This document provides an overview of big data and Apache Hadoop. It defines big data as large and complex datasets that are difficult to process using traditional database management tools. It discusses the sources and growth of big data, as well as the challenges of capturing, storing, searching, sharing, transferring, analyzing and visualizing big data. It describes the characteristics and categories of structured, unstructured and semi-structured big data. The document also provides examples of big data sources and uses Hadoop as a solution to the challenges of distributed systems. It gives a high-level overview of Hadoop's core components and characteristics that make it suitable for scalable, reliable and flexible distributed processing of big data.
Scholars and researchers are being asked by an increasing number of research sponsors and journals to outline how they will manage and share their research data. This is an introduction to data management and sharing practices with some specific information for Columbia University researchers.
Lecture at an event "SEEDS Kick-off meeting", FORS, Lausanne, Switzerland.
Related materials: http://www.snf.ch/en/funding/programmes/scopes/Pages/default.aspx
http://seedsproject.ch/?page_id=368
I will discuss the growth of big data and the evolution of traditional enterprise models with addition of critical building blocks to handle the intense development of data in the enterprise. According to IDC approximations the size of the digital universe in 2011 will be 1.8 zettabytes. With statistics evolution beyond Moore’s Law the average enterprise will need to manage 50 times more information by the year 2020 while cumulative IT team by only 1.5 percent. With this challenge in mind, the combination of big data models into existing enterprise infrastructures is a critical element when seeing the addition of new big data building blocks while bearing in mind the efficiency.
We have entered an era of Big Data. Huge information is for the most part accumulation of information sets so extensive and complex that it is exceptionally hard to handle them utilizing close by database administration devices. The principle challenges with Big databases incorporate creation, curation, stockpiling, sharing, inquiry, examination and perception. So to deal with these databases we require, "exceedingly parallel software's". As a matter of first importance, information is procured from diverse sources, for example, online networking, customary undertaking information or sensor information and so forth. Flume can be utilized to secure information from online networking, for example, twitter. At that point, this information can be composed utilizing conveyed document frameworks, for example, Hadoop File System. These record frameworks are extremely proficient when number of peruses are high when contrasted with composes.
Challenging Problems for Scalable Mining of Heterogeneous Social and Informat...BigMine
In today’s interconnected real world, social and informational entities are interconnected, forming gigantic, interconnected, integrated social and information networks. By structuring these data objects into multiple types, such networks become semi-structured heterogeneous social and information networks. Most real world applications that handle big data, including interconnected social media and social networks, medical information systems, online e-commerce systems, or database systems, can be structured into typed, heterogeneous social and information networks. For example, in a medical care network, objects of multiple types, such as patients, doctors, diseases, medication, and links such as visits, diagnosis, and treatments are intertwined together, providing rich information and forming heterogeneous information networks. Effective analysis of large-scale heterogeneous social and information networks poses an interesting but critical challenge.
In this talk, we present a set of data mining scenarios in heterogeneous social and information networks and show that mining typed, heterogeneous networks is a new and promising research frontier in data mining research. However, such mining may raise some serious challenging problems on scalability computation. We identify a set of problems on scalable computation and calls for serious studies on such problems. This includes how to efficiently computation for (1) meta path-based similarity search, (2) rank-based clustering, (3) rank-based classification, (4) meta path-based link/relationship prediction, and (5) topical hierarchies from heterogeneous information networks. We introduce some recent efforts, discuss the trade-offs between query-independent pre-computation vs. query-dependent online computation, and point out some promising research directions.
All about Big Data components and the best tools to ingest, process, store and visualize the data.
This is a keynote from the series "by Developer for Developers" powered by eSolutionsGrup.
Available techniques in hadoop small file issueIJECEIAES
Hadoop is an optimal solution for big data processing and storing since being released in the late of 2006, hadoop data processing stands on master-slaves manner that’s splits the large file job into several small files in order to process them separately, this technique was adopted instead of pushing one large file into a costly super machine to insights some useful information. Hadoop runs very good with large file of big data, but when it comes to big data in small files it could facing some problems in performance, processing slow down, data access delay, high latency and up to a completely cluster shutting down. In this paper we will high light on one of hadoop’s limitations, that’s affects the data processing performance, one of these limits called “big data in small files” accrued when a massive number of small files pushed into a hadoop cluster which will rides the cluster to shut down totally. This paper also high light on some native and proposed solutions for big data in small files, how do they work to reduce the negative effects on hadoop cluster, and add extra performance on storing and accessing mechanism.
In the past decade a number of technologies have revolutionized the way we do analytics in banking. In this talk we would like to summarize this journey from classical statistical offline modeling to the latest real-time streaming predictive analytical techniques.
In particular, we will look at hadoop and how this distributing computing paradigm has evolved with the advent of in-memory computing. We will introduce Spark, an engine for large-scale data processing optimized for in-memory computing.
Finally, we will describe how to make data science actionable and how to overcome some of the limitations of current batch processing with streaming analytics.
I have collected information for the beginners to provide an overview of big data and hadoop which will help them to understand the basics and give them a Start-Up.
Scholars and researchers are being asked by an increasing number of research sponsors and journals to outline how they will manage and share their research data. This is an introduction to data management and sharing practices with some specific information for Columbia University researchers.
Lecture at an event "SEEDS Kick-off meeting", FORS, Lausanne, Switzerland.
Related materials: http://www.snf.ch/en/funding/programmes/scopes/Pages/default.aspx
http://seedsproject.ch/?page_id=368
I will discuss the growth of big data and the evolution of traditional enterprise models with addition of critical building blocks to handle the intense development of data in the enterprise. According to IDC approximations the size of the digital universe in 2011 will be 1.8 zettabytes. With statistics evolution beyond Moore’s Law the average enterprise will need to manage 50 times more information by the year 2020 while cumulative IT team by only 1.5 percent. With this challenge in mind, the combination of big data models into existing enterprise infrastructures is a critical element when seeing the addition of new big data building blocks while bearing in mind the efficiency.
We have entered an era of Big Data. Huge information is for the most part accumulation of information sets so extensive and complex that it is exceptionally hard to handle them utilizing close by database administration devices. The principle challenges with Big databases incorporate creation, curation, stockpiling, sharing, inquiry, examination and perception. So to deal with these databases we require, "exceedingly parallel software's". As a matter of first importance, information is procured from diverse sources, for example, online networking, customary undertaking information or sensor information and so forth. Flume can be utilized to secure information from online networking, for example, twitter. At that point, this information can be composed utilizing conveyed document frameworks, for example, Hadoop File System. These record frameworks are extremely proficient when number of peruses are high when contrasted with composes.
Challenging Problems for Scalable Mining of Heterogeneous Social and Informat...BigMine
In today’s interconnected real world, social and informational entities are interconnected, forming gigantic, interconnected, integrated social and information networks. By structuring these data objects into multiple types, such networks become semi-structured heterogeneous social and information networks. Most real world applications that handle big data, including interconnected social media and social networks, medical information systems, online e-commerce systems, or database systems, can be structured into typed, heterogeneous social and information networks. For example, in a medical care network, objects of multiple types, such as patients, doctors, diseases, medication, and links such as visits, diagnosis, and treatments are intertwined together, providing rich information and forming heterogeneous information networks. Effective analysis of large-scale heterogeneous social and information networks poses an interesting but critical challenge.
In this talk, we present a set of data mining scenarios in heterogeneous social and information networks and show that mining typed, heterogeneous networks is a new and promising research frontier in data mining research. However, such mining may raise some serious challenging problems on scalability computation. We identify a set of problems on scalable computation and calls for serious studies on such problems. This includes how to efficiently computation for (1) meta path-based similarity search, (2) rank-based clustering, (3) rank-based classification, (4) meta path-based link/relationship prediction, and (5) topical hierarchies from heterogeneous information networks. We introduce some recent efforts, discuss the trade-offs between query-independent pre-computation vs. query-dependent online computation, and point out some promising research directions.
All about Big Data components and the best tools to ingest, process, store and visualize the data.
This is a keynote from the series "by Developer for Developers" powered by eSolutionsGrup.
Available techniques in hadoop small file issueIJECEIAES
Hadoop is an optimal solution for big data processing and storing since being released in the late of 2006, hadoop data processing stands on master-slaves manner that’s splits the large file job into several small files in order to process them separately, this technique was adopted instead of pushing one large file into a costly super machine to insights some useful information. Hadoop runs very good with large file of big data, but when it comes to big data in small files it could facing some problems in performance, processing slow down, data access delay, high latency and up to a completely cluster shutting down. In this paper we will high light on one of hadoop’s limitations, that’s affects the data processing performance, one of these limits called “big data in small files” accrued when a massive number of small files pushed into a hadoop cluster which will rides the cluster to shut down totally. This paper also high light on some native and proposed solutions for big data in small files, how do they work to reduce the negative effects on hadoop cluster, and add extra performance on storing and accessing mechanism.
In the past decade a number of technologies have revolutionized the way we do analytics in banking. In this talk we would like to summarize this journey from classical statistical offline modeling to the latest real-time streaming predictive analytical techniques.
In particular, we will look at hadoop and how this distributing computing paradigm has evolved with the advent of in-memory computing. We will introduce Spark, an engine for large-scale data processing optimized for in-memory computing.
Finally, we will describe how to make data science actionable and how to overcome some of the limitations of current batch processing with streaming analytics.
I have collected information for the beginners to provide an overview of big data and hadoop which will help them to understand the basics and give them a Start-Up.
An Introduction to Big data and problems associated with storing and analyzing big data and How Hadoop solves the problem with its HDFS and MapReduce frameworks. A little intro to HDInsight, Hadoop on windows azure.
Introduction to Cloud computing and Big Data-HadoopNagarjuna D.N
Cloud Computing Evolution
Why Cloud Computing needed?
Cloud Computing Models
Cloud Solutions
Cloud Jobs opportunities
Criteria for Big Data
Big Data challenges
Technologies to process Big Data- Hadoop
Hadoop History and Architecture
Hadoop Eco-System
Hadoop Real-time Use cases
Hadoop Job opportunities
Hadoop and SAP HANA integration
Summary
this presentation describes the company from where I did my summer training and what is bigdata why we use big data, big data challenges, the issue in big data, the solution of big data issues, hadoop, docker , Ansible etc.
A brief intro on the idea of what is Big Data and it's potential. This is primarily a basic study & I have quoted the source of infographics, stats & text at the end. If I have missed any reference due to human error & you recognize another source, please mention.
Big data analytics: Technology's bleeding edgeBhavya Gulati
There can be data without information , but there can not be information without data.
Companies without Big Data Analytics are deaf and dumb , mere wanderers on web.
A Review Paper on Big Data and Hadoop for Data Scienceijtsrd
Big data is a collection of large datasets that cannot be processed using traditional computing techniques. It is not a single technique or a tool, rather it has become a complete subject, which involves various tools, technqiues and frameworks. Hadoop is an open source framework that allows to store and process big data in a distributed environment 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. Mr. Ketan Bagade | Mrs. Anjali Gharat | Mrs. Helina Tandel "A Review Paper on Big Data and Hadoop for Data Science" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29816.pdf Paper URL: https://www.ijtsrd.com/computer-science/data-miining/29816/a-review-paper-on-big-data-and-hadoop-for-data-science/mr-ketan-bagade
AmbushMarketing is a practice of hijacking another advertiser’s campaign. This ppt describes the benefits of doing it in an appropriate manner and why big brands doing this to outrun their competitors through their own current campaigns.
Balsamiq Wireframes is a rapid low-fidelity UI wireframing tool that reproduces the experience of sketching on a notepad or whiteboard but using a computer.
Balsamiq is the first tool out of your UX toolbox: faster than the sketchbook, and almost as fun to use. It forces you to focus on IA the rest can come later.
Balsamiq is the perfect first UX tool for students or anyone interested in getting into UX. It's by far the most approachable and takes you 80% there to pro.
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.
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/
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
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
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.
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
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
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.
2. Objectives
What is Big Data
BIG DATA Challenges
Sources of Big Data challenges
Categories Of 'Big Data'
Characteristics Of 'Big Data’
Live Example
Introduction Of Hadoop
3. Big Data - Definition and Concepts
• Big data is the term for collection of data sets so
large and complex that it become difficult to process
using on – hand database management tool
• Traditionally, “Big Data” = massive volumes of data
– E.g., volume of data at CERN, NASA, Google, …
• Where does the Big Data come from?
– Everywhere! Web logs, GPS systems, sensor networks,
social networks, Internet-based text documents, Internet
search indexes, detail call records, astronomy, atmospheric
science, biology, nuclear physics, biochemical experiments,
medical records, scientific research, military surveillance,
multimedia archives, …
4.
5. Data Explosion
2.5 billion gigabytes of data was generated everyday in 2012.
40,000 search queries search in every second.
300 hours of video is uploaded every min.
31.25 million message sent & 2.77 million video been viewed.
2020 all data will pass through cloud .
6. Technology Insights 6.1
The Data Size Is Getting Big, Bigger, …
• Hadron Collider - 1
PB/sec
• Boeing jet - 20 TB/hr
• Facebook - 500 TB/day
• YouTube – 1 TB/4 min
• The proposed Square
Kilometer Array
telescope (the world’s
proposed biggest
telescope) – 1 EB/day
8. BIG DATA Challenges-
The challenges include :
Capture,
Storage,
Search,
Sharing,
Transfer analysis and
Visualization.
9. Categories Of 'Big Data'
Big data could be found in three forms:
1. Structured
2. Unstructured
3. Semi-structured
10. Structured
Any data that can be stored, accessed and
processed in the form of fixed format is termed
as a 'structured' data.
11. • In semi-structured data, the entities belonging to the
same class may have different attributes even though
they are grouped together.
Semi-structured Data
• Example :
A Word document is generally considered to be unstructured
data. However, you can add metadata tags in the form of
keywords and other metadata that represent the document
content and make it easier for that document to be found when
people search for those terms -- the data is now semi-
structured. Nevertheless, the document still lacks the complex
organization of the database, so falls short of being fully
structured data.
15. Distributed System
A model in which components located on
networked computer communication.
Distributed System use multiple
Machine for a single job.
16. How does a distributed system works ?
1 Machine
Data = 1 Terabyte
Processing time 45 min.
100 Machine
Data = 1 Terabyte
Processing time 47 sec.
17. Challenges of Distributed System
1. Multiple computer are used.
2. High chance of system failure.
3. Limit Bandwidth.
4. Complex Programming.
Solution to all these is Hadoop
19. Hadoop
Doug Cutting is creator of Hadoop.
Hadoop don’t have any meaning – its a
made up name by his kid.
20. Hadoop
Hadoop is a framework that allows for
distributed processing of large data sets across
clusters of commodity computers using simple
programming models.
Hadoop is an open source, java based
programming framework that supports the
processing and storage of extremely large data
sets in a distributed computing environment.
21. Why Hadoop
1. Runs a number of application which involving
Petabyte of data.
2. Has a distributed file system, called HDFS,
which enables fast data transfer among the
nodes or server.
22. Big Data Technologies Hadoop
• Hadoop Technical Components
– Hadoop Distributed File System (HDFS)
– Name Node (primary facilitator)
– Secondary Node (backup to Name Node)
– Job Tracker
– Slave Nodes (the grunts of any Hadoop cluster)
– Additionally, Hadoop ecosystem is made up of a
number of complementary sub-projects: NoSQL
(Cassandra, Hbase), DW (Hive), …
• NoSQL = not only SQL
23. Hadoop Characteristics
1. Economical – ordinary computer can be used for data
processing.
2. Reliable - Stores copies of the data on different machines
and is resistant to hardware failure.
3. Scalable - Hadoop cluster can be extended by just adding
nodes in the cluster.
4. Flexible - Can store a lot of data and decide to use it later.
26. Top 10 Big Data Vendors
with Primary Focus on Hadoop
$0
$10
$20
$30
$40
$50
$60
$70
27. Stream Analytics Applications
• e-Commerce
• Telecommunication
• Law Enforcement and Cyber Security
• Power Industry
• Financial Services
• Health Services
• Government
