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.
every business needs a data analytics to get a detailed value of cost and profits. we will study the importance in detail in this particular presentation.
Role of business intelligence in knowledge managementShakthi Fernando
This study is fundamentally based on the most common components of a Business Intelligence System, data warehouses, ETL tools, OLAP techniques and data mining, which comfort the decision making function. It further describe about the role of each component in a Business Intelligence System and how Business Intelligence Systems can be used for better business decision making at each level of management.
A short overview about Business Intelligence. What BI is in short, how BI market is growing, what vendors are operating in the market today. Future directions.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
every business needs a data analytics to get a detailed value of cost and profits. we will study the importance in detail in this particular presentation.
Role of business intelligence in knowledge managementShakthi Fernando
This study is fundamentally based on the most common components of a Business Intelligence System, data warehouses, ETL tools, OLAP techniques and data mining, which comfort the decision making function. It further describe about the role of each component in a Business Intelligence System and how Business Intelligence Systems can be used for better business decision making at each level of management.
A short overview about Business Intelligence. What BI is in short, how BI market is growing, what vendors are operating in the market today. Future directions.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
What is BI,Definition, examples, BI industry, Solutions, Evolution, Catogeries, Key Stages of BI, BI significance, BI technologies, tools, future of BI
Business Intelligence, Portals, Dashboards and Operational Matrix with ShareP...Optimus BT
Key areas of growth in Business Intelligence with SharePoint | Optimus BT
The business environment has driven organizations to view BI as more than technology with strategy, end user adoption, collaboration, real time predictions, pervasive deployments, and mobile adoptions that allows for intelligent business decision making process while keeping cost and IT expertise at the forefront.
This presentation provides an overview of current state of Business Intelligence, portals and dashboards has been deployed enterprise wide, challenges in the existing deployments and how Business Intelligence can be scaled up to the end users in terms of cross organizational collaboration, business initiatives, data availability, user acceptance and data source integration. Even more, this presentation talks about the trends that drive BI which includes but not limited to predictive & real time analysis, Mobile Business Intelligence and Social BI within the process framework, Social Media Analytics, Self Service BI. Towards the later part of the presentation we talk about the vision of a BI solution to meet business initiatives, and how SharePoint 2010 will be a clear winner in terms of how various platforms stack up. There are few real implementation screens illustrated towards the end w.r.t Sales, financial and decision making portal dashboards.
Optimus BT provides end to end SharePoint Business Intelligence Software Consulting and solution implementation services. Using SQL server integration capabilities and SharePoint excel services our BI solutions bring analytics from data warehouse systems into an intuitive dashboard which helps real time reporting with self service tools. Learn more @ www.optimusbt.com/sharepoint_business_intelligence
The Economic Value of Data: A New Revenue Stream for Global CustodiansCognizant
Global custodians' big data offers myriad opportunities for generating value from analytics solutions; we explore various paths and offer three use cases to illustrate. Data aggregation, risk management, digital experience, operational agility and cross-selling are all covered.
How to Create and Manage a Successful Analytics OrganizationDATAVERSITY
For the last few years, analytics, data science and data management have achieved tremendous exposure on all the media channels. Big Data has become a major topic of discussion, catalyzing attention among the C-Level executives and driving investments and projects inside the enterprise. However, it is really interesting that just a selected group of business has created successful data teams and has mastered the skills to manage it. What we have seen is that most companies still do not know how to create, implement and manage a data and analytics organization. Above all, if data has become an strategic asset and is being considered the new oil for the 21st century economy, what your strategy to handle it ? This webinar will help to bring some concepts and ideas to enlighten your path to create and manage an analytics organization, providing some real life examples on companies which did it.
Business intelligence, Data Analytics & Data VisualizationMuthu Natarajan
Business Intelligence, Cloud Computing, Data Analytics, Data Scrubbing, Data Mining, Big Data & Intelligence, How to use Data into Information, Decision Based,Methods for Business Intelligence, Advanced Analytics, OLAP, MultiDimensional Data, Data Visualization
Big Data Analytics Architecture Powerpoint Presentation SlidesSlideTeam
"You can download this product from SlideTeam.net"
Select our content ready Big Data Analytics Architecture PowerPoint Presentation Slides to showcase the process of data curation and analysis. This big data analysis framework PowerPoint complete deck comprises of professionally designed PPT slides like conceptual view of big data reference, different types of data, important aspects, unified information management, real-time analytics, intelligent process, architecture principles, all forms of data, consistent information and object model, integrated analysis, insight to action, etc. Demonstrate how to connect information from different areas using the big data management presentation design. Big data analytics framework presentation deck also goes well with various related topics such as big data processing, data science, data warehouse, data storage, data analysis, data virtualization, modern data architecture and many more. Data analytics platform PPT design is a helpful tool to simplify the data discovery process. Showcase unified approach of data management with ready to use big data storage architecture PowerPoint template. Address feelings of inferiority with our Big Data Analytics Architecture Powerpoint Presentation Slides. Enhance the confidence they have in their ability. https://bit.ly/3sLV07g
Big Data Analytics Architecture PowerPoint Presentation SlidesSlideTeam
Presenting this set of slides with name - Big Data Analytics Architecture Powerpoint Presentation Slides. This PPT deck displays twenty six slides with in depth research. Our topic oriented Big Data Analytics Architecture Powerpoint Presentation Slides presentation deck is a helpful tool to plan, prepare, document and analyse the topic with a clear approach. We provide a ready to use deck with all sorts of relevant topics subtopics templates, charts and graphs, overviews, analysis templates. Outline all the important aspects without any hassle. It showcases of all kind of editable templates infographs for an inclusive and comprehensive Big Data Analytics Architecture Powerpoint Presentation Slides presentation. Professionals, managers, individual and team involved in any company organization from any field can use them as per requirement.
What is BI,Definition, examples, BI industry, Solutions, Evolution, Catogeries, Key Stages of BI, BI significance, BI technologies, tools, future of BI
Business Intelligence, Portals, Dashboards and Operational Matrix with ShareP...Optimus BT
Key areas of growth in Business Intelligence with SharePoint | Optimus BT
The business environment has driven organizations to view BI as more than technology with strategy, end user adoption, collaboration, real time predictions, pervasive deployments, and mobile adoptions that allows for intelligent business decision making process while keeping cost and IT expertise at the forefront.
This presentation provides an overview of current state of Business Intelligence, portals and dashboards has been deployed enterprise wide, challenges in the existing deployments and how Business Intelligence can be scaled up to the end users in terms of cross organizational collaboration, business initiatives, data availability, user acceptance and data source integration. Even more, this presentation talks about the trends that drive BI which includes but not limited to predictive & real time analysis, Mobile Business Intelligence and Social BI within the process framework, Social Media Analytics, Self Service BI. Towards the later part of the presentation we talk about the vision of a BI solution to meet business initiatives, and how SharePoint 2010 will be a clear winner in terms of how various platforms stack up. There are few real implementation screens illustrated towards the end w.r.t Sales, financial and decision making portal dashboards.
Optimus BT provides end to end SharePoint Business Intelligence Software Consulting and solution implementation services. Using SQL server integration capabilities and SharePoint excel services our BI solutions bring analytics from data warehouse systems into an intuitive dashboard which helps real time reporting with self service tools. Learn more @ www.optimusbt.com/sharepoint_business_intelligence
The Economic Value of Data: A New Revenue Stream for Global CustodiansCognizant
Global custodians' big data offers myriad opportunities for generating value from analytics solutions; we explore various paths and offer three use cases to illustrate. Data aggregation, risk management, digital experience, operational agility and cross-selling are all covered.
How to Create and Manage a Successful Analytics OrganizationDATAVERSITY
For the last few years, analytics, data science and data management have achieved tremendous exposure on all the media channels. Big Data has become a major topic of discussion, catalyzing attention among the C-Level executives and driving investments and projects inside the enterprise. However, it is really interesting that just a selected group of business has created successful data teams and has mastered the skills to manage it. What we have seen is that most companies still do not know how to create, implement and manage a data and analytics organization. Above all, if data has become an strategic asset and is being considered the new oil for the 21st century economy, what your strategy to handle it ? This webinar will help to bring some concepts and ideas to enlighten your path to create and manage an analytics organization, providing some real life examples on companies which did it.
Business intelligence, Data Analytics & Data VisualizationMuthu Natarajan
Business Intelligence, Cloud Computing, Data Analytics, Data Scrubbing, Data Mining, Big Data & Intelligence, How to use Data into Information, Decision Based,Methods for Business Intelligence, Advanced Analytics, OLAP, MultiDimensional Data, Data Visualization
Big Data Analytics Architecture Powerpoint Presentation SlidesSlideTeam
"You can download this product from SlideTeam.net"
Select our content ready Big Data Analytics Architecture PowerPoint Presentation Slides to showcase the process of data curation and analysis. This big data analysis framework PowerPoint complete deck comprises of professionally designed PPT slides like conceptual view of big data reference, different types of data, important aspects, unified information management, real-time analytics, intelligent process, architecture principles, all forms of data, consistent information and object model, integrated analysis, insight to action, etc. Demonstrate how to connect information from different areas using the big data management presentation design. Big data analytics framework presentation deck also goes well with various related topics such as big data processing, data science, data warehouse, data storage, data analysis, data virtualization, modern data architecture and many more. Data analytics platform PPT design is a helpful tool to simplify the data discovery process. Showcase unified approach of data management with ready to use big data storage architecture PowerPoint template. Address feelings of inferiority with our Big Data Analytics Architecture Powerpoint Presentation Slides. Enhance the confidence they have in their ability. https://bit.ly/3sLV07g
Big Data Analytics Architecture PowerPoint Presentation SlidesSlideTeam
Presenting this set of slides with name - Big Data Analytics Architecture Powerpoint Presentation Slides. This PPT deck displays twenty six slides with in depth research. Our topic oriented Big Data Analytics Architecture Powerpoint Presentation Slides presentation deck is a helpful tool to plan, prepare, document and analyse the topic with a clear approach. We provide a ready to use deck with all sorts of relevant topics subtopics templates, charts and graphs, overviews, analysis templates. Outline all the important aspects without any hassle. It showcases of all kind of editable templates infographs for an inclusive and comprehensive Big Data Analytics Architecture Powerpoint Presentation Slides presentation. Professionals, managers, individual and team involved in any company organization from any field can use them as per requirement.
Business intelligence- Components, Tools, Need and Applicationsraj
As part of the research project for the course Technical Foundations of Information Systems at the University of Illinois, our team worked on the topic, Business Intelligence. The presentation focuses on what is Business Intelligence, its various components, latest tools, the need of BI as well as applications of this technology. This project deals with the latest development of BI technologies (hardware or software) and includes comprehensive literature survey from Journals, and the Internet.
Enterprise Architecture - An Introduction Daljit Banger
The Slides are from my session at "An Evening of Enterprise Architecture Awareness" held at theUniversity of Sussex Hosted by the BCS Local Chapter and facilitated by the BCS EA Specialist Group.
Big data automation is gaining traction as industries start capturing more data. Know how data analysts and data scientists can take advantage of automation.
Big data automation is gaining traction as industries start capturing more data. Know how data analysts and data scientists can take advantage of automation.
https://www.dasca.org/
Now companies are in the middle of a renovation that forces them to be analytics-driven to
continue being competitive. Data analysis provides a complete insight about their business. It
also gives noteworthy advantages over their competitors. Analytics-driven insights compel
businesses to take action on service innovation, enhance client experience, detect irregularities in
process and provide extra time for product or service marketing. To work on analytics driven
activities, companies require to gather, analyse and store information from all possible sources.
Companies should bring appropriate tools and workflows in practice to analyse data rapidly and
unceasingly. They should obtain insight from data analysis result and make changes in their
business process and practice on the basis of gained result. It would help to be more agile than
their previous process and function.
The need, applications, challenges, new trends and
a consulting perspective
(Why is Big Data a strategic need for optimization of organizational processes especially in the business domains and what is the consultant’s role?)
With every transaction and activity, organizations churn out data. This process happens even in the case of idle operation. Hence, data needs to be effectively analyzed to manage all processes better. Data can be used to make sense of the current situation and predict outcomes. It also can be used to optimize business processes and operations. This is easier said than done as data is being produced at an unprecedented rate, huge volumes and a high degree of variety. For the outcome of the data analysis to be relevant, all the data sets must be factored in to the analysis and predictions. This is where big data analysis comes in with its sophisticated tools that are also now easy on the pocket if one prefers the open source.
The future of high potential marketing lead generation would be based on big data. Virtually every business vertical can benefit from big data initiatives. Even those without deep pockets can use the cloud model for business analytics/big data analysis.
Some challenges remain to be addressed to engender large scale adoption but the current benefits outweigh the concerns.
India has seen a massive growth in big data adoption and the trend will grow though it is generally amongst the bigger players. As quality of data improves and customer reluctance to being honest when they volunteer data reduces, the forecasts will become more accurate and Big Data will have come to its rightful place as a key enabler.
How Analytics Has Changed in the Last 10 Years (and How It’s Staye.docxpooleavelina
How Analytics Has Changed in the Last 10 Years (and How It’s Stayed the Same)
· Thomas H. Davenport
June 22, 2017
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Photo by Ferdinand Stöhr
Ten years ago, Jeanne Harris and I published the book Competing on Analytics, and we’ve just finished updating it for publication in September. One major reason for the update is that analytical technology has changed dramatically over the last decade; the sections we wrote on those topics have become woefully out of date. So revising our book offered us a chance to take stock of 10 years of change in analytics.
Of course, not everything is different. Some technologies from a decade ago are still in broad use, and I’ll describe them here too. There has been even more stability in analytical leadership, change management, and culture, and in many cases those remain the toughest problems to address. But we’re here to talk about technology. Here’s a brief summary of what’s changed in the past decade.
The last decade, of course, was the era of big data. New data sources such as online clickstreams required a variety of new hardware offerings on premise and in the cloud, primarily involving distributed computing — spreading analytical calculations across multiple commodity servers — or specialized data appliances. Such machines often analyze data “in memory,” which can dramatically accelerate times-to-answer. Cloud-based analytics made it possible for organizations to acquire massive amounts of computing power for short periods at low cost. Even small businesses could get in on the act, and big companies began using these tools not just for big data but also for traditional small, structured data.
Insight Center
· Putting Data to Work
Analytics are critical to companies’ performance.
Along with the hardware advances, the need to store and process big data in new ways led to a whole constellation of open source software, such as Hadoop and scripting languages. Hadoop is used to store and do basic processing on big data, and it’s typically more than an order of magnitude cheaper than a data warehouse for similar volumes of data. Today many organizations are employing Hadoop-based data lakes to store different types of data in their original formats until they need to be structured and analyzed.
Since much of big data is relatively unstructured, data scientists created ways to make it structured and ready for statistical analysis, with new (and old) scripting languages like Pig, Hive, and Python. More-specialized open source tools, such as Spark for streaming data and R for statistics, have also gained substantial popularity. The process of acquiring and using open source software is a major change in itself for established busines ...
Azure Synapse: data lake & modern data warehouse dalla A alla ZRoberto Messora
Con Azure Synapse abbiamo finalmente a disposizione un ambiente integrato in cui poter implementare compiutamente un modern Data Warehouse. Abbiamo ormai capito sul campo che non ha senso mettere in competizione fra di loro data lake e data warehouse, con Azure Synapse la piena collaborazione fra di loro diventa il punto di forza di una strategia sui dati che unifica in un unico ambiente data ingestion, data preparation e analytics.In questa sessione verrà mostrato come Azure Synapse permetta di fare tutto questo a partire dal dato grezzo proveniente dalle più svariate fonti dati.
Azure Data Factory: l'evoluzione della specie della data integrationRoberto Messora
Microsoft definisce Azure Data Factory come un servizio gestito di hybrid data integration, una descrizione fin troppo generica per una delle componenti più importanti della cloud data platform.
In questa sessione entreremo nel merito delle funzionalità offerte da Data Factory, degli scenari di data integration supportati e delle opzioni di security soprattutto in contesti ibridi cloud/on-premise.
Scopriremo che trasferire e trasformare dati nel cloud può essere semplice e relativamente poco costoso.
Sviluppare un'applicazione web basata su ASP.NET Core nel mondo reale con Visual Studio Code
Codice della demo:
https://github.com/robymes/JoinTheExpert-WebDay
L'avvento dei container nello scenario IT ci fornisce una soluzione in più per il consolidamento dei nostri server di esercizio. In questa sessione vedremo come utilizzare Docker al fine di effettuare il deploy di una soluzione che utilizza alcune delle applicazioni più diffuse, sia on-premise che in-the-cloud, Azure o Amazon che sia, in modo da ridurre drasticamente l'incertezza dei side-effect di ambiente passando da uno all'altro.
Event streaming pipeline with Windows Azure and ArcGIS Geoevent extensionRoberto Messora
Real time monitoring and Internet of Things are key success factors in many business activities.
In this presentation we will show how we solved a common issue in managing a large number of different types of event per second that contain some sort of geographical information.
We built a processing pipeline leveraging the high ingestion capabilities of Windows Azure Event Hub and Stream Analytics, then applying location analytics procedures with ArcGIS GeoEvent Processor.
In this way we can select just the informations we need to be processed by the ArcGIS platform, reducing the number of events and normalizing data content.
Code quality e test automatizzati con JavaScriptRoberto Messora
JavaScript è ormai ovunque nel mondo dello sviluppo web, è sbarcato persino sul server, la produzione di codice è aumentata a dismisura, framework e librerie sono spuntati come funghi, ma... siamo sicuri di mandare in produzione codice di qualità? quali strumenti e quali tecniche abbiamo a disposizione per aumentare la confidenza circa la bontà di ciò che scriviamo? Quali strategie possiamo adottare per migliorare il ciclo di vita delle nostre soluzioni e le attività di sviluppo?
In questa sessione proveremo a illustrare come organizzare la codebase di una tipica solution JavaScript, quali strategie adottare per migliorare la qualità del codice a cominciare dallo unit testing, quali strumenti utilizzare per automatizzare tutte le attività ripetitive a valle della scrittura del codice,
L'obiettivo è quello di proporre un modo per disciplinare le attività di sviluppo e rendere il più possibile confortevole la vita professionale dello sviluppatore web.
HTML5 Single Page Application è il nuovo hype tecnologico: tutti ne parlano, il web ne è pervaso, da GMail a Facebook e Twitter, dal desktop al mobile, dagli Appennini alle Ande.
In questa sessione ci occuperemo di tutti quegli aspetti di organizzazione di una solution in termini di codebase, unit testing e processo di build, presentando alcuni strumenti che stanno emergendo fra quelli disponibili.
Demo: http://www.communitydays.it/events/2014-Roma/web02/
Abbiamo sdoganato JavaScript a tutti i livelli: è diventato un linguaggio di programmazione di prima classe e ne abbiamo cominciato a conoscere i segreti e le caratteristiche. Ma non basta, le applicazioni web client si fanno sempre più complesse e non è pensabile fare debug solo a colpi di F5 e Firebug. In questa sessione vi svelerò un segreto: anche in Javascript è possibile fare Unit Testing, darò alcune indicazioni su come organizzare la codebase in modo da farlo con intelligenza.
HTML5 Single Page Application è il nuovo hype tecnologico: tutti ne parlano, il web ne è pervaso, da GMail a Facebook e Twitter, dal desktop al mobile, dagli Appennini alle Ande.
In questa sessione proveremo a capire che cosa sia una SPA a partire dal ruolo centrale che riveste Javascript sia in termini di librerie di base che di organizzazione del codice applicativo. Affronteremo anche temi inerenti la UI, i servizi di back-end, lo unit testing, la security, il mobile in modo da offrire un panorama completo di che cosa sia in effetti una SPA HTML5.
Javascript avanzato: sfruttare al massimo il webRoberto Messora
Javascript è uno dei linguaggi più sottovalutati e più incompresi dell'intero panorama dei linguaggi di programmazione, eppure è anche uno dei più utilizzati.
Da una parte le molteplici e differenti declinazioni degli strumenti di navigazione web, dall'altra l'infelice scelta storica di usare il termine "script", hanno contribuito alla creazione del mito di un linguaggio poco rigoroso, al servizio di ogni sorta di trucco o pezza di codice.
La verità invece racconta di un linguaggio dinamico ad oggetti a tutti gli effetti, con caratteristiche molto interessanti, seppur con qualche difetto, ma soprattutto un linguaggio che, sull'onda di HTML5, rivestirà se possibile ancora più importanza nell'immediato futuro.
In questa sessione verranno presentati aspetti poco conosciuti, ma molto importanti, di Javascript (scoping, hoisting, closures, ecc.), verranno presentati alcuni design patterns che permettono di strutturare in maniera intelligente le nostre librerie applicative in funzione della manutenibilità e delle performance, senza tralasciare, ove possibile, uno sguardo ad alcuni framework come jQuery o KnockoutJS.
Con l'avvento su scala globale di HTML5 le tecnologie web si sono evolute cercando di offrire all'utente una migliore esperienza applicativa sempre più simile a quella desktop. Sul piano tecnico questo viene realizzato spostando la logica di presentazione sul browser client facendo leva su Javascript e CSS3. In questa sessione vedremo come KnockoutJS, un presentation framework Javascript basato sul pattern Model-View-ViewModel, permette di sviluppare Rich Internet Application (RIA) analizzando le sue caratteristiche implementative e mostrando esempi di casi reali anche in ambito mobile.
MV* presentation frameworks in Javascript: en garde, pret, allez!Roberto Messora
HTML5 is the playing area, the strip, Javascript presentation frameworks are the fences and they are fierce and proud. In this presentation we will attend an interesting match between two of the emerging contenders in the MV* family: KnockoutJS and BackboneJS. We'll try to understand how they solve the same issues in modern web software development to better decide which one is suitable in our scenario.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...2023240532
Quantitative data Analysis
Overview
Reliability Analysis (Cronbach Alpha)
Common Method Bias (Harman Single Factor Test)
Frequency Analysis (Demographic)
Descriptive Analysis
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
2. Big Data, Analytics, AI, Machine Learning, Deep Learning
Executive Overview
Big Data & Analytics Reference Architectures Conceptual View
Big Data & Analytics Reference Architectures Logical View
Big Data & Analytics Reference Architectures Technological View
Analytics Overview and Case Studies
Event Store
Domain Model
Cloudera
Agenda
3. Big Data, Analytics, AI, Machine Learning, Deep Learning
Executive Overview
Big Data & Analytics Reference Architectures Conceptual View
Big Data & Analytics Reference Architectures Logical View
Big Data & Analytics Reference Architectures Technological View
Analytics Overview and Case Studies
Event Store
Domain Model
Cloudera
Agenda
7. Big Data, Analytics, AI, Machine Learning, Deep Learning
Executive Overview
Big Data & Analytics Reference Architectures Conceptual View
Big Data & Analytics Reference Architectures Logical View
Big Data & Analytics Reference Architectures Technological View
Analytics Overview and Case Studies
Event Store
Domain Model
Cloudera
Agenda
8. Data is often considered to be the crown jewels of an organization.
1) Most companies already use analytics in the form of reports and dashboards to help run
their business. This is largely based on well structured data from operational systems that
conform to pre-determined relationships (“a single version of the truth”).
2) Big Data, however, doesn’t follow this structured model. The streams are all different and it
is difficult to establish common relationships. But with its diversity and abundance come
opportunities to learn and to develop new ideas – ideas that can help change the business
(“a single version of the facts”)
The architectural challenge is to bring the two paradigms together. So, rather than approach Big
Data as a new technology silo, an organization should strive to create a unified information
architecture – one that enables it to leverage all types of data, as situations demand, to
promptly satisfy business needs.
The objective of this workshop is to describe a reference architecture (and its implementation)
that promotes a unified vision for information management and analytics.
Executive Overview
8
10. Big Data, Analytics, AI, Machine Learning, Deep Learning
Executive Overview
Big Data & Analytics Reference Architectures Conceptual View
Big Data & Analytics Reference Architectures Logical View
Big Data & Analytics Reference Architectures Technological View
Analytics Overview and Case Studies
Event Store
Domain Model
Cloudera
Agenda
11. The architecture is organized into views that highlight three focus areas:
1. universal information management
2. real-time analytics
3. intelligent processes
They represent architecturally significant capabilities that are important to most organizations
today.
Big Data & Analytics Reference Architectures Conceptual View
11
12. Unified Information Management addresses the need to manage information holistically as
opposed to maintaining independently governed silos. At a high level this includes:
o High Volume Data Acquisition – The system must be able to acquire data despite high
volumes, velocity, and variety. It may not be necessary to persist all data that is received.
o Multi-Structured Data Organization and Discovery – The ability to navigate and search across
different forms of data can be enhanced by the capability to organize data of different
structures into a common schema.
o Low Latency Data Processing – Data processing can occur at many stages of the architecture.
In order to support the processing requirements of Big Data, the system must be fast and
efficient.
o Single Version of the Truth – When two people perform the same form of analysis they
should get the same result. As obvious as this seems, it isn’t necessarily a small feat,
especially if the two people belong to different departments or divisions of a company. Single
version of truth requires architecture consistency and governance.
Unified Information Management
12
13. Real-Time Analytics enables the business to leverage information and analysis as events are
unfolding. At a high level this includes:
o Speed of Thought Analysis – Analysis is often a journey of discovery, where the results of one
query determine the content of the next. The system must support this journey in an
expeditious manner. System performance must keep pace with the users’ thought process.
o Interactive Dashboards – Interactive dashboards allow the user to immediately react to
information being displayed, providing the ability to drill down and perform root cause
analysis of situations at hand.
o Advanced Analytics – Advanced forms of analytics, including data mining, machine learning,
and statistical analysis enable businesses to better understand past activities and spot trends
that can carry forward into the future. Applied in real-time, advanced analytics can enhance
customer interactions and buying decisions, detect fraud and waste, and enable the business
to make adjustments according to current conditions.
o Event Processing – Real-time processing of events enables immediate responses to existing
problems and opportunities. It filters through large quantities of streaming data, triggering
predefined responses to known data patterns.
Real-Time Analytics
13
14. A key objective for any Big Data and Analytics program is to execute business processes more
effectively and efficiently. This means channeling the intelligence one gains from analysis
directly into the processes that the business is performing. At a high level this includes:
o Application-Embedded Analysis – Many workers today can be classified as knowledge
workers; they routinely make decisions that affect business performance. Embedding analysis
into the applications they use helps them to make more informed decisions.
o Optimized Rules and Recommendations –With optimized rules and recommendations,
insight from analysis is used to influence the decision logic as the process is being executed.
o Guided User Navigation – Whenever possible the system should leverage the information
available in order to guide the user along the most appropriate path of investigation.
o Performance and Strategy Management – Analytics can also provide insight to guide and
support the performance and strategy management processes of a business. It can help to
ensure that strategy is based on sound analysis. Likewise, it can track business performance
versus objectives in order to provide insight on strategy achievement.
Intelligent Processes
14
15. Big Data, Analytics, AI, Machine Learning, Deep Learning
Executive Overview
Big Data & Analytics Reference Architectures Conceptual View
Big Data & Analytics Reference Architectures Logical View
Big Data & Analytics Reference Architectures Technological View
Analytics Overview and Case Studies
Event Store
Domain Model
Cloudera
Agenda
16. Big Data & Analytics Reference Architectures Logical View
16
The high-level logical view defines a multi-tier architecture template that can be used to describe
many types of technology solutions.
17. Big Data & Analytics Reference Architectures Logical View
17
This layer includes the hardware
and platforms on which the Big
Data and Analytics components
run. As shared infrastructure, it
can be used to support multiple
concurrent implementations, in
support of, or analogous to, Cloud
Computing.
This layer includes infrastructure to
support traditional databases,
specialized Big Data management
systems, and infrastructure that
has been optimized for analytics.
18. Big Data & Analytics Reference Architectures Logical View
18
At the bottom are data stores that
have been commissioned for
specific purposes (g.e. individual
operational data stores, CMS, etc.)
These data stores represent
sources of data that are ingested
(upward) into the Logical Data
Warehouse (LDW). The LDW
represents a collection of data that
has been provisioned for historical
and analytical purposes.
Above the LDW are components
that provide processing and event
detection for all forms of data.
At the top of the layer are
components that virtualize all
forms of data for universal
consumption.
19. Big Data & Analytics Reference Architectures Logical View
19
The Services Layer includes
components that provide or
perform commonly used services.
Presentation Services and
Information Services are types of
Services in a Service Oriented
Architecture (SOA). They can be
defined, cataloged, used, and
shared across solutions. Business
Activity Monitoring, Business
Rules, and Event Handling provide
common services for the
processing layer(s) above.
20. Big Data & Analytics Reference Architectures Logical View
20
The Process Layer represents
components that perform higher
level processing activities. For the
purpose of Big Data and Analytics,
this layer calls out several types of
applications that support
analytical, intelligence gathering,
and performance management
processes.
The Interaction Layer is comprised
of components used to support
interaction with end users.
Common artifacts for this layer
include dashboards, reports,
charts, graphs, and spreadsheets.
In addition, this layer includes the
tools used by analysts to perform
analysis and discovery activities.
21. Big Data & Analytics Reference Architectures Logical View
21
The results of analysis can be
delivered via many different
channels. The architecture calls
out common IP network based
channels such as desktops and
laptops, common mobile network
channels such as mobile phones
and tablets, and other channels
such as email, SMS, and hardcopy.
The architecture is supported by a
number of components that affect
all layers of the architecture. These
include information and analysis
modeling, monitoring,
management, security, and
governance.
22. Big Data, Analytics, AI, Machine Learning, Deep Learning
Executive Overview
Big Data & Analytics Reference Architectures Conceptual View
Big Data & Analytics Reference Architectures Logical View
Big Data & Analytics Reference Architectures Technological View
Analytics Overview and Case Studies
Event Store
Domain Model
Cloudera
Agenda
23. Big Data & Analytics Reference Architectures Technological View
23
24. It lets you publish and subscribe to streams of records. In this respect it is similar to a message
queue or enterprise messaging system.
It lets you store streams of records ia a fault-tolerant way.
It lets you process streams of records as they occur.
Apache Kafka
24
Apache Kafka™ is a distributed streaming platform.
Website: https://kafka.apache.org/
25. Speed –up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk
Ease of use – API in Java, Scala, Python and R
Generality – powerful stack of libraries including SQL and DataFrames, Mllib for machine
learning, GraphX and Spark Streaming
Runs Everywhere - Spark runs on Hadoop, Mesos, standalone, or in the cloud
Apache Spark
25
Apache Spark™ is a fast and general engine for large-scale data processing.
Website: http://spark.apache.org/
27. Reference Architectures – Lambda Architecture
27
Batch Layer manages the master data set, an immutable, append-only set of raw data
Speed Layer
ingest streaming data or micro-batches and provide an «active partition» with a
limited window of mutability
Serving Layer
output from the batch and speed layers are stored in the serving layer (BASE
compliant)
28. Reference Architectures – Lambda Architecture
28
Complexity
Many moving parts
Restatement is difficult
Two code base must be kept in sync
Proper failure handling is complex
29. Reference Architectures – Kappa Architecture
29
Jay Kreps, the creating of Kafka and one of the first proponents of stream-based
architectures, joking called his alternative the “Kappa Architecture”.
31. There are more options today for where to deploy a solution than ever before. At a high level
the four options for deployment of architecture components are:
1) Public Cloud – In the public cloud model, a company rents resources from a third party. The
most advanced usage of public cloud is where the business functionality is provided by the
cloud provider (i.e., software-as-a-service). Public cloud might also be used as the platform
upon which the business functionality is built (i.e., platform-as-a-service), or the public
cloud may simply provide the infrastructure for the system (i.e.,infrastructure-as-a-service).
2) Private Cloud - Private cloud is the same as public cloud, but the cloud is owned by a
company instead of being provided by a third party. Private clouds are ideal for hosting and
integrating very large data volumes while keeping data secure behind corporate firewalls.
3) Managed Services – In this model a company owns the components of the system, but
outsources some or all aspects of runtime operations.
4) Traditional IT – In this model a company owns and operates the system.
These various options for deployment are not mutually exclusive.
Deployment
32
32. Security
33
1) Authentication (Kerberos, LDAP, …)
2) Authorization (ACE, ACL, Sentry,…)
3) Encryption & Data Masking (Over-the-Wire Encryption, Encryption at Rest, Field-
Level Encryption, Format-preserving Encryption)
4) Auditing & Data Lineage
5) Disaster Recovery & Backup
The Keys to secure the enterprise Big Data platform are:
33. Big Data, Analytics, AI, Machine Learning, Deep Learning
Executive Overview
Big Data & Analytics Reference Architectures Conceptual View
Big Data & Analytics Reference Architectures Logical View
Big Data & Analytics Reference Architectures Technological View
Analytics Overview and Case Studies
Event Store
Domain Model
Cloudera
Agenda
35. Analytics - Data Science
36
Notebooks combine code, output and narrative into a single document.
Notebooks
You can condunct analysis writing down code, results, ideas and thoughts.
You have multiple languages and versions in a single multi-tenant environment.
Easy to share
Easy version control
36. 37
Data Science is the science of building data products.
OVERT DATA PRODUCTS COVERT DATA PRODUCTS
• Products where the data is clearly visible
as part of the deliverable.
• Descriptive Analysis
• Dashboarding
• Reporting
• Deliver results rather than data; data is
hidden.
• Recommendation Engine
• …
Website:https://www.oreilly.com/ideas/evolution-of-data-products
Analytics - Data Science Data Products
37. BENEFITS
Analytics allows to better manage Customer Base and extract customer
value
Analyze customer profiles, behaviors and purchases and obtain a complete and strategic view
of the most recurrent customer behaviors
Develop a tailored proposition by customer segment to increase customer value along the
whole client lifecycle
Address marketing efforts based on customer insights and value
Drive consumer segments to exploit product portfolio at the right time of their customer
journey
DIGITAL DIGITAL
38. Analytics will be carried out in order to offer actionable insights on
customer and will follow a multi-step approach
Business
Objective
&Question
Business
Actions
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
%Accounts
Deciles
Responders
Non Responders
Model Interpretation
Modeling
Data Preparation
Data Exploration/Understanding
Simple exploratory Analysis in order to understand the whole set of information
available, identify problems in the data, and start observing relationships
among variables.
Use of data visualization techniques for exploring the set of information
Data is prepared for data mining and machine learning models
Imputation of missing values, computation of new variables potentially useful for
the business question, transformation of variables to make them meaningful for
the problem to be solved
Models are implemented
Available data is used and synthesized to answer the business question, by
identifying relationships among target variable and input variables
It may be a recursive process based also on sampling data and assessing models
and results
Model results are interpreted in order to be useful for business strategy and
actions.
39. OBJECTIVES
ANALYTICAL
MODEL
… that can be answered through specific statistical models and
approaches
CustomerValue
Customer Life Time
New Customer
identification
and engagement
Clienteling
& Caring
Program
Actions to retain
leaving customers
Churn Model
ENGAGE NEW
CUSTOMERS
NURTURE & DEVELOP
LOYALTY CUSTOMERS
RETAIN LEAVING
CUSTOMERS
+
Clickstream & Content Analysis
Next Best Offer Analysis
Segmentation (deterministical vs behavioural)
Propensity Model
40. Why Algorithms Analysis
Propensity Models
The model assigns a propensity score to each customer and allows to priorite initiatives
Propensity model allows to estimate
Re-purchasing probability of customers
Retargeting Optimization: predict the likehood of booking a flight for potential customers
Up-selling propensity: Reservation upgrade or ancillary services proposal
Etc.
Address marketing
investments on customer
with highest propensity
to:
– Increase up-selling
– Increase cross-selling
– Increase active
customers
– Increase redemption
of marketing
campaigns
Regressions
Decision Trees
Random Forests
Neural Networks
Support Vector Machines
Ensemble Models
…
What
+
41. Why Algorithms Analysis
Behavioural Segmentation
Behavioral segmentation follows a statistical clustering algorithm which:
Identify most significant variables for the analysis
Aggregate customers into mutually exclusive groups with similar behavioral patterns, by creating
clusters are as similar as possible
Customer affiliation to a specific cluster varies overtime, based on his behavior
Get strategic insight on
customer base to increase
loyalty and value
Tailor contact strategy
(“the right action for the
right customer”)
Enhance the website
experience
Increase the redemption
rate for targeting
marketing campaigns
Data transformation
Factor analysis
Unsupervised Clustering
models
What
+
42. Why Algorithms Analysis
Churn Models
Churn analysis is a multivariate data mining technique that assigns a score to customer attrition
It estimates the probability that a customer will not buy from a company anymore or for a given period of
time
Historical data on customers leaving the company will be investigated in order to identify anticipatory
signals. Information on flying behavior, enriched data (lifestyle, interests, motivation, SOW, price sensitivity)
and customer hyper-profile will be used to compare churn vs loyal behavior
Optimization of costs and
marketing activities in
customer retention
Identification of high risk
customers sorted by
profitability
Increase active customers
Regressions
Decision Trees
Random Forests
Neural Networks
Support Vector Machines
Ensemble Models
…
What
43. Big Data, Analytics, AI, Machine Learning, Deep Learning
Executive Overview
Big Data & Analytics Reference Architectures Conceptual View
Big Data & Analytics Reference Architectures Logical View
Big Data & Analytics Reference Architectures Technological View
Analytics Overview and Case Studies
Event Store
Domain Model
Cloudera
Agenda
49. Big Data, Analytics, AI, Machine Learning, Deep Learning
Executive Overview
Big Data & Analytics Reference Architectures Conceptual View
Big Data & Analytics Reference Architectures Logical View
Big Data & Analytics Reference Architectures Technological View
Analytics Overview and Case Studies
Event Store
Domain Model
Cloudera
Agenda
50. Place the project's primary focus on the core domain and domain logic
Base complex designs on a model of the domain
Initiate a creative collaboration between technical and domain experts to iteratively refine a
conceptual model that addresses particular domain problems.
Concepts
– Context: the setting in which a word or statement appears that determines its meaning
– Domain: a sphere of knowledge (ontology), influence, or activity. The subject area to which
the user applies a program is the domain of the software
– Model: a system of abstractions that describes selected aspects of a domain and can be used
to solve problems related to that domain
– Ubiquitous Language: a language structured around the domain model and used by all team
members to connect all the activities of the team with the software
– Bounded context: explicitly define the context within which a model applies. Explicitly set
boundaries in terms of team organization, usage within specific parts of the application
– Context map: Identify each model in play on the project and define its bounded context. This
includes the implicit models of non-object-oriented subsystems. Name each bounded
context, and make the names part of the ubiquitous language. Describe the points of contact
between the models, outlining explicit translation for any communication
Domain Driven Design: Concepts
51
51. Entity: An object that is not defined by its attributes, but rather by a thread of continuity and its
identity
Value Object: an object that contains attributes but has no conceptual identity. They should be
treated as immutable
Aggregate: a collection of objects that are bound together by a root entity, otherwise known as
an aggregate root. The aggregate root guarantees the consistency of changes being made within
the aggregate by forbidding external objects from holding references to its members
Domain Event: a domain object that defines an event (something that happens). A domain
event is an event that domain experts care about
Service: when an operation does not conceptually belong to any object. Following the natural
contours of the problem, you can implement these operations in services
Domain Driven Design: Building Blocks
52
54. Big Data, Analytics, AI, Machine Learning, Deep Learning
Executive Overview
Big Data & Analytics Reference Architectures Conceptual View
Big Data & Analytics Reference Architectures Logical View
Big Data & Analytics Reference Architectures Technological View
Analytics Overview and Case Studies
Event Store
Domain Model
Cloudera
Agenda
56. Cloudera Manager
57
Cloudera Manager is an end-to-end application for managing CDH clusters. Cloudera Manager sets the
standard for enterprise deployment by delivering granular visibility into and control over every part of
the CDH cluster—empowering operators to improve performance, enhance quality of service,
increase compliance and reduce administrative costs.
58. Cloudera Navigator Optimizer
59
How can you assess the risk and true cost of offloading ETL and analytic workloads and understand
what it takes to get there?
o Cloudera Navigator Optimizer gives you the insights and risk-assessments you need to build out
a comprehensive strategy for Hadoop success. Simply upload your existing SQL workloads to
get started, and Navigator Optimizer will identify relative risks and development costs for
offloading these to Hadoop based on compatibility and complexity.
o To efficiently optimize performance for the latest technologies, like Hive and Impala, you need
visibility into what users are doing with the data and when the queries themselves are to
blame. Cloudera Navigator Optimizer gives you that visibility and lets you focus optimization
efforts on critical areas and best practices.
64. Cloudera Product Mapping View
65
Cloudera Enterprise is available on a subscription basis in five editions, each designed for your
specific needs.
– Essentials provides superior support and advanced management for core Apache Hadoop
– Data Science and Engineering for programmatic preparation and predictive modeling
– Operational DB for online applications and real-time serving
– Analytic DB for BI and SQL analytics
– The Enterprise Data Hub gives you everything you need to become information-driven, with
complete use of the platform.