Slide du petit déjeuner du 11 décembre 2013
Dans un contexte économique délicat, les outils du « big data » apportent toute la rapidité, la souplesse et la scalabilité requise pour mettre en oeuvre des projets d'entreprise tirant profit de volumes d'information importants. Ces technologies sont désormais une réalité à intégrer aux projets SI.
La société Klee Group organise ce déjeuner thématique en proposant des intervenants du Big Data :
- Mongo DB
- Elasticsearch
- CMS Rubedo
The document discusses various applications of data mining, including financial data analysis, retail industry analysis, telecommunications analysis, and biological data analysis. It provides examples of how data mining is used for tasks like customer segmentation, marketing campaign analysis, fraud detection, and gene sequence analysis. The document also covers trends in data mining, such as visual data mining and audio data mining.
Bigdata.
Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. Challenges include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy. The term "big data" often refers simply to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. "There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem."[2] Analysis of data sets can find new correlations to "spot business trends, prevent diseases, combat crime and so on."[3] Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet search, fintech, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics,[4] connectomics, complex physics simulations, biology and environmental research.[5]
Data sets grow rapidly - in part because they are increasingly gathered by cheap and numerous information-sensing Internet of things devices such as mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks.[6][7] The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s;[8] as of 2012, every day 2.5 exabytes (2.5×1018) of data are generated.[9] One question for large enterprises is determining who should own big-data initiatives that affect the entire organization.[10]
Relational database management systems and desktop statistics- and visualization-packages often have difficulty handling big data. The work may require "massively parallel software running on tens, hundreds, or even thousands of servers".[11] What counts as "big data" varies depending on the capabilities of the users and their tools, and expanding capabilities make big data a moving target. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration."
This document provides an introduction to big data, including its key characteristics of volume, velocity, and variety. It describes different types of big data technologies like Hadoop, MapReduce, HDFS, Hive, and Pig. Hadoop is an open source software framework for distributed storage and processing of large datasets across clusters of computers. MapReduce is a programming model used for processing large datasets in a distributed computing environment. HDFS provides a distributed file system for storing large datasets across clusters. Hive and Pig provide data querying and analysis capabilities for data stored in Hadoop clusters using SQL-like and scripting languages respectively.
1. The document discusses Big Data analytics using Hadoop. It defines Big Data and explains the 3Vs of Big Data - volume, velocity, and variety.
2. It then describes Hadoop, an open-source framework for distributed storage and processing of large data sets across clusters of commodity hardware. Hadoop uses HDFS for storage and MapReduce for distributed processing.
3. The core components of Hadoop are the NameNode, which manages file system metadata, and DataNodes, which store data blocks. It explains the write and read operations in HDFS.
THE 3V's OF BIG DATA: VARIETY, VELOCITY, AND VOLUME from Structure:Data 2012Gigaom
The document discusses the 3 V's of big data: volume, velocity, and variety. It provides examples of how each V impacts data analysis and storage. It also discusses how text data has been a major driver of big data growth and challenges. The key challenges are processing large and diverse datasets quickly enough to keep up with real-time data streams and demands.
Disclaimer :
The images, company, product and service names that are used in this presentation, are for illustration purposes only. All trademarks and registered trademarks are the property of their respective owners.
Data/Image collected from various sources from Internet.
Intention was to present the big picture of Big Data & Hadoop
Guest Lecture: Introduction to Big Data at Indian Institute of TechnologyNishant Gandhi
This document provides an introduction to big data, including definitions of big data and why it is important. It discusses characteristics of big data like volume, velocity, variety and veracity. It provides examples of big data applications in various industries like GE, Boeing, social media, finance, CERN, journalism, politics and more. It also introduces NoSQL and the CAP theorem, and concludes that big data is changing business and technology by enabling new insights from data to reduce costs and optimize operations.
Analytics, machine e deep learning, data/event streaming
Big data streaming: abilitare la macchina del tempo
Real time event streaming e nuovi paradigmi concettuali:
- Transazioni distribuite
- Consistenza eventuale
- Proiezioni materializzate
Real time event streaming e nuovi paradigmi architetturali:
- Enterprise service bus
- Event store
- Database delle proiezioni
Cenni di Domain Driven Design: una visione strategica della modellazione del proprio dominio di business nell'era dei bi Data.
Big Data Analysis Patterns - TriHUG 6/27/2013boorad
Big Data Analysis Patterns: Tying real world use cases to strategies for analysis using big data technologies and tools.
Big data is ushering in a new era for analytics with large scale data and relatively simple algorithms driving results rather than relying on complex models that use sample data. When you are ready to extract benefits from your data, how do you decide what approach, what algorithm, what tool to use? The answer is simpler than you think.
This session tackles big data analysis with a practical description of strategies for several classes of application types, identified concretely with use cases. Topics include new approaches to search and recommendation using scalable technologies such as Hadoop, Mahout, Storm, Solr, & Titan.
This document provides an overview of big data. It defines big data as large volumes of diverse data that are growing rapidly and require new techniques to capture, store, distribute, manage, and analyze. The key characteristics of big data are volume, velocity, and variety. Common sources of big data include sensors, mobile devices, social media, and business transactions. Tools like Hadoop and MapReduce are used to store and process big data across distributed systems. Applications of big data include smarter healthcare, traffic control, and personalized marketing. The future of big data is promising with the market expected to grow substantially in the coming years.
Big data refers to the large and complex data sets that are difficult to analyze and process using traditional data processing applications. Retailers can leverage big data analytics to gain insights from customer data on social media and other sources to make better business decisions and stay competitive. Walmart analyzes over 2 million daily consumer insights and comments to better understand customers and manage inventory and logistics in a cost-effective way, helping ensure the best prices and customer service.
The document summarizes a report on the global big data and data analytics market for homeland security and public safety from 2017-2022. It forecasts that the market will grow at a compound annual rate of 17.5% to reach $11 billion by 2022. It also finds that government intelligence agencies and police/law enforcement will increase spending the most during this period. Finally, it identifies key drivers of growth for this market, including new data sources from smartphones/IoT, cloud adoption, and the need to address evolving security threats.
The document discusses big data, its history, technologies, and uses. It begins with an introduction to big data and defines it using the 3Vs/4Vs model, describing the volume, velocity, variety and increasingly veracity of data. It then discusses big data technologies like Hadoop, databases, reporting, dashboards and real-time analytics. Examples are given of how big data is used, such as understanding customers, optimizing business processes, improving health outcomes, and improving security and law enforcement. Requirements for big data analytics are also mentioned, including data management, analytics applications, and business interpretation.
The document discusses big data, including the different units used to measure data size like bytes, kilobytes, megabytes, etc. It notes that big data is difficult to store and process using traditional tools due to its large size and complexity. Big data is growing rapidly in volume, velocity and variety. Some challenges in analyzing big data include its unstructured nature, size that exceeds capabilities of conventional tools, and need for real-time insights. Security, access control, data classification and performance impacts must be considered when protecting big data.
A high level overview of common Cassandra use cases, adoption reasons, BigData trends, DataStax Enterprise and the future of BigData given at the 7th Advanced Computing Conference in Seoul, South Korea
MapReduce allows distributed processing of large datasets across clusters of computers. It works by splitting the input data into independent chunks which are processed by the map function in parallel. The map function produces intermediate key-value pairs which are grouped by the reduce function to form the output data. Fault tolerance is achieved through replication of data across nodes and re-executing failed tasks. This makes MapReduce suitable for efficiently processing very large datasets in a distributed environment.
Ce document émane de la société Les Brigades du Marketing, agence de conseil et de service en marketing, à découvrir à l’adresse www.lesbrigadesdumarketing.com.
Un opérateur de téléphonie souhaitait mieux exploiter ses données au moyen du big data.
Nous lui avons proposé un programme de déploiement du big data, assorti d’une mécanique de gamification, s’inscrivant dans une volonté plus générale de conduite du changement.
Le plus heureux des hasards a fait que des papillons ont bien voulu se poser aux endroits où apparaissent des informations sensibles (nom de marques, de clients, de prestataires ou d’intervenants, chiffres, etc.), nous aidant ainsi à préserver leur confidentialité.
Pour contacter l’auteur de ce document, nous vous invitons à adresser un e-mail à l’adresse suivante : contact@lesbrigadesdumarketing.com.
Le Big Data ou la science des données est un sujet complexe et passionnant. Sciences des données, données massives, le Big-Data est de plus en plus considéré comme un or noir car il permet aux entreprises de développer leur potentiel économique. Dans cette perspective, Il est essentiel de se poser la question de l’intérêt du Big Data et de son avenir à Bruxelles.
Talking about Big Data generates a lot of questions; however, most of the focus is on the technologies and skills required to collect and store this volume of information as opposed to the insight that companies need to derive from it. What factors should organizations consider in order to ensure that they are capitalizing on their investments with these technologies? How do you break through business silos to enable sharing of data to increase organizational value? Leveraging his cross-industry experience at companies like The Walt Disney Company, Travelers Insurance and Demand Media, Brendan Aldrich will discuss the question of “big value” with industry examples and a particular focus on his current work to deploy a “data democracy” within the City Colleges of Chicago.
Session Discovery Topics:
• Big value - keeping an eye on the forest (assumptions, judgment and bias)
• Data democracy - increasing productivity with data transparency and open access
Utiliser le profil client, le big data pour améliorer les ventes en temps réel Jean-Michel Franco
Réalisée dans le cadre de la conférence Nice Interactions avec Jason Mc Fall de Nice Systems, cette présentation montre comme la collecte de données de parcours client et la mise en oeuvre de recommandations temps personnalisées en fonction du profil, du parcours et du contexte permet d'améliorer les ventes, qu'elles soient directes (site web, applis mobiles) ou indirectes (centres d'appels, visite commerciales, vente en boutique...) .
Faut-il obligatoirement passer par le Big Data pour personnaliser l'expérienc...Sparkow
Webinar 28 Janvier 2015
Faut-il obligatoirement passer par le BigData pour personnaliser l'expérience client sur le web ?
Présenté par :
Pascal Morvan, Solution Selling Director, Sparkow
Jérémy Viault, Product Marketing Manager, Sparkow
Recommander les produits pertinents pour chaque client nécessite une grande quantité de données : sur les produits, les clients, les comportements…
Est-il pour autant nécessaire de parler de Big Data ?
Ce webinar vous proposera un panorama de l'état de l'art en matière de recommandation de produits et de contenus pour y voir plus clair.
Digital has engendered a fundamental shift in the way we behave, think and perform business. One of the most essential transformations for today’s organisations is to adapt to how the customer has changed. This obviously has a massive impact on the salesforce and its methods. The Customer Journey has changed dramatically, becoming far more digitized and needs consistent use of many tools, technologies and methods to effectively reach the target audience.
Introduction à ElasticSearch et les possibilités offertes par l'outil.
Retours d'expériences, recommandations et demonstration des outils gravitant autour : Kibana, Rivers, Logstash...
Téléchargez le fichier pour disposer des animations !
Data-Ed Webinar: Demystifying Big Data DATAVERSITY
We are in the middle of a data flood and we need to figure out how to tame it without drowning. Most of what has been written about Big Data is focused on selling hardware and services. But what about a Big Data Strategy that guides hardware and software decisions? While virtually every major organization is faced with the challenge of figuring out the approach for and the requirements of this new development, jumping into the fray hastily and unprepared will only reproduce the same dismal IT project results as previously experienced. Join Dr. Peter Aiken as he will debunk a number of misconceptions about Big Data as your un-typical IT project. He will provide guidance on how to establish realistic Big Data management plans and expectations, and help demonstrate the value of such actions to both internal and external decision makers without getting lost in the hype.
Takeaways:
- The means by which Big Data techniques can complement existing data management practices
- The prototyping nature of practicing Big Data techniques
- The distinct ways in which utilizing Big Data can generate business value
- Bigger Data isn’t always Better Data
[Fr] La personnalisation client relancée grâce aux Big DataYann Gourvennec
Le marketing personnalisé est à l'ordre du jour des innovations marketing depuis un bon bout de temps. Exemple parfait de ce que Bernard Cova a appelé les "panacées marketing". On le voit ressurgir de temps en temps, au gré des innovations technologiques. En 2015, ce sont les Big Data qui sont le principal moteur de ce Risorgimento. Est-ce justifié et la personnalisation est-elle le futur du marketing, un simple rêve de bobo, une innovation mineure, voire même un passage obligé pour innovateurs nostalgiques ? À vous de juger.
Big Data: InterConnect 2016 Session on Getting Started with Big Data AnalyticsCynthia Saracco
Learn how to get started with Big Data using a platform based on Apache Hadoop, Apache Spark, and IBM BigInsights technologies. The emphasis here is on free or low-cost options that require modest technical skills.
Collective Intelligence- Man, Machine and Internet | Converge Chennai 2015Thoughtworks
This document discusses the convergence of man, machine, and the internet through various technologies over the 19th, 20th, and 21st centuries including implantable pacemakers, GPS trackers, baby monitors, activity trackers, and intelligent assistants like JARVIS. It then discusses predictions for the future in 2029 with vehicles that can self-diagnose and communicate with service centers, public transportation that communicates to optimize traffic flow, and smart cities where vehicles and infrastructure can communicate to optimize services. Finally, it discusses current Internet of Things technologies including a device to help farmers predict milk production and a cycling navigation tool.
Big Data : au delà du proof of concept et de l'expérimentation (Matinale busi...Jean-Michel Franco
Concrétiser les promesses du Big Data avec Hadoop, le Self-Service, les data lakes et le machine learning. Quels cas d'usage, quels retours d'expérience, quelle plate-forme?
Creating an end-to-end Recommender System with Apache Spark and Elasticsearch...sparktc
At the sold-out Spark & Machine Learning Meetup in Brussels on October 27, 2016, Nick Pentreath of the Spark Technology Center teamed up with Jean-François Puget of IBM Analytics to deliver a talk called Creating an end-to-endRecommender System with Apache Spark and Elasticsearch.
Jean-François and Nick started with a look at the workflow for recommender systems and machine learning, then moved on to data modeling and using Spark ML for collaborative filtering. They closed with a discussion of deploying and scoring the recommender models, including a demo.
Présentation de l'E-transformation du Parcours Client dans le cadre du MBA Marketing et Commerce sur Internet, #MBAMCI, Institut Leonard de Vinci, La Defense-Paris. Promo Part Time 2014/2015
- Introduction
- Côté clients
- Outils de l’e-transformation
- Impacts entreprises
- Un exemple : le parcours client voiture
- Conclusion
How to use the power of data in e-Commerce? Applying the Big Data solutions makes it possible to analyse data in real time. This allows us to use the data not for reports only, but to translate them into action.
Enabling Telco to Build and Run Modern Applications Tugdual Grall
This document discusses how MongoDB can help enable businesses to build and run modern applications. It begins with an overview of Tugdual Grall and his background. It then discusses how industries and data have changed, driving the need for a next generation database. The rest of the document provides an overview of MongoDB, including the company, technology, and community. Examples are given of how MongoDB has helped companies in the telecommunications industry achieve a single customer view, improve product catalogs and personalization, and build mobile and open data APIs.
Solr Under the Hood at S&P Global- Sumit Vadhera, S&P Global Lucidworks
This document summarizes S&P Global's use of Solr for search capabilities across their large datasets. It discusses how S&P Global indexes over 50 million documents into Solr monthly and handles over 5 million queries per week. It outlines challenges faced with an on-premise Solr deployment and how migrating to Solr Cloud helped address issues like performance, availability, and scalability. Next steps discussed include improving relevancy through data science, continuing to leverage new Solr features, and exploring ways to integrate machine learning into search capabilities.
Séminaire Big Data Alter Way - Elasticsearch - octobre 2014ALTER WAY
This document discusses Elasticsearch and how it can be used to search, analyze, and make sense of large amounts of data. It provides examples of how Elasticsearch is being used by large companies to handle petabytes of data and gain insights. Implementations in France are highlighted. The document concludes by demonstrating how easily Elasticsearch can be deployed and used to ingest and search sample data.
Sql Start! 2020 - SQL Server Lift & Shift su AzureMarco Obinu
Slide of the session delivered during SQL Start! 2020, where I illustrate different approaches to determine the best landing zone for you SQL Server workloads.
Video (ITA): https://youtu.be/1hqT_xHs0Qs
The document discusses MongoDB and data treatment. It covers how MongoDB can help with data integrity, confidentiality, correctness and reliability. It also discusses how MongoDB supports dynamic schemas, replication for high availability, security features and can be used as part of a modern enterprise technology stack including integration with Hadoop. MongoDB can be deployed on Azure as a fully managed service.
ADV Slides: When and How Data Lakes Fit into a Modern Data ArchitectureDATAVERSITY
Whether to take data ingestion cycles off the ETL tool and the data warehouse or to facilitate competitive Data Science and building algorithms in the organization, the data lake – a place for unmodeled and vast data – will be provisioned widely in 2020.
Though it doesn’t have to be complicated, the data lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the data swamp, but not the data lake! The tool ecosystem is building up around the data lake and soon many will have a robust lake and data warehouse. We will discuss policy to keep them straight, send data to its best platform, and keep users’ confidence up in their data platforms.
Data lakes will be built in cloud object storage. We’ll discuss the options there as well.
Get this data point for your data lake journey.
Oracle in the 2014 edition of its Open World rolled out new database public cloud service with its DBaaS offerings, but this is just a piece in each company's technological architecture. Businesses still have the need to create a Private cloud and discover the driver to create it; Wether it is a measured service,consolidation or rapid provisioning, finding this driver will be the initial building block for it. This presentation will give you an insight on how a Private Cloud is architected, how the service catalog is the most important brick and how get the benefit of this upcoming era of Databases.
Gab Genai Cloudera - Going Beyond Traditional Analytic IntelAPAC
This document discusses Intel and Cloudera's partnership in helping organizations leverage big data analytics. It provides an overview of Cloudera's history and capabilities in supporting enterprises with Hadoop-based solutions. It then contrasts traditional analytics approaches that brought data to compute with Cloudera's approach of bringing compute to data using their Enterprise Data Hub. Several case studies are presented of organizations achieving new insights and business value through Cloudera's platform. The document emphasizes that Cloudera offers an open, scalable and cost-effective platform for various analytics workloads and enables a thriving ecosystem of partners.
The document discusses new rules and strategies for retailers in an evolving customer relationship landscape. It notes there are now 56 touchpoints between a customer's moment of inspiration and transaction. It then discusses components of digital transformation like customer experience management, cross-channel order orchestration, and building a single customer view. The document outlines how retailers can create customer connections and profiles by leveraging enterprise data. It also discusses the need for customer engagement in stores through technologies like self-scanning and mobile payments. Finally, it discusses how front-end store technologies can empower associates and optimize processes.
Serverless SQL provides a serverless analytics platform that allows users to analyze data stored in object storage without having to manage infrastructure. Key features include seamless elasticity, pay-per-query consumption, and the ability to analyze data directly in object storage without having to move it. The platform includes serverless storage, data ingest, data transformation, analytics, and automation capabilities. It aims to create a sharing economy for analytics by allowing various users like developers, data engineers, and analysts flexible access to data and analytics.
Webinar: Expanding Retail Frontiers with MongoDBMongoDB
Twenty-first century retailers are facing an increasingly challenging and competitive environment. Given the rise of ecommerce and pressure on margins, retailers are looking for innovative services as well as ways to improve customer service, loyalty and engagement. Leading organizations in retail are choosing MongoDB because of its ability to help them compete, providing superior customer experience and accelerated time to market. In this webinar, hear how MongoDB enables retailers to develop:
Enriched Product Catalog Management
Distribution and Logistics Management
Solutions Real time Analysis of Customer Behavior
The use cases are specific to retail, but the patterns of usage - agility, scale, and global distribution - will be applicable across many industries.
MongoDB World 2019: Near Real-Time Analytical Data Hub with MongoDBMongoDB
Attendees will learn how to build an operational data hub that can be used as a silo-buster. In this session, I will show how we developed a data hub at TD Ameritrade to provide actionable 360 views of client data using MongoDB. I will also explain why this approach suited our use case better than a Hadoop-based data lake.
Data Science and Enterprise Engineering with Michael Finger and Chris RobisonDatabricks
1) Initially, the data science and engineering teams at Overstock worked independently and were not regularly delivering business value or solving problems in real-time.
2) They came together to solve problems like real-time bidding, where they needed to score users and bid on ads within 10 milliseconds.
3) Over the next 6 months, they improved from scoring users daily to hourly to within minutes by streamlining processes and moving from batch to micro-batch processing. However, they still needed to get faster to enable real-time personalization on the site.
In this webinar, Michael Nash of BoldRadius explores the Typesafe Reactive Platform.
The Typesafe Reactive Platform is a suite of technologies and tools that support the creation of reactive applications, that is, applications that handle the kind of responsiveness requirements, data volume, and user load that was out of practical reach only a few years ago.
From analysis of the human genome to wearable technology to communications at a massive scale, BoldRadius has the premier team of experts with decades of collective experience in designing and building these types of applications, and in helping teams adopt these tools.
According to a recent Harvard Business Review study, there’s only a 43% chance that customers who have a poor experience will stick with you for the next 12 months. Contrast that to the 74% that will remain your customer if they have a great experience. Learn how Macy’s, a leading American department store chain founded in 1858 with over 750 stores in North America, is transforming their customer experience with DataStax Enterprise.
Webinar recording: https://youtu.be/CiUVxh6Ov_E
View current and past DataStax webinars: http://www.datastax.com/resources/webinars
ADV Slides: Platforming Your Data for Success – Databases, Hadoop, Managed Ha...DATAVERSITY
Thirty years is a long time for a technology foundation to be as active as relational databases. Are their replacements here? In this webinar, we say no.
Databases have not sat around while Hadoop emerged. The Hadoop era generated a ton of interest and confusion, but is it still relevant as organizations are deploying cloud storage like a kid in a candy store? We’ll discuss what platforms to use for what data. This is a critical decision that can dictate two to five times additional work effort if it’s a bad fit.
Drop the herd mentality. In reality, there is no “one size fits all” right now. We need to make our platform decisions amidst this backdrop.
This webinar will distinguish these analytic deployment options and help you platform 2020 and beyond for success.
Applications need data, but the legacy approach of n-tiered application architecture doesn’t solve for today’s challenges. Developers aren’t empowered to build and iterate their code quickly without lengthy review processes from other teams. New data sources cannot be quickly adopted into application development cycles, and developers are not able to control their own requirements when it comes to data platforms.
Part of the challenge here is the existing relationship between two groups: developers and DBAs. Developers are trying to go faster, automating build/test/release cycles with CI/CD, and thrive on the autonomy provided by microservices architectures. DBAs are stewards of data protection, governance, and security. Both of these groups are critically important to running data platforms, but many organizations deal with high friction between these teams. As a result, applications get to market more slowly, and it takes longer for customers to see value.
What if we changed the orientation between developers and DBAs? What if developers consumed data products from data teams? In this session, Pivotal’s Dormain Drewitz and Solstice’s Mike Koleno will speak about:
- Product mindset and how balanced teams can reduce internal friction
- Creating data as a product to align with cloud-native application architectures, like microservices and serverless
- Getting started bringing lean principles into your data organization
- Balancing data usability with data protection, governance, and security
Presenter : Dormain Drewitz, Pivotal & Mike Koleno, Solstice
By 2020, 50% of all new software will process machine-generated data of some sort (Gartner). Historically, machine data use cases have required non-SQL data stores like Splunk, Elasticsearch, or InfluxDB.
Today, new SQL DB architectures rival the non-SQL solutions in ease of use, scalability, cost, and performance. Please join this webinar for a detailed comparison of machine data management approaches.
Similar to Le big data à l'épreuve des projets d'entreprise (20)
Livre blanc de la plateforme digitale de gestion de contenus et de e-commerce Rubedo.
Ce document présente l'ensemble des fonctionnalités intégrées ; de la gestion de multi-sites web à la mise en œuvre d'actions de marketing automation.
Rubedo est une plateforme digitale :
✓ Multicanal : Tirez le meilleur parti de vos données en les
exploitant sur tous vos canaux de diffusion digitale : Web / Mobile / Apps iOS & Android...
✓ Behavior-driven : Résolument centré sur l’utilisateur, Rubedo est le seul outil à intégrer nativement des techniques avancées de personnalisation et de marketing automation.
✓ Agile : Créez des sites, des mises en page, des structures de données, en bref modelez votre présence digitale en toute autonomie.
✓ Big Data : Accélérez votre transformation digitale, décloisonnez votre SI et dopez votre compétitivité grâce au Big Data.
Rubedo est une plateforme digitale open-source professionnelle de gestion de contenus et de e-commerce. Le socle big data de Rubedo intègre l’analyse prédictive pour offrir la personnalisation en temps réel des sites aux centres d’intérêt des visiteurs anonymes ou connectés.
Une gamme complète de fonctionnalités permet de mutualiser sur un même socle technique de multiples sites internet, intranet, ecommerce ou applications métier.
Ce livre blanc présente les possibilités offertes aux administrateurs pour créer et gérer des sites web avec Rubedo.
1) The document discusses digital experience trends for 2016, including retargeting, new engaging content formats, optimizing the mobile experience, and personalizing the digital experience.
2) It also discusses the importance of customer experience, with 89% of businesses competing on customer experience by 2020, and how companies that know their customers well can build loyalty.
3) Marketing automation is mentioned as a trend, with the goal of allowing customers to build, track, and manage digital campaigns and monitor lead flow from marketing to sales.
Créer une liste de contenus manuelle ou automatisée ou créer des listes de contenus contextuels avec Rubedo. Ce tutoriel présente les possibilité de paramétrage avancé pour créer des listes de contenus en fonction des besoins.
Liste des fonctionnalités disponibles sur la version 3.3 de la plateforme digitale Rubedo.
Ces fonctionnalités sont intégrées dans Rubedo et ne nécessitent pas de modules externes sauf pour les enquêtes en ligne.
Le CMS Rubedo permet de créer et modéliser en live les différents champs nécessaires à la création de types de contenus.
Ce tutoriel présente les étapes pour créer et modifier les types de contenus.
De la création de catalogues produits à la gestion des commandes, Rubedo Commerce propose une solution complète de e-commerce intégrée au CMS Rubedo. Avec l'utilisation du moteur de recommandations personnalisées, profitez de la personnalisation automatique et en temps réel avec les Magic Query et proposez le bon produit à la bonne personne pour convertir vos prospects en clients.
Tutoriel pour l'import et la mise à jour automatique des contenus dans le cms Rubedo.
Description des étapes pour l'import automatique des contenus monolingues et multilingues.
Le module permet de créer automatiquement le types de contenus, les taxonomies de classement et les contenus. Il ne reste plus qu'a les afficher sur les pages.
Rubedo CMS : designing content and user layouts.
For each of your content types, you can create your own custom content layout by selecting the fields (title, summary, date, image…) you want to be displayed. To go any further, you can decide whether each element will be displayed or not for each device (desktop, tablet or smartphone).
For each of your content types, you can create your own custom content layout by selecting the fields (title, summary, date, image…) you want to be displayed. To go any further, you can decide whether each element will be displayed or not for each device (desktop, tablet or smartphone).
I. Taxonomy involves classifying contents using hierarchical vocabularies of terms to describe and organize them. This allows contents to be associated with multiple classifications beyond just the website structure.
II. The navigation vocabulary represents the website structure and defines where content is displayed. Other vocabularies are used for searching, filtering, and secondary navigation.
III. Vocabularies can be associated with workspaces to define which contributors can use them, and which are allowed depends on the content and media types.
Personnalisation des sites web : vers la fin d’une communication de masse ?
Face à une concurrence multi-canal féroce, le nouveau challenge consiste à fidéliser les utilisateurs en leur proposant des contenus et produits en adéquation avec leurs centres d’intérêt. L’avènement des solutions prédictives et les nouvelles capacités d’analyse des données (Big Data) permettent désormais de proposer des communications interactives personnalisées à chaque individu. L’analyse de la segmentation pour optimiser son mix se conjuguera-t-elle bientôt au passé ?
La nouvelle ère de la personnalisation automatique et en temps réel pour délivrer la bonne information, au bon utilisateur et au bon moment et ainsi devancer vos concurrents n'est plus réservée aux grandes entreprises.
Guide administrateur du CMS Rubedo. Ce guide permet de paramétrer de multiples sites web et/ou e-commerce, d'activer la personnalisation automatique et en temps réel des contenus et des produits pour tous les visiteurs.
Rubedo 2.2 implements a new e-commerce module to complete its multi-site functionalities.
From product management to order management, Rubedo Commerce provides a collection of blocks to manage multiple online stores.
Rubedo Commerce est une solution de gestion de sites/ multi-sites e-commerce.
Adossé aux fonctionnalités de gestion des contenus et des médias de Rubedo cms, Rubedo Commerce offre un environnement complet permettant de gérer plusieurs sites internet, intranet, extranet et e-commerce.
Rubedo Commerce bénéficie ainsi de l’ensemble des fonctionnalités de Rubedo : e-mailing, gestion des droits, médiathèques, studios, enquêtes, …
Pour optimiser les ventes, un moteur de recommandation/personnalisation par ciblage comportemental est intégré dans la solution Rubedo.
Au-delà des performances, un CMS Big Data tel que Rubedo apporte de nombreuses solutions pour tous les utilisateurs : rapidité de développement, évolutivité, souplesse dans la modélisation des données, mais également un enrichissement de l'expérience utilisateur.
Pour aller encore plus loin dans l'innovation pour les utilisateurs, le CMS Rubedo 2.2 intégrera un outil d'analyse comportementale (Behavioral / Content targeting) pour personnaliser les sites web et e-commerce aux préférences des utilisateurs. Les Magic Queries analysent en temps réel les différents parcours des internautes et proposent des contenus personnalisés.
Guide administration pour le CMS Rubedo en version 2.1.0
Ce guide présente les concepts et détaille les étapes de création d'un site avec le CMS open source big data Rubedo.
Le cms open source Rubedo est un cms NoSQL français, cms MongoDB et Zend framework 2 (PHP).
Rubedo propose des fonctions de cms multilingue et des fonctions multi sites à partir d'une interface d'administration unique. Le CMS Rubedo permet ainsi de gérer plusieurs sites en mode super administrateur et de déléguer la gestion des sites. On parle d'un mode multi-administrateurs.
Cette capacité multi-site permet également de proposer des sites avec des langues différentes.
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9. Ecotaxe
§ Flux entrant 24/7
• 2 000 points par seconde
• 200 paquets par seconde
§ Flux sortant 24/7
• 3* 200 paquets par seconde
§ Conservation 3 mois
• 1, 5 Milliard de paquets
• 7 téraoctets
11. Big Data
Règle des 3V
Big data is high-volume, high-velocity and high-variety information
assets that demand cost-effective, innovative forms of information
processing for enhanced insight and decision making.
gartner.com
12. Big Data
Règle des 3V
Big data is high-volume, high-velocity and high-variety information
assets that demand cost-effective, innovative forms of information
processing for enhanced insight and decision making.
gartner.com
Variety
Volume
Velocity
29. Ecotaxe
§ Flux entrant 24/7
• 2 000 points par seconde
• 200 paquets par seconde
§ Flux sortant 24/7
• 3* 200 paquets par seconde
§ Conservation 3 mois
• 1, 5 Milliard de paquets
• 7 téraoctets
#Volume #Velocity
31. RETEX MongoDB
Changement de paradigme
§ En phase amont
Lutter contre la peur des décideurs / la résistance des équipes
§ En phase de spécifications /réalisation
Intégrer l’approche documentaire vs approche relationnelle
Former les équipes de développement
Exemple : logique transactionnelle
§ En phase de production
Lutter contre l’hébergement traditionnel / san
Favoriser l’approche horizontale vs verticale
32. Vertical / Horizontal
« Scalabilité » Verticale
Si besoin de plus de puissance
• on ajoute de la mémoire ….
• puis on remplace par un serveur de gamme plus
puissante
Corollaire : les machines sont surdimensionnées
pour absorber une augmentation potentielle de
charge
33. Vertical / Horizontal
« Scalabilité » Verticale
Si besoin de plus de puissance
• on ajoute de la mémoire ….
• puis on remplace par un serveur de gamme plus
puissante
Corollaire : les machines sont surdimensionnées
pour absorber une augmentation potentielle de
charge
34. Vertical / Horizontal
« Scalabilité» Horizontale
Si besoin de plus de puissance
• on ajoute des serveurs
Corollaire : linéarisation du coût / usage
35. Vertical / Horizontal
« Scalabilité» Horizontale
Si besoin de plus de puissance
• on ajoute des serveurs
Corollaire : linéarisation du coût / usage
36. MongoDB
Ne pas utilisez MongoDB si votre système est transactionnel, pour le reste …
§ Avantages
• Qualité de la documentation
• Mise en œuvre rapide
• Versatilité
§ Inconvénient
• Sharding pas si simple !
§ Bénéfices
• Agilité fonctionnelle
• Evolution du modèle aisée / versionnement natif
• Agilité technique
• Alignement matériel par rapports aux usages
43. RETEX Rubedo
Premier CMS open-source
basé sur un socle NoSQL
+
Dans un monde où
LAMP est LA Norme
NoSQL, mais pour quoi faire ?
44. NoSQL et Gestion de contenus
§ Les CMS gèrent des Contenus …
… structurés
et
classés
45. Rubedo : comparaison des approches
Approche relationnelle
type MySQL
Pour un type de contenu : 6 tables
Pour 10 types de contenus : 29 tables
1 requête unitaire = 6 tables et 2 jointures
Approche NoSQL
documentaire
type MongoDB
Pour un type de contenu : 1 collection
Pour 10 types de contenus : 1 collection
1 requête unitaire : 1 collection
46. Rubedo : les atouts du NoSQL
§ Atouts Fonctionnels
§ Limites & précautions
• Souplesse de modélisation
• Evolutivité dans le temps
• Fonctionnalités de Recherche
•
•
Pas de transactions
Déport des règles métiers dans
la couche applicative
§ Atouts Techniques
•
•
•
•
•
Performances en lecture/écriture
Stockage de grands volumes
Montée en charge linéaire
Gestion des fichiers intégrée (MongoDB) •
Sécurité centralisée
•
Framework de développement
indispensable !
Certaines typologies de projets
peuvent nécessiter une
architecture hybride (site de ecommerce complexe par
exemple)
47. Rubedo : les cas d’usage
Performances &
Volumétrie
Mobilité
Ergonomie
Souplesse
Use
cases
Recherche &
Géolocalisation
Ouverture &
Extensibilité
§ Portails à fort trafic ou volumétrie § Contenus géo-localisés & cartographie
§ Moteurs de recherche verticaux
§ Plateformes multi-sites
§ Plateformes de contribution décentralisées
§ Sites mobiles
52. Agenda
• Purpose of Elasticsearch
• Features of Product
• Customer Examples
• Company Overview
• Commercial Offerings
• Resources
53. Purpose of Elasticsearch
• Organize data and make it easily accessible
– Through powerful search and analytics
– Easily consumable (even for non-data scientists)
– Elegantly handles extremely large data volumes
– Delivers results in real time
• Technology stack agnostic
• Used across all market verticals
54. Features of Elasticsearch
• Structured & unstructured search
• Advanced analytics capabilities
• Unmatched performance
• Real-time results
• Highly scalable
• User friendly installation and maintenance
62. Company Overview
More than 5 million downloads
400,000 New Downloads per Month
1000s of Mission Critical Implementations
Top Investors: Benchmark Capital, Index
Ventures
• Seasoned Executive Team
•
•
•
•
– Founded by Creator of Elasticsearch
– Seasoned Executives from SpringSource
64. User Raves
Chris Cowan @uhduh
I’m in love with @elasticsearch! I want to use it for everything right now!
Alain Richardt @alaincxs
Moving ffrom #solr to # Elasticsearch is like upgrading from a Reliant Robin to a McLaren
F1
Pete Connolly @peteconnolly
Two really useful and productive days of training from @kimchy and @uboness all about
#elasticsearch. Best training course in years
Cyril Lacôte @clacote
#ElasticSearch is the s*&t. Amazingly simple and powerful. Open source is awesome.
That's made my day.
Logan Lowell @fractaloop
Tweaking @elasticsearch for huge indexes can be fun. I'm very glad the IRC channel is so
helpful too.
65. Product Offerings:
Support Throughout Your Project
1. Core Elasticsearch Training
2. Development and Production Support
3. Technical Account Manager
66. 1: Training
Core Elasticsearch Training
• Two day classroom training
• Delivered by Elasticsearch developers
1. Worldwide Public Courses
2. Onsite Training Course
68. 3: Technical Account Manager
•
•
•
•
•
Named technical resource
Single point of contact into Elasticsearch
Onboarding call to assess your goals
Four health checks per year
Go-to expert to drive success with your
Elasticsearch deployment
72. Top Big Data Challenges?
Translation?
Most struggle
to know what
Big Data is,
how to manage
it and who can
manage it
3
Source: Gartner
73. Understanding Big Data – It’s Not Very “Big”
64% - Ingest diverse,
new data in real-time
15% - More than 100TB
of data
20% - Less than 100TB
(average of all? <20TB)
from Big Data Executive Summary – 50+ top executives from Government and F500 firms
4
75. 6
Applications
CRM, ERP, Collaboration, Mobile, BI
Data Management
Online Data
RDBMS
RDBMS
Offline Data
Hadoop
Infrastructure
OS & Virtualization, Compute, Storage, Network
EDW
Security & Auditing
Management & Monitoring
Enterprise Big Data Stack
76. Consideration – Online vs. Offline
Online
• Real-time
• Low-latency
• High availability
7
vs.
Offline
• Long-running
• High-Latency
• Availability is lower priority
78. MongoDB/NoSQL Is Good for!
360° View of the
Customer
Fraud Detection
User Data
Management
Content
Management &
Delivery
Reference Data
Product Catalogs
9
Mobile & Social
Apps
Machine to
Machine Apps
Data Hub
79. Hadoop Is Good for!
Risk Modeling
Recommendation
Engine
Ad Targeting
Transaction
Analysis
Trade
Surveillance
Network Failure
Prediction
10
Churn Analysis
Search Quality
Data Lake
81. Case Study
Insurance leader generates coveted 360-degree view of
customers in 90 days – “The Wall”
Problem
•
No single view of
customer
•
145 yrs of policy data,
70+ systems, 15+ apps
•
2 years, $25M trying to
aggregate in RDBMS –
failed
Why MongoDB
• Agility – prototype in 5
days; production in 90
days
• Dynamic schema & rich
querying – combine
disparate data into one
data store
• Hot tech to attract top
talent
12
Results
• Increased call center
productivity
• Better customer
experience, reduced
churn, more upsell opps
• Dozens more projects in
the works to leverage
this data platform
85. MongoDB Vision
To provide the best database for how we build and
run apps today
Build
– New and complex data
– Flexible
– New languages
– Faster development
16
Run
– Big Data scalability
– Real-time
– Commodity hardware
– Cloud
86. Fortune 500 & Global 500
• 10 of the Top Financial Services Institutions
• 10 of the Top Electronics Companies
• 10 of the Top Media and Entertainment
Companies
• 8 of the Top Retailers
• 6 of the Top Telcos
• 5 of the Top Technology Companies
• 4 of the Top Healthcare Companies
17
88. MongoDB Features
• JSON Document Model
with Dynamic Schemas
• Full, Flexible Index Support
and Rich Queries
• Auto-Sharding for
Horizontal Scalability
• Built-In Replication for High
Availability
• Text Search
• Advanced Security
• Aggregation Framework
and MapReduce
• Large Media Storage with
GridFS
19
92. MongoDB Products and Services
Subscriptions
MongoDB Enterprise, MMS (On-Prem), Professional Support,
Commercial License
Consulting
Expert Resources for All Phases of MongoDB Implementations
Training
Online and In-Person for Developers and Administrators
MongoDB Management Service (MMS)
Cloud-Based Suite of Services for Managing MongoDB
Deployments
23
94. MongoDB Enterprise
Enterprise build with value-added capabilities
• Advanced Security w/Kerberos
• On-Prem Management
– Visualization and alerts on 100+ system metrics
– Backup features coming soon
– On-premise version of MongoDB Monitoring Services (MMS)
• Enterprise Software Integration via SNMP
• Private, On-Demand MongoDB University Training
• Certified OS Support
25
95. MongoDB Management Service
Cloud-based suite of services for managing
MongoDB deployments
• Monitoring, with charts,
dashboards and alerts on 100+
metrics
• Backup and restore, with pointin-time recovery, support for
sharded clusters
• MMS On-Prem included with MongoDB Enterprise
(backup coming soon)
26
96. Consulting
Technical Account
Manager
Custom Consulting
• Named MongoDB
expert
• Assist with all phases of
project
• Advisory services
• E.g., config., testing,
optimization, best
practices
• Ongoing basis
Lightning Consults also available
27
Health Check
• Assess overall status
and health of existing
MongoDB deployment
97. Training
Public
Private
• Dev, admin, and
combined courses
available
• North America and
EMEA
• Customized to your
needs
• For devs and admins
• On-Site
Online
• Free
• For devs and admins
• 7 weeks
• Weekly lectures,
homework, final exam
Private, On-Demand MongoDB University Training
Included with MongoDB Enterprise Subscription
28
98. For More Information
Resource
MongoDB Downloads
mongodb.com/download
Free Online Training
education.mongodb.com
Webinars and Events
mongodb.com/events
White Papers
mongodb.com/white-papers
Case Studies
mongodb.com/customers
Presentations
mongodb.com/presentations
Documentation
docs.mongodb.org
Additional Info
29
Location
info@mongodb.com