This document provides an overview of big data and Hadoop. It discusses the scale of big data, noting that Facebook handles 180PB per year and Twitter handles 1.2 million tweets per second. It also covers the volume, variety, and velocity challenges of big data. Hadoop and MapReduce are introduced as the leading solutions for distributed storage and processing of big data using a scale-out architecture. Key ideas of Hadoop include storing large data across multiple machines in HDFS and processing that data in parallel using MapReduce jobs.
“BIG DATA” is data that is big in
volume
velocity and
Variety
“TODAY’S BIG MAY BE TOMMOROW’S NORMAL”
Varieties deals with a wide range of data types
Structured data - RDMS
Semi – structured data – HTML,XML
Unstructured data – audios, videos, emails, photos, pdf, social media
hadoop
It was created by DOUG CUTTING and MICHEAL CAFARELLA in 2005
2003 – NUTCH open source search engine( lucene ,sphinx ,etc…)
(google published some papers mentioning about DFS and MAP REDUCE)
After yahoo took this initiative step
Then the creation of hadoop took place
Hadoop 0.1.0 was relesed april 2006
As of now hadoop 2.8 is available
The document discusses the importance of non-functional requirements (NFRs) in software development. It notes that NFRs such as performance, reliability, and usability must be defined, tested, and validated throughout the development lifecycle. Ignoring NFRs can negatively impact the cost, timeline, and ultimate success of a project. The document provides examples of different types of NFRs and urges considering stakeholders' perspectives to prioritize the most important NFRs to test.
This document is the first deliverable of the Lean Big Data work package 7 (WP7). The main goal of the package 7 is to provide the use cases applications that will be used to validate the Lean Big Data platform. To this end, an analysis of requirement of each use case will be provided in the scope.This analysis will be used as basis for the description of the evaluation, benchmarking and validation of the Lean Big Data platform.
This deliverable comprises the analysis of requirements for the following case of study provided in the context of Lean Big Data: Data Centre monitoring Case Study, Electronic Alignment of Direct Debit transactions Case Study, Social Network-based Area surveillance Case Study and Targeted Advertisement Case Study.
Simply Business is a leading insurance provider for small business in the UK and we are now growing to the USA. In this presentation, I explain how our data platform is evolving to keep delivering value and adapting to a company that changes really fast.
The Double win business transformation and in-year ROI and TCO reductionMongoDB
This document discusses how modern information management with flexible data platforms like MongoDB can help businesses transform and drive ROI through cost reduction and increased productivity compared to legacy systems. It provides examples of strategic areas where MongoDB can modernize an organization's full technology stack from data in motion/at rest to apps, compute, storage and networks. Success stories show how MongoDB has helped companies like Barclays reduce costs and complexity while improving resiliency, agility and innovation.
This document discusses big data and Hadoop. It defines big data as large data sets that cannot be processed by traditional software tools within a reasonable time frame due to the volume and variety of data. It then describes the three V's of big data - volume, velocity, and variety. The document provides examples of sources of big data and discusses how Hadoop, an open-source software framework, can be used to manage and analyze big data through its core components - HDFS for storage and MapReduce for processing. Finally, it provides a high-level overview of how MapReduce works.
This document provides an overview of big data and Hadoop. It discusses the scale of big data, noting that Facebook handles 180PB per year and Twitter handles 1.2 million tweets per second. It also covers the volume, variety, and velocity challenges of big data. Hadoop and MapReduce are introduced as the leading solutions for distributed storage and processing of big data using a scale-out architecture. Key ideas of Hadoop include storing large data across multiple machines in HDFS and processing that data in parallel using MapReduce jobs.
“BIG DATA” is data that is big in
volume
velocity and
Variety
“TODAY’S BIG MAY BE TOMMOROW’S NORMAL”
Varieties deals with a wide range of data types
Structured data - RDMS
Semi – structured data – HTML,XML
Unstructured data – audios, videos, emails, photos, pdf, social media
hadoop
It was created by DOUG CUTTING and MICHEAL CAFARELLA in 2005
2003 – NUTCH open source search engine( lucene ,sphinx ,etc…)
(google published some papers mentioning about DFS and MAP REDUCE)
After yahoo took this initiative step
Then the creation of hadoop took place
Hadoop 0.1.0 was relesed april 2006
As of now hadoop 2.8 is available
The document discusses the importance of non-functional requirements (NFRs) in software development. It notes that NFRs such as performance, reliability, and usability must be defined, tested, and validated throughout the development lifecycle. Ignoring NFRs can negatively impact the cost, timeline, and ultimate success of a project. The document provides examples of different types of NFRs and urges considering stakeholders' perspectives to prioritize the most important NFRs to test.
This document is the first deliverable of the Lean Big Data work package 7 (WP7). The main goal of the package 7 is to provide the use cases applications that will be used to validate the Lean Big Data platform. To this end, an analysis of requirement of each use case will be provided in the scope.This analysis will be used as basis for the description of the evaluation, benchmarking and validation of the Lean Big Data platform.
This deliverable comprises the analysis of requirements for the following case of study provided in the context of Lean Big Data: Data Centre monitoring Case Study, Electronic Alignment of Direct Debit transactions Case Study, Social Network-based Area surveillance Case Study and Targeted Advertisement Case Study.
Simply Business is a leading insurance provider for small business in the UK and we are now growing to the USA. In this presentation, I explain how our data platform is evolving to keep delivering value and adapting to a company that changes really fast.
The Double win business transformation and in-year ROI and TCO reductionMongoDB
This document discusses how modern information management with flexible data platforms like MongoDB can help businesses transform and drive ROI through cost reduction and increased productivity compared to legacy systems. It provides examples of strategic areas where MongoDB can modernize an organization's full technology stack from data in motion/at rest to apps, compute, storage and networks. Success stories show how MongoDB has helped companies like Barclays reduce costs and complexity while improving resiliency, agility and innovation.
This document discusses big data and Hadoop. It defines big data as large data sets that cannot be processed by traditional software tools within a reasonable time frame due to the volume and variety of data. It then describes the three V's of big data - volume, velocity, and variety. The document provides examples of sources of big data and discusses how Hadoop, an open-source software framework, can be used to manage and analyze big data through its core components - HDFS for storage and MapReduce for processing. Finally, it provides a high-level overview of how MapReduce works.
Hadoop World 2011: Hadoop’s Life in Enterprise Systems - Y Masatani, NTTDataCloudera, Inc.
NTT DATA has been providing Hadoop professional services for enterprise customers for years. In this talk we will categorize Hadoop integration cases based on our experience and illustrate archetypal design practices how Hadoop clusters are deployed into existing infrastructure and services. We will also present enhancement cases motivated by customer’s demand including GPU for big math, HDFS capable storage system, etc.
Big Data with Hadoop – For Data Management, Processing and StoringIRJET Journal
This document discusses big data and Hadoop. It begins with defining big data and explaining its characteristics of volume, variety, velocity, and veracity. It then provides an overview of Hadoop, describing its core components of HDFS for storage and MapReduce for processing. Key technologies in Hadoop's ecosystem are also summarized like Hive, Pig, and HBase. The document concludes by outlining some challenges of big data like issues of heterogeneity and incompleteness of data.
Transaction Processing Systems (TPS) collect, store, and modify data from daily business transactions. TPS have features like rapid response, reliability, and inflexibility as they treat all transactions equally. There are two main types of TPS - batch processing, where data is collected and processed later, and real-time processing, where data is processed immediately. Data warehouses are large databases used to support management decision making through analysis of historical data from various sources.
This document discusses opportunities for using big data in private wealth management. It begins by defining big data and describing how data volumes have increased exponentially. It then outlines several potential use cases for big data in areas like real-time performance metrics, portfolio optimization, and leveraging customer data. For each use case, it describes current limitations and how a big data approach could enable new capabilities. Finally, it proposes a phased approach for wealth managers to identify use cases, prioritize them, implement proofs of concept, and incrementally automate analysis and reporting. The overall message is that big data can enhance analytics and open up new opportunities previously only available to investment banks.
This document summarizes a summer training seminar on BigData Hadoop that was attended. The training was provided by LinuxWorld Informatics Pvt Ltd, which offers open source and commercial training programs. The attendee learned about Hadoop, MapReduce, single and multi-node clusters, Docker, and Ansible. Big data challenges related to volume, variety, velocity, and veracity of data were also covered. Hadoop and its core components HDFS and MapReduce were explained as solutions for storing and processing large datasets in a distributed manner across commodity hardware. Docker containers were introduced as a lightweight alternative to virtual machines.
The document discusses Microsoft's Windows Azure Platform, including its pricing models, workload patterns suited for cloud computing, channel models, and support offerings. It provides details on consumption pricing for computing, storage, databases, and data transfer. Promotional offers and programs are also summarized, along with the monthly SLA and worldwide coverage of the platform.
This document discusses big data, including what it is, common data sources, its volume, velocity and variety characteristics, solutions like Hadoop and its HDFS and MapReduce components, and the impact and future of big data. It explains that big data refers to large and complex datasets that are difficult to process using traditional tools. Hadoop provides a framework to store and process big data across clusters of commodity hardware.
This document discusses different architectures for big data systems, including traditional, streaming, lambda, kappa, and unified architectures. The traditional architecture focuses on batch processing stored data using Hadoop. Streaming architectures enable low-latency analysis of real-time data streams. Lambda architecture combines batch and streaming for flexibility. Kappa architecture avoids duplicating processing logic. Finally, a unified architecture trains models on batch data and applies them to real-time streams. Choosing the right architecture depends on use cases and available components.
Big Data Architectures @ JAX / BigDataCon 2016Guido Schmutz
Mit der Architektur steht und fällt jedes IT-Projekt. Das gilt in noch stärkerem Maße für Big-Data-Projekte, denn hier konnten noch keine Standards über Jahrzehnte ihre Tauglichkeit beweisen. Dennoch verbreiten und etablieren sich auch hier gute und effektive Lösungen. Der Vortrag erklärt, welche Bausteine wichtig für die verschiedenen Einsatzmöglichkeiten im Big-Data-Umfeld sind, und wie sie in konkrete Lösungen gegossen werden können. Dabei beleuchtet er sowohl traditionelle Big-Data-Architekturen als auch aktuelle Ansätze, wie z. B. die Lambda- und die Kappa-Architektur. Ebenfalls ein Thema sind Stream-Processing-Infrastrukturen und ihre Kombination mit Big-Data-Technologien. Ausgehend von einer produkt- und technologieunabhängigen Referenzarchitektur stellt dieser Vortrag verschiedene Lösungsmöglichkeiten auf Basis von Open-Source-Komponenten vor.
Lean Enterprise, Microservices and Big DataStylight
This document discusses enabling the lean enterprise through technologies like microservices, continuous integration/deployment, and cloud computing. It begins by defining the lean enterprise and the OODA loop concept. It then explains how technologies like AWS, big data, and microservices can help organizations continuously observe, orient, decide, and act. Specific AWS services like EC2, EMR, Kinesis, Redshift, S3, and DynamoDB are reviewed. The benefits of breaking up monolithic systems into microservices and implementing devops practices like CI/CD are also summarized.
This document discusses smart apps and how Pivotal uses data science to build them. It describes three key components of smart apps: data, a smart system that uses data science to understand user behavior, and a user interface. It then provides examples of smart apps Pivotal has developed for logistics and automotive customers, describing how machine learning models were used to predict delivery locations and road conditions. The document emphasizes an API-first approach and using cloud platforms like Cloud Foundry to operationalize models and deliver insights through predictive APIs.
This document discusses the concept of big data. It defines big data as massive volumes of structured and unstructured data that are difficult to process using traditional database techniques due to their size and complexity. It notes that big data has the characteristics of volume, variety, and velocity. The document also discusses Hadoop as an implementation of big data and how various industries are generating large amounts of data.
IT overview for nonprofits by Dave Cortright (IT4NP)Dave Cortright
Information, tips and resources for managing the tech side of the org. Presented to The Greater Sum virtual incubator in July 2020. For more info check out http://it4np.com
Splunk has helped MetroPCS speed up troubleshooting and launch new products faster by allowing them to ingest and analyze call detail records and other network data. It was deployed within 2 weeks and has provided unexpected benefits like subpoena compliance and understanding overall system health. Splunk insights have helped MetroPCS optimize call routing to save hundreds of thousands in costs.
Data Engineer's Lunch #85: Designing a Modern Data StackAnant Corporation
What are the design considerations that go into architecting a modern data warehouse? This presentation will cover some of the requirements analysis, design decisions, and execution challenges of building a modern data lake/data warehouse.
Cassandra & puppet, scaling data at $15 per monthdaveconnors
Constant Contact shares lessons learned from DevOps approach to implementing Cassandra to manage social media data for over 400k small business customers. Puppet is the critical in our tool chain. Single most important factor was the willingness of Development and Operations to stretch beyond traditional roles and responsibilities.
A very categorized presentation about big data analytics Various topics like Introduction to Big Data,Hadoop,HDFS Map Reduce, Mahout,K-means Algorithm,H-Base are explained very clearly in simple language for everyone to understand easily.
AquaQ Analytics Kx Event - Data Direct Networks PresentationAquaQ Analytics
This document discusses using DDN's parallel file systems to improve the performance of kdb+ analytics queries on large datasets. Running kdb+ on a parallel file system can significantly reduce query latency by distributing data and queries across multiple file system servers. This allows queries to achieve near linear speedups as more servers are added. The shared namespace also allows multiple independent kdb+ instances to access the same consolidated datasets.
Graspan: A Big Data System for Big Code AnalysisAftab Hussain
We built a disk-based parallel graph system, Graspan, that uses a novel edge-pair centric computation model to compute dynamic transitive closures on very large program graphs.
We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations.
These analyses were used to augment the existing checkers; these augmented checkers found 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18.
- Accepted in ASPLOS ‘17, Xi’an, China.
- Featured in the tutorial, Systemized Program Analyses: A Big Data Perspective on Static Analysis Scalability, ASPLOS ‘17.
- Invited for presentation at SoCal PLS ‘16.
- Invited for poster presentation at PLDI SRC ‘16.
Hadoop World 2011: Hadoop’s Life in Enterprise Systems - Y Masatani, NTTDataCloudera, Inc.
NTT DATA has been providing Hadoop professional services for enterprise customers for years. In this talk we will categorize Hadoop integration cases based on our experience and illustrate archetypal design practices how Hadoop clusters are deployed into existing infrastructure and services. We will also present enhancement cases motivated by customer’s demand including GPU for big math, HDFS capable storage system, etc.
Big Data with Hadoop – For Data Management, Processing and StoringIRJET Journal
This document discusses big data and Hadoop. It begins with defining big data and explaining its characteristics of volume, variety, velocity, and veracity. It then provides an overview of Hadoop, describing its core components of HDFS for storage and MapReduce for processing. Key technologies in Hadoop's ecosystem are also summarized like Hive, Pig, and HBase. The document concludes by outlining some challenges of big data like issues of heterogeneity and incompleteness of data.
Transaction Processing Systems (TPS) collect, store, and modify data from daily business transactions. TPS have features like rapid response, reliability, and inflexibility as they treat all transactions equally. There are two main types of TPS - batch processing, where data is collected and processed later, and real-time processing, where data is processed immediately. Data warehouses are large databases used to support management decision making through analysis of historical data from various sources.
This document discusses opportunities for using big data in private wealth management. It begins by defining big data and describing how data volumes have increased exponentially. It then outlines several potential use cases for big data in areas like real-time performance metrics, portfolio optimization, and leveraging customer data. For each use case, it describes current limitations and how a big data approach could enable new capabilities. Finally, it proposes a phased approach for wealth managers to identify use cases, prioritize them, implement proofs of concept, and incrementally automate analysis and reporting. The overall message is that big data can enhance analytics and open up new opportunities previously only available to investment banks.
This document summarizes a summer training seminar on BigData Hadoop that was attended. The training was provided by LinuxWorld Informatics Pvt Ltd, which offers open source and commercial training programs. The attendee learned about Hadoop, MapReduce, single and multi-node clusters, Docker, and Ansible. Big data challenges related to volume, variety, velocity, and veracity of data were also covered. Hadoop and its core components HDFS and MapReduce were explained as solutions for storing and processing large datasets in a distributed manner across commodity hardware. Docker containers were introduced as a lightweight alternative to virtual machines.
The document discusses Microsoft's Windows Azure Platform, including its pricing models, workload patterns suited for cloud computing, channel models, and support offerings. It provides details on consumption pricing for computing, storage, databases, and data transfer. Promotional offers and programs are also summarized, along with the monthly SLA and worldwide coverage of the platform.
This document discusses big data, including what it is, common data sources, its volume, velocity and variety characteristics, solutions like Hadoop and its HDFS and MapReduce components, and the impact and future of big data. It explains that big data refers to large and complex datasets that are difficult to process using traditional tools. Hadoop provides a framework to store and process big data across clusters of commodity hardware.
This document discusses different architectures for big data systems, including traditional, streaming, lambda, kappa, and unified architectures. The traditional architecture focuses on batch processing stored data using Hadoop. Streaming architectures enable low-latency analysis of real-time data streams. Lambda architecture combines batch and streaming for flexibility. Kappa architecture avoids duplicating processing logic. Finally, a unified architecture trains models on batch data and applies them to real-time streams. Choosing the right architecture depends on use cases and available components.
Big Data Architectures @ JAX / BigDataCon 2016Guido Schmutz
Mit der Architektur steht und fällt jedes IT-Projekt. Das gilt in noch stärkerem Maße für Big-Data-Projekte, denn hier konnten noch keine Standards über Jahrzehnte ihre Tauglichkeit beweisen. Dennoch verbreiten und etablieren sich auch hier gute und effektive Lösungen. Der Vortrag erklärt, welche Bausteine wichtig für die verschiedenen Einsatzmöglichkeiten im Big-Data-Umfeld sind, und wie sie in konkrete Lösungen gegossen werden können. Dabei beleuchtet er sowohl traditionelle Big-Data-Architekturen als auch aktuelle Ansätze, wie z. B. die Lambda- und die Kappa-Architektur. Ebenfalls ein Thema sind Stream-Processing-Infrastrukturen und ihre Kombination mit Big-Data-Technologien. Ausgehend von einer produkt- und technologieunabhängigen Referenzarchitektur stellt dieser Vortrag verschiedene Lösungsmöglichkeiten auf Basis von Open-Source-Komponenten vor.
Lean Enterprise, Microservices and Big DataStylight
This document discusses enabling the lean enterprise through technologies like microservices, continuous integration/deployment, and cloud computing. It begins by defining the lean enterprise and the OODA loop concept. It then explains how technologies like AWS, big data, and microservices can help organizations continuously observe, orient, decide, and act. Specific AWS services like EC2, EMR, Kinesis, Redshift, S3, and DynamoDB are reviewed. The benefits of breaking up monolithic systems into microservices and implementing devops practices like CI/CD are also summarized.
This document discusses smart apps and how Pivotal uses data science to build them. It describes three key components of smart apps: data, a smart system that uses data science to understand user behavior, and a user interface. It then provides examples of smart apps Pivotal has developed for logistics and automotive customers, describing how machine learning models were used to predict delivery locations and road conditions. The document emphasizes an API-first approach and using cloud platforms like Cloud Foundry to operationalize models and deliver insights through predictive APIs.
This document discusses the concept of big data. It defines big data as massive volumes of structured and unstructured data that are difficult to process using traditional database techniques due to their size and complexity. It notes that big data has the characteristics of volume, variety, and velocity. The document also discusses Hadoop as an implementation of big data and how various industries are generating large amounts of data.
IT overview for nonprofits by Dave Cortright (IT4NP)Dave Cortright
Information, tips and resources for managing the tech side of the org. Presented to The Greater Sum virtual incubator in July 2020. For more info check out http://it4np.com
Splunk has helped MetroPCS speed up troubleshooting and launch new products faster by allowing them to ingest and analyze call detail records and other network data. It was deployed within 2 weeks and has provided unexpected benefits like subpoena compliance and understanding overall system health. Splunk insights have helped MetroPCS optimize call routing to save hundreds of thousands in costs.
Data Engineer's Lunch #85: Designing a Modern Data StackAnant Corporation
What are the design considerations that go into architecting a modern data warehouse? This presentation will cover some of the requirements analysis, design decisions, and execution challenges of building a modern data lake/data warehouse.
Cassandra & puppet, scaling data at $15 per monthdaveconnors
Constant Contact shares lessons learned from DevOps approach to implementing Cassandra to manage social media data for over 400k small business customers. Puppet is the critical in our tool chain. Single most important factor was the willingness of Development and Operations to stretch beyond traditional roles and responsibilities.
A very categorized presentation about big data analytics Various topics like Introduction to Big Data,Hadoop,HDFS Map Reduce, Mahout,K-means Algorithm,H-Base are explained very clearly in simple language for everyone to understand easily.
AquaQ Analytics Kx Event - Data Direct Networks PresentationAquaQ Analytics
This document discusses using DDN's parallel file systems to improve the performance of kdb+ analytics queries on large datasets. Running kdb+ on a parallel file system can significantly reduce query latency by distributing data and queries across multiple file system servers. This allows queries to achieve near linear speedups as more servers are added. The shared namespace also allows multiple independent kdb+ instances to access the same consolidated datasets.
Similar to Functional Big Data (by Vance Shipley) (20)
Graspan: A Big Data System for Big Code AnalysisAftab Hussain
We built a disk-based parallel graph system, Graspan, that uses a novel edge-pair centric computation model to compute dynamic transitive closures on very large program graphs.
We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations.
These analyses were used to augment the existing checkers; these augmented checkers found 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18.
- Accepted in ASPLOS ‘17, Xi’an, China.
- Featured in the tutorial, Systemized Program Analyses: A Big Data Perspective on Static Analysis Scalability, ASPLOS ‘17.
- Invited for presentation at SoCal PLS ‘16.
- Invited for poster presentation at PLDI SRC ‘16.
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j
Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.
Measures in SQL (SIGMOD 2024, Santiago, Chile)Julian Hyde
SQL has attained widespread adoption, but Business Intelligence tools still use their own higher level languages based upon a multidimensional paradigm. Composable calculations are what is missing from SQL, and we propose a new kind of column, called a measure, that attaches a calculation to a table. Like regular tables, tables with measures are composable and closed when used in queries.
SQL-with-measures has the power, conciseness and reusability of multidimensional languages but retains SQL semantics. Measure invocations can be expanded in place to simple, clear SQL.
To define the evaluation semantics for measures, we introduce context-sensitive expressions (a way to evaluate multidimensional expressions that is consistent with existing SQL semantics), a concept called evaluation context, and several operations for setting and modifying the evaluation context.
A talk at SIGMOD, June 9–15, 2024, Santiago, Chile
Authors: Julian Hyde (Google) and John Fremlin (Google)
https://doi.org/10.1145/3626246.3653374
UI5con 2024 - Keynote: Latest News about UI5 and it’s EcosystemPeter Muessig
Learn about the latest innovations in and around OpenUI5/SAPUI5: UI5 Tooling, UI5 linter, UI5 Web Components, Web Components Integration, UI5 2.x, UI5 GenAI.
Recording:
https://www.youtube.com/live/MSdGLG2zLy8?si=INxBHTqkwHhxV5Ta&t=0
What is Augmented Reality Image Trackingpavan998932
Augmented Reality (AR) Image Tracking is a technology that enables AR applications to recognize and track images in the real world, overlaying digital content onto them. This enhances the user's interaction with their environment by providing additional information and interactive elements directly tied to physical images.
SOCRadar's Aviation Industry Q1 Incident Report is out now!
The aviation industry has always been a prime target for cybercriminals due to its critical infrastructure and high stakes. In the first quarter of 2024, the sector faced an alarming surge in cybersecurity threats, revealing its vulnerabilities and the relentless sophistication of cyber attackers.
SOCRadar’s Aviation Industry, Quarterly Incident Report, provides an in-depth analysis of these threats, detected and examined through our extensive monitoring of hacker forums, Telegram channels, and dark web platforms.
UI5con 2024 - Boost Your Development Experience with UI5 Tooling ExtensionsPeter Muessig
The UI5 tooling is the development and build tooling of UI5. It is built in a modular and extensible way so that it can be easily extended by your needs. This session will showcase various tooling extensions which can boost your development experience by far so that you can really work offline, transpile your code in your project to use even newer versions of EcmaScript (than 2022 which is supported right now by the UI5 tooling), consume any npm package of your choice in your project, using different kind of proxies, and even stitching UI5 projects during development together to mimic your target environment.
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI AppGoogle
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-fusion-buddy-review
AI Fusion Buddy Review: Key Features
✅Create Stunning AI App Suite Fully Powered By Google's Latest AI technology, Gemini
✅Use Gemini to Build high-converting Converting Sales Video Scripts, ad copies, Trending Articles, blogs, etc.100% unique!
✅Create Ultra-HD graphics with a single keyword or phrase that commands 10x eyeballs!
✅Fully automated AI articles bulk generation!
✅Auto-post or schedule stunning AI content across all your accounts at once—WordPress, Facebook, LinkedIn, Blogger, and more.
✅With one keyword or URL, generate complete websites, landing pages, and more…
✅Automatically create & sell AI content, graphics, websites, landing pages, & all that gets you paid non-stop 24*7.
✅Pre-built High-Converting 100+ website Templates and 2000+ graphic templates logos, banners, and thumbnail images in Trending Niches.
✅Say goodbye to wasting time logging into multiple Chat GPT & AI Apps once & for all!
✅Save over $5000 per year and kick out dependency on third parties completely!
✅Brand New App: Not available anywhere else!
✅ Beginner-friendly!
✅ZERO upfront cost or any extra expenses
✅Risk-Free: 30-Day Money-Back Guarantee!
✅Commercial License included!
See My Other Reviews Article:
(1) AI Genie Review: https://sumonreview.com/ai-genie-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
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E-Invoicing Implementation: A Step-by-Step Guide for Saudi Arabian CompaniesQuickdice ERP
Explore the seamless transition to e-invoicing with this comprehensive guide tailored for Saudi Arabian businesses. Navigate the process effortlessly with step-by-step instructions designed to streamline implementation and enhance efficiency.
Unveiling the Advantages of Agile Software Development.pdfbrainerhub1
Learn about Agile Software Development's advantages. Simplify your workflow to spur quicker innovation. Jump right in! We have also discussed the advantages.
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j
Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.
E-commerce Development Services- Hornet DynamicsHornet Dynamics
For any business hoping to succeed in the digital age, having a strong online presence is crucial. We offer Ecommerce Development Services that are customized according to your business requirements and client preferences, enabling you to create a dynamic, safe, and user-friendly online store.
A Study of Variable-Role-based Feature Enrichment in Neural Models of CodeAftab Hussain
Understanding variable roles in code has been found to be helpful by students
in learning programming -- could variable roles help deep neural models in
performing coding tasks? We do an exploratory study.
- These are slides of the talk given at InteNSE'23: The 1st International Workshop on Interpretability and Robustness in Neural Software Engineering, co-located with the 45th International Conference on Software Engineering, ICSE 2023, Melbourne Australia
Transform Your Communication with Cloud-Based IVR SolutionsTheSMSPoint
Discover the power of Cloud-Based IVR Solutions to streamline communication processes. Embrace scalability and cost-efficiency while enhancing customer experiences with features like automated call routing and voice recognition. Accessible from anywhere, these solutions integrate seamlessly with existing systems, providing real-time analytics for continuous improvement. Revolutionize your communication strategy today with Cloud-Based IVR Solutions. Learn more at: https://thesmspoint.com/channel/cloud-telephony
Do you want Software for your Business? Visit Deuglo
Deuglo has top Software Developers in India. They are experts in software development and help design and create custom Software solutions.
Deuglo follows seven steps methods for delivering their services to their customers. They called it the Software development life cycle process (SDLC).
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2. Agenda
MapReduce
Google
Scaling Out
Key Value Store
Chaining
Fault Tolerance
Functional Example
Business Problem
Design
Processes
Schema
Big Data Guidelines
4. Google MapReduce
+ Paper published in 2004
+ Implemented in 2003
+ Production use at Google
+ Built for Google
+ Not open sourced
5. Google in 2004
+ Clusters of 100s or 1000s of servers
o Linux
o dual-processor x86
o 2-4 GB memory
o 100BaseT or GigE
o inexpensive IDE hard drives
+ Servers fail every day
+ Network maintenance is constant
6. Scaling Out
+ Scaling up (faster computer) doesn’t get far
+ Scaling out is the only next step
+ Hundreds/thousands of modest computers
outperform the biggest single computers
+ Scaling one to a few is hard
+ Scaling a few to many is easy
+ Scaling many to massive is (almost) trivial
8. Intermediate Data
+ Input data is split between the workers
+ Map workers create key/value pairs
+ Reduce workers read in all intermediate
data and sort by key
+ Reduce workers then iterate over the sorted
data producing a result for each key
10. Rinse and Repeat
+ Often the results of one MapReduce are
used as input to another
+ Building on a powerful basic functional
model complex data processing can be
accomplished
12. Fault Tolerance
+ Likelihood of failure rises with number of
servers and processing time
+ Resiliency is a necessity at scale
+ Scheduler/Supervisor (master) reassigns
failed jobs and ensures reduce workers find
the (right) data
16. Example Business Problem
Scenario:
A mobile operator wants to know if an instant
messaging (IM) service would be useful to
current subscribers.
Question:
What percentage of text messages (SMS)
are part of a conversation?
17. Challenge
✓ 10 million subscribers
✓ average of 100 SMS a month per subscriber
✓ ∴ one billion SMS each month
✓ call detail records (CDR) include SMS but also
voice and data events
✓ ∴ 20 billion (20,000,000,000) records/month
18. Requirements
+ Identify SMS conversations
o messages sent or received with one other party
o interval between messages < 10 minutes
o at least three messages exchanged
+ Provide result as
o ratio of conversational to non-conversational SMS
o per subscriber
o per month
20. Filter
+ Read events from CDR files
o records are in chronological order
o read files in chronological order
+ Discard non-SMS events
+ Distribute SMS events to Map processes
o Consistent distribution by subscriber
21. Hashing
+ To analyze interval between
messages one process must
handle all events for a
particular subscriber
+ Simple Hash:
o M = last four digits of subscriber’s
mobile number
o N = number of processes available
o Pid = M rem N
22. Map
+ Read subscriber’s stored data
+ Find other party in set
+ Increment total count of messages
+ Is previous message < 10 minutes?
o Is next previous message < 10m before previous?
Increment conversational messages count
+ Update previous and next previous times
24. Interim Data
+ We are using an in memory key value store
+ The key is the subscriber number
+ The value is a set of OtherParty
+ OtherParty data structure contains counts
+ When the map is complete we transfer the
data to disk for persistence
25. Reduce
+ Collect intermediate data
from disk copies
+ Iterate through all parties for
each subscriber
+ Total all party counts
+ Provide result as percentage
of conversational messages
to total messages
26. Big Data Guidelines
+ Find opportunities for concurrency
+ Choose the right containers for your data
+ Use memory as effectively as possible
+ Minimize copying data
+ Avoid any unnecessary overhead
+ Anything you are going to do hundreds of
billions of times should be efficient!
In order to successfully handle really big data requires massive concurrency and in the real world this requires fault tolerance.
Google didn’t invent map and reduce but they were the first to apply the paradigm in a general way on a massive scale.
… or, more probably, a number of results. By dividing the work we can assign it to many servers. This concurrency is what allows scale.
Here is an example of something which Google do as part of their core business. Google places web sites which are linked to by many other web sites higher in search results (PageRank). To determine this a map reads web pages found by crawlers and creates key/value pairs. These are written in memory and then pushed out in blocks to disk. A reduce reads these disk blocks and sorts all the intermediate data by key. The reduce function then iterates over all the pairs for a key and outputs one result for each key.
The results from one MapReduce can, and often are, provided as input for further MapReduce runs.
Something like RAID, maybe Reduced Array of Inexpensive Servers (RAIS)? The can and do fail individually without the system failing.
The user process forks all of the other processes which will be used including a master process. The master then assigns those processes work to perform, either map or reduce roles.
The master process monitors each worker by sending a ping periodically. When it detects that a server has failed (or is no longer reachable) it will reassign that server’s work to another worker. After this reassignment each of the reduce workers will be notified to ignore the failed server and instead get the interim data from the newly assigned server.
This is a contrived example.
That’s billion with a ‘B’. In Canada that’s 1,000 million.
There is an obvious hole in this pseudo code, the first two messages of the conversation are not included in the conversational totals. I could have accommodated that but I left it out to keep the example as simple possible.