoop is a framework which is majorly
used for storing and processing the big data. Hadoop is an open-source
software which means that anyone can change its features. The code of the
Hadoop software is openly available. The Hadoop software works in the
distributed computing environment. This environment of Hadoop is known as
Hadoop Distributed File System. The software is written in the Java
programming language.
FEATURES OF HADOOP
Here are some features of the Hadoop software which makes it more
user-friendly. They are listed below-->
● Perfect for the analysis of big data
Hadoop is one of those frameworks which are capable of processing and
storing the big data. If the data is processed in the Hadoop software,
then it will require low bandwidth because the logic of the processing
is transferred to the computing nodes instead of the actual data.
Here, low bandwidth means we can work on the low internet
connection also. This activity increases the efficiency of the
applications built in Hadoop. This concept of Hadoop is known as
the data locality concept.
● Scalability
The scalability means the Hadoop clusters can be extended to any
number of computer nodes. There would not be any problem. For the
extension, we do not need to modify the logic of the applications.
2. ● Fault tolerance
The best feature of the Hadoop framework is its backup system. The Hadoop
software automatically makes the copies of the input data. The data can be
recovered easily in case of system failure.
MODULES OF THE HADOOP FRAMEWORK
Here are the modules of the Hadoop framework. Modules mean the small and
important parts of the Hadoop software. They are listed below-->
● HDFS
HDFS is an important component of the Hadoop framework. What it does is,
it breaks the input data into small pieces and distributes them over the
computer nodes. The Hadoop works on the principle of parallel computing.
● YARN
YARN stands for Yet Another Resource Negotiator. The task of the YARN is
job scheduling and the management of the clusters.
● MapReduce
The MapReduce is a framework which does parallel computing in the Hadoop
with the help of key-value pair. What it does is, it receives the input data
and converts the input data into a data set which can be computed into the
key-value pairs. The output of such input is taken with the help of the
reduce task method.
● Hadoop Common
These are nothing but the Java libraries. These libraries are used for initiating
the Hadoop software. These libraries are also used by the other modules of
the Hadoop software.
ADVANTAGES OF THE HADOOP
Here are some advantages of the Hadoop software. They are listed below-->
● It can prevent system failure.
● It is scalable.
● It is cost-effective.
● The framework is fast.
● It is flexible.
3. CONCLUSION
As said above, Hadoop is used for the management of big data. We know that
at present every sector is working on the big data. The big data has a bright
future and so does the Hadoop. Big data is not possible without frameworks
like Hadoop. Students should learn Hadoop. Many courses are available which
provide a complete course on Hadoop as well as Hadoop certification.
