This document discusses building products with data at the core. It provides an overview of the data landscape, including the growth of data volumes and the big data market. The data landscape map shows the evolution of data tools from 1980s spreadsheets to today's AI-informed decisions. Categories of the data landscape include databases, data warehouses, ETL, data prep, data virtualization, business intelligence, visualization, master data management, governance, and data science tools. Examples are given for popular tools in each category.
Data is like the new currency, and myriad technologies revolves around the idea of putting Big Data into work, while enhancing the levels of ROI. Gear up to witness a humongous growth of data in the 2017, both in respect to variety and volume.
DataDevOps: A Manifesto for a DevOps-like Culture Shift in Data & AnalyticsDr. Arif Wider
A talk given by Dr. Arif Wider (ThoughtWorks) and Sebastian Herold (Zalando) at OOP 2018 in Munich.
Abstract:
More and more companies migrate their monolithic applications to a microservices architecture. However, maintaining a consistent and usable data landscape has only become more challenging by this: huge amounts of structured and unstructured data, and hundreds of data sources.
Furthermore, data-driven product development multiplies the analytics requirements: every product team needs constantly updated and specially tailored metrics which often combine product specific data with company wide data.
Having a centralized data team does not scale in this setting as it becomes the bottleneck between data producers and data consumers.
We created a Manifesto of seven principles which break with traditional separation of roles and show a path how to deal with distributed data in a federal and scalable fashion. This leads to DataDev: a culture shift similar to DevOps in which application developers own their data and take over responsibilities for data & analytics.
Learn about our experiences and best practices with facilitating this cultural transformation at Scout24, the provider of Europe’s largest online markets for cars and real estate.
DataDevOps - A Manifesto on Shared Data Responsibility in Times of MicroservicesDr. Arif Wider
A talk by Sebastian Herold (Scout24) and Arif Wider (ThoughtWorks)
Abstract:
More and more companies successfully migrate their monolithic applications to a Microservices architecture. However, maintaining a consistent and usable data landscape has only become more challenging by this: unstructured data, huge amounts of data, and hundreds of data sources. Having a centralized data team does not scale in this setting as it becomes the bottleneck between application developers and business analysts.
We created a Data Manifesto of seven principles which break with traditional role separations and show a path how to deal with distributed data in a federal and scalable fashion. This leads to DataDevOps: a culture where application developers also own their data. Learn about the experiences we made with facilitating this cultural transformation at Scout24, the provider of Europe’s largest online markets for cars and real estate.
DAMA Webinar: Turn Grand Designs into a Reality with Data VirtualizationDenodo
Watch full webinar here: https://buff.ly/2HMdbUp
What started to evolve as the most agile and real-time enterprise data fabric, data virtualization is proving to go beyond its initial promise and is becoming one of the most important enterprise big data fabrics.
Attend this session to learn:
• What data virtualization really is,
• How it differs from other enterprise data integration technologies
• Real-world examples of data virtualization in action from companies such as Logitech, Autodesk and Festo.
Data is like the new currency, and myriad technologies revolves around the idea of putting Big Data into work, while enhancing the levels of ROI. Gear up to witness a humongous growth of data in the 2017, both in respect to variety and volume.
DataDevOps: A Manifesto for a DevOps-like Culture Shift in Data & AnalyticsDr. Arif Wider
A talk given by Dr. Arif Wider (ThoughtWorks) and Sebastian Herold (Zalando) at OOP 2018 in Munich.
Abstract:
More and more companies migrate their monolithic applications to a microservices architecture. However, maintaining a consistent and usable data landscape has only become more challenging by this: huge amounts of structured and unstructured data, and hundreds of data sources.
Furthermore, data-driven product development multiplies the analytics requirements: every product team needs constantly updated and specially tailored metrics which often combine product specific data with company wide data.
Having a centralized data team does not scale in this setting as it becomes the bottleneck between data producers and data consumers.
We created a Manifesto of seven principles which break with traditional separation of roles and show a path how to deal with distributed data in a federal and scalable fashion. This leads to DataDev: a culture shift similar to DevOps in which application developers own their data and take over responsibilities for data & analytics.
Learn about our experiences and best practices with facilitating this cultural transformation at Scout24, the provider of Europe’s largest online markets for cars and real estate.
DataDevOps - A Manifesto on Shared Data Responsibility in Times of MicroservicesDr. Arif Wider
A talk by Sebastian Herold (Scout24) and Arif Wider (ThoughtWorks)
Abstract:
More and more companies successfully migrate their monolithic applications to a Microservices architecture. However, maintaining a consistent and usable data landscape has only become more challenging by this: unstructured data, huge amounts of data, and hundreds of data sources. Having a centralized data team does not scale in this setting as it becomes the bottleneck between application developers and business analysts.
We created a Data Manifesto of seven principles which break with traditional role separations and show a path how to deal with distributed data in a federal and scalable fashion. This leads to DataDevOps: a culture where application developers also own their data. Learn about the experiences we made with facilitating this cultural transformation at Scout24, the provider of Europe’s largest online markets for cars and real estate.
DAMA Webinar: Turn Grand Designs into a Reality with Data VirtualizationDenodo
Watch full webinar here: https://buff.ly/2HMdbUp
What started to evolve as the most agile and real-time enterprise data fabric, data virtualization is proving to go beyond its initial promise and is becoming one of the most important enterprise big data fabrics.
Attend this session to learn:
• What data virtualization really is,
• How it differs from other enterprise data integration technologies
• Real-world examples of data virtualization in action from companies such as Logitech, Autodesk and Festo.
Big data is primarily associated with AI and new technology. It is as much a revolution in cooperation patterns, however. Big data entails the democratisation of data within an organisation, enabling agile, data-driven innovation in a manner that was previously unavailable. Knowing this, how can you work as an organisation to harvest the fruits and what can go wrong?
What makes it worth becoming a Data Engineer?Hadi Fadlallah
This presentation explains what data engineering is for non-computer science students and why it is worth being a data engineer. I used this presentation while working as an on-demand instructor at Nooreed.com
IDC Portugal | Como Libertar os Seus Dados com Virtualização de DadosDenodo
Watch full webinar here: https://bit.ly/3w1LoDi
Os dados se tornaram o ativo mais crítico para qualquer empresa ter sucesso nesta era de transformação digital.
Nesta sessão, Paul Moxon da Denodo irá explicar como funciona a virtualização de dados e como pode ajudar as organizações a responder melhor às necessidades de negócios, integrando dados de várias fontes de dados, também minimizando custos e tempo, e aumentando a quantidade de dados disponibilizados em geral.
Para melhor compreensão, Mariana Pinto da Passio Consulting apresentará uma demonstração ao vivo da Plataforma Denodo.
Why Data Virtualization Matters in Your PortfolioDenodo
Watch full webinar here: [https://buff.ly/2W925vO]
Enterprise data virtualization has become critical to every organization in overcoming growing data challenges. In this webinar, Forrester analyst Noel Yuhanna, author of The Enterprise Data Virtualization Wave, will address:
Data virtualization market growth trends and momentum
Key solutions and use cases
How leaders like Denodo are differentiating from other vendors in the market
During this Big Data Warehousing Meetup, we discussed how graph databases work, shared some real world use cases, and showed a live demo of the world’s leading graph database, Neo4J. Pitney Bowes demonstrated their new MDM product developed on a graph database.
For more information, check out the other slides from this meetup or visit our website at www.casertaconcepts.com
Self -Service Data preparation for Data-Driven marketingJean-Michel Franco
Marketing needs data to operate in the digital era. But there's a data gap. Self Service data preparation is tackling the painful last mile of data, the one that makes everyone ready to put data at work for its own needs. See how that works.
OpenSymmetry - Business Intelligence MaturityOpenSymmetry
OpenSymmetry Breakout Session during the Callidus Customer Connections Conference in Las Vegas - May 2013. Presenter: Trevor Dunham, Director of Business Intelligence with OpenSymmetry
This presentation explains what data engineering is and describes the data lifecycles phases briefly. I used this presentation during my work as an on-demand instructor at Nooreed.com
State of the State: What’s Happening in the Database Market?Neo4j
Speaker: Lance Walter, CMO, Neo4j
Abstract: The data management landscape continues to evolve rapidly. More and more organizations are waking up to the value of connections and relationships in data, and that’s why Gartner recently named Graph databases one of their Top 10 Technology Trends for 2019.
This session will provide an overview of graph technology and talk about the past, present, and future of graphs and data management. Multiple use cases and customer examples will be covered, including examples of where graph databases can assist and accelerate machine learning and AI projects.
ADV Slides: How to Improve Your Analytic Data Architecture MaturityDATAVERSITY
Many organizations are immature when it comes to data use. The answer lies in delivering a greater level of insight from data, straight to the point of need. Enter: machine learning.
In this webinar, William will look at categories of organizational response to the challenge across strategy, architecture, modeling, processes, and ethics. Machine learning maturity levels tend to move in harmony across these categories. As a general principle of maturity models, you can’t skip levels in any category, nor can you advance in one category well beyond the others.
Vis-à-vis ML, attaining and retaining momentum up the model is paramount for success. You will ascend the model through concerted efforts delivering business wins utilizing progressive elements of the model, and thereby increasing your machine learning maturity. The model will evolve. No plateaus are comfortable for long.
With ML maturity markers, sequencing, and tactics, this webinar provides a plan for how to build analytic Data Architecture maturity in your organization.
To disrupt and innovate, you need access to data. All of your data. The challenge for many organisations is that the data they need is locked away in a variety of silos. And there's perhaps no bigger silo than one of the most a widely deployed business application: SAP. Bringing together all your data for analytics and machine learning unlocks new insights and business value. Together, Cloudera and Datavard hold the key to breaking SAP data out of its silo, providing access to unlimited and untapped opportunities that currently lay hidden.
Big data is primarily associated with AI and new technology. It is as much a revolution in cooperation patterns, however. Big data entails the democratisation of data within an organisation, enabling agile, data-driven innovation in a manner that was previously unavailable. Knowing this, how can you work as an organisation to harvest the fruits and what can go wrong?
What makes it worth becoming a Data Engineer?Hadi Fadlallah
This presentation explains what data engineering is for non-computer science students and why it is worth being a data engineer. I used this presentation while working as an on-demand instructor at Nooreed.com
IDC Portugal | Como Libertar os Seus Dados com Virtualização de DadosDenodo
Watch full webinar here: https://bit.ly/3w1LoDi
Os dados se tornaram o ativo mais crítico para qualquer empresa ter sucesso nesta era de transformação digital.
Nesta sessão, Paul Moxon da Denodo irá explicar como funciona a virtualização de dados e como pode ajudar as organizações a responder melhor às necessidades de negócios, integrando dados de várias fontes de dados, também minimizando custos e tempo, e aumentando a quantidade de dados disponibilizados em geral.
Para melhor compreensão, Mariana Pinto da Passio Consulting apresentará uma demonstração ao vivo da Plataforma Denodo.
Why Data Virtualization Matters in Your PortfolioDenodo
Watch full webinar here: [https://buff.ly/2W925vO]
Enterprise data virtualization has become critical to every organization in overcoming growing data challenges. In this webinar, Forrester analyst Noel Yuhanna, author of The Enterprise Data Virtualization Wave, will address:
Data virtualization market growth trends and momentum
Key solutions and use cases
How leaders like Denodo are differentiating from other vendors in the market
During this Big Data Warehousing Meetup, we discussed how graph databases work, shared some real world use cases, and showed a live demo of the world’s leading graph database, Neo4J. Pitney Bowes demonstrated their new MDM product developed on a graph database.
For more information, check out the other slides from this meetup or visit our website at www.casertaconcepts.com
Self -Service Data preparation for Data-Driven marketingJean-Michel Franco
Marketing needs data to operate in the digital era. But there's a data gap. Self Service data preparation is tackling the painful last mile of data, the one that makes everyone ready to put data at work for its own needs. See how that works.
OpenSymmetry - Business Intelligence MaturityOpenSymmetry
OpenSymmetry Breakout Session during the Callidus Customer Connections Conference in Las Vegas - May 2013. Presenter: Trevor Dunham, Director of Business Intelligence with OpenSymmetry
This presentation explains what data engineering is and describes the data lifecycles phases briefly. I used this presentation during my work as an on-demand instructor at Nooreed.com
State of the State: What’s Happening in the Database Market?Neo4j
Speaker: Lance Walter, CMO, Neo4j
Abstract: The data management landscape continues to evolve rapidly. More and more organizations are waking up to the value of connections and relationships in data, and that’s why Gartner recently named Graph databases one of their Top 10 Technology Trends for 2019.
This session will provide an overview of graph technology and talk about the past, present, and future of graphs and data management. Multiple use cases and customer examples will be covered, including examples of where graph databases can assist and accelerate machine learning and AI projects.
ADV Slides: How to Improve Your Analytic Data Architecture MaturityDATAVERSITY
Many organizations are immature when it comes to data use. The answer lies in delivering a greater level of insight from data, straight to the point of need. Enter: machine learning.
In this webinar, William will look at categories of organizational response to the challenge across strategy, architecture, modeling, processes, and ethics. Machine learning maturity levels tend to move in harmony across these categories. As a general principle of maturity models, you can’t skip levels in any category, nor can you advance in one category well beyond the others.
Vis-à-vis ML, attaining and retaining momentum up the model is paramount for success. You will ascend the model through concerted efforts delivering business wins utilizing progressive elements of the model, and thereby increasing your machine learning maturity. The model will evolve. No plateaus are comfortable for long.
With ML maturity markers, sequencing, and tactics, this webinar provides a plan for how to build analytic Data Architecture maturity in your organization.
To disrupt and innovate, you need access to data. All of your data. The challenge for many organisations is that the data they need is locked away in a variety of silos. And there's perhaps no bigger silo than one of the most a widely deployed business application: SAP. Bringing together all your data for analytics and machine learning unlocks new insights and business value. Together, Cloudera and Datavard hold the key to breaking SAP data out of its silo, providing access to unlimited and untapped opportunities that currently lay hidden.
Watch here: https://bit.ly/3i2iJbu
You will often hear that "data is the new gold". In this context, data management is one of the areas that has received more attention by the software community in recent years. From Artificial Intelligence and Machine Learning to new ways to store and process data, the landscape for data management is in constant evolution. From the privileged perspective of an enterprise middleware platform, we at Denodo have the advantage of seeing many of these changes happen.
Join us for an exciting session that will cover:
- The most interesting trends in data management.
- Our predictions on how those trends will change the data management world.
- How these trends are shaping the future of data virtualization and our own software.
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.
Big Data LDN 2018: DATA MANAGEMENT AUTOMATION AND THE INFORMATION SUPPLY CHAI...Matt Stubbs
Date: 14th November 2018
Location: Governance and MDM Theatre
Time: 10:30 - 11:00
Speaker: Mike Ferguson
Organisation: IBS
About: For most organisations today, data complexity has increased rapidly. In the area of operations, we now have cloud and on-premises OLTP systems with customers, partners and suppliers accessing these applications via APIs and mobile apps. In the area of analytics, we now have data warehouse, data marts, big data Hadoop systems, NoSQL databases, streaming data platforms, cloud storage, cloud data warehouses, and IoT-generated data being created at the edge. Also, the number of data sources is exploding as companies ingest more and more external data such as weather and open government data. Silos have also appeared everywhere as business users are buying in self-service data preparation tools without consideration for how these tools integrate with what IT is using to integrate data. Yet new regulations are demanding that we do a better job of governing data, and business executives are demanding more agility to remain competitive in a digital economy. So how can companies remain agile, reduce cost and reduce the time-to-value when data complexity is on the up?
In this session, Mike will discuss how companies can create an information supply chain to manufacture business-ready data and analytics to reduce time to value and improve agility while also getting data under control.
Die Big Data Fabric als Enabler für Machine Learning & AIDenodo
Ansehen: https://bit.ly/2Cet17K
Erstklassige Big Data Fabrics liefern verlässliche Insights, gewährleisten höchste End-to-End Sicherheitsstandards und ermöglichen eine konsistente Datenintegration in Echtzeit – während den Business-Anwendern agile Werkzeuge zum selbstgesteuerten Datenkonsum bereitgestellt werden.
Erfahren Sie in dem Vortrag, wie die Big Data Fabric als Enabler für ML & AI:
- den Business-Anwendern und Data Scientists einen schnellen und agilen Datenzugriff via Self-Services ermöglicht
- Data Governance und Security Richtlinien zentral und verlässlich managebar macht
- relevante Insights aus aktuellen und konsistenten Daten liefert
Watch full webinar here: https://bit.ly/3mdj9i7
You will often hear that "data is the new gold"? In this context, data management is one of the areas that has received more attention from the software community in recent years. From Artificial Intelligence and Machine Learning to new ways to store and process data, the landscape for data management is in constant evolution. From the privileged perspective of an enterprise middleware platform, we at Denodo have the advantage of seeing many of these changes happen.
In this webinar, we will discuss the technology trends that will drive the enterprise data strategies in the years to come. Don't miss it if you want to keep yourself informed about how to convert your data to strategic assets in order to complete the data-driven transformation in your company.
Watch this on-demand webinar as we cover:
- The most interesting trends in data management
- How to build a data fabric architecture?
- How to manage your data integration strategy in the new hybrid world
- Our predictions on how those trends will change the data management world
- How can companies monetize the data through data-as-a-service infrastructure?
- What is the role of voice computing in future data analytic
[DSC Adria 23] Thomas Miebach A modern, business focused data strategy with C...DataScienceConferenc1
In this session we’ll highlight some key challenges with the “old” data world, and how to overcome them with a modern data strategy focused on the business outcomes. We’ll illustrate how Collibra fits into this strategy and how Collibra supports it.
A Key to Real-time Insights in a Post-COVID World (ASEAN)Denodo
Watch full webinar here: https://bit.ly/2EpHGyd
Presented at Data Champions, Online Asia 2020
Businesses and individuals around the world are experiencing the impact of a global pandemic. With many workers and potential shoppers still sequestered, COVID-19 is proving to have a momentous impact on the global economy. Regardless of the current situation and post-pandemic era, real-time data becomes even more critical to healthcare practitioners, business owners, government officials, and the public at large where holistic and timely information are important to make quick decisions. It enables doctors to make quick decisions about where to focus the care, business owners to alter production schedules to meet the demand, government agencies to contain the epidemic, and the public to be informed about prevention.
In this on-demand session, you will learn about the capabilities of data virtualization as a modern data integration technique and how can organisations:
- Rapidly unify information from disparate data sources to make accurate decisions and analyse data in real-time
- Build a single engine for security that provides audit and control by geographies
- Accelerate delivery of insights from your advanced analytics project
Self Service Analytics and a Modern Data Architecture with Data Virtualizatio...Denodo
Watch full webinar here: https://bit.ly/32TT2Uu
Data virtualization is not just for self-service, it’s also a first-class citizen when it comes to modern data platform architectures. Technology has forced many businesses to rethink their delivery models. Startups emerged, leveraging the internet and mobile technology to better meet customer needs (like Amazon and Lyft), disrupting entire categories of business, and grew to dominate their categories.
Schedule a complimentary Data Virtualization Discovery Session with g2o.
Traditional companies are still struggling to meet rising customer expectations. During this webinar with the experts from g2o and Denodo we covered the following:
- How modern data platforms enable businesses to address these new customer expectation
- How you can drive value from your investment in a data platform now
- How you can use data virtualization to enable multi-cloud strategies
Leveraging the strategy insights of g2o and the power of the Denodo platform, companies do not need to undergo the costly removal and replacement of legacy systems to modernize their systems. g2o and Denodo can provide a strategy to create a modern data architecture within a company’s existing infrastructure.
DAS Slides: Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key inter-relationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how Data Architecture is a key component of an overall enterprise architecture for enhanced business value and success.
How Data Virtualization Puts Enterprise Machine Learning Programs into Produc...Denodo
Watch full webinar here: https://bit.ly/3offv7G
Presented at AI Live APAC
Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python and Scala put advanced techniques at the fingertips of the data scientists. However, these data scientists spend most of their time looking for the right data and massaging it into a usable format. Data virtualization offers a new alternative to address these issues in a more efficient and agile way.
Watch this on-demand session to learn how companies can use data virtualization to:
- Create a logical architecture to make all enterprise data available for advanced analytics exercise
- Accelerate data acquisition and massaging, providing the data scientist with a powerful tool to complement their practice
- Integrate popular tools from the data science ecosystem: Spark, Python, Zeppelin, Jupyter, etc.
Building a Data Strategy – Practical Steps for Aligning with Business GoalsDATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace – from digital transformation, to marketing, to customer centricity, to population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.
Building the Artificially Intelligent EnterpriseDatabricks
This session looks at where we are today with data and analytics and what is needed to transition to the Artificially Intelligent Enterprise.
How do you mobilise developers to exploit what data scientists and business analysts have built? How do you align it all with business strategy to maximise business outcomes? How do you combine BI, predictive and prescriptive analytics, automation and reinforcement learning to get maximum value across the enterprise? What is the blueprint for building the artificially intelligent enterprise?
•Data and analytics – Where are we?
•Why is the journey only half-way done?
•2021 and beyond – The new era of AI usage and not just build
•The requirement – event-driven, on-demand and automated analytics
•Operationalising what you build – DataOps, MLOps and RPA
•Mobilising the masses to integrate AI into processes – what needs to be done?
•Business strategy alignment – the guiding light to AI utilisation for high reward
•Agility step change – the shift to no-code integration of AI by citizen developers
•Recording decisions, and analysing business impact
•Reinforcement-learning – transitioning to continuous reward
¿Cómo las manufacturas están evolucionando hacia la Industria 4.0 con la virt...Denodo
Watch full webinar here: https://bit.ly/3cbpipB
Uno de los sectores en los que la transformación digital está teniendo un efecto más disruptivo es el de la fabricación. Líderes del sector manufacturero están apostando por el Big Data, la computación en la nube, la inteligencia artificial y el Internet de las Cosas (IoT) entre otras tecnologías, además de contemplar la llegada de la 5G, con el fin de:
- Automatizar los procesos de manera eficiente, para permitir una mayor producción en menor tiempo
- Crear valor añadido en los productos manufacturados
- Conectar la planta industrial con el punto de venta
- Impulsar el análisis en tiempo real de datos provenientes de diferentes cadenas de producción
Sin embargo, para alcanzar estos objetivos y llevar a cabo esta revolución tecnológica, también conocida como industria 4.0, las manufacturas tienen que enfrentarse a una serie de desafíos no negligentes. El sector industrial es el que genera más datos en el mundo, y en la era digital, la velocidad, la diversidad y el volumen exponencial de los datos pueden superar las arquitecturas de TI tradicionales. Además, la mayoría de los fabricantes se enfrentan a silos de datos, lo que hace que su tratamiento sea lento y costoso. Necesitan entonces una plataforma de TI fiable que permita integrar, centralizar y analizar datos de distintas fuentes y diferentes formatos de manera ágil y segura para poner la información al servicio del negocio.
Los expertos de Enki y Denodo te proponen este seminario online para descubrir qué es la virtualización de datos, y por qué líderes del sector apuestan por esta tecnología innovadora para optimizar su estrategia de TI y conseguir un ROI significativo gracias a un acceso más rápido, simple y unificado a los datos industriales.
DAS Slides: Cloud-Based Data Warehousing – What’s New and What Stays the SameDATAVERSITY
Data warehousing, after decades of widespread adoption, still holds a strong place in today’s organization. Cloud-based technologies have revolutionized the traditional world of data warehousing, offering transformational ways to support analytics and reporting. Join this webinar to understand what has changed in the world of data warehousing with the introduction of cloud-based technologies, and what has remained the same.
Trends in Enterprise Advanced AnalyticsDATAVERSITY
If you missed out on all the trends for 2019 published in
December, or even if you caught some of them, this one merits your time. We’ll be going into 2019 and beyond, since the winners will have an eye on the long view for the source of competitive advantage that is analytics.
It is a fascinating, explosive time for enterprise
analytics.
It is from the position of analytics leadership that the
mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data and projects that will deliver analytics.
After all, no matter what business you’re in, you’re in the business of analytics.
The coming years will be full of big changes in enterprise data architecture. William will kick off the Advanced Analytics 2019 series with a discussion of the trends winning organizations should build into their plans, expectations, vision and awareness now.
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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.
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
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
3. 3 | Proprietary & Confidential
The volume of data is exploding
100K hours of videos streamed
750Ksongs played
Per minute in 2018!
50Kposts on Instagram
Source: hDps://www.entrepreneur.com/ar5cle/314672
4. 4
Worldwide Big Data market revenues for so<ware and
services projec?on
$42B2018
$103B2027
~11%CAGR
Source: Wikibon
hDps://www.forbes.com/sites/louiscolumbus/
2018/05/23/10-charts-that-will-change-your-
5. 5
Data is like carbon emissions.
It’s dirty and hard to clean.
6. 6
1980’s
HIPPO in a
Spreadsheet
1990’s
Recommendation
Engines on an
Enterprise Data
Warehouse
2000’s
Data Science
in a Data Lake
Today
AI-informed
Decisions
Emergence of New Data Tools Manage Data
The trend has been moving towards automation and AI
7. Tools From Data Collec?on to Consump?on
Product Manager
Graphic Source:
hDps://edw2019.dataversity.net/uploads/handouts/
TUE_1345_O'Sullivan-Raje_COLOR_10247.pdf
9. Data Landscape - Databases
Category Description Examples
Databases
Data lives in a databases.
Different types: analytical,
transactional, hybrid etc.
● Oracle
● MySQL
● SQL Server
● PostgreSQL
10. Data Landscape - Enterprise Data Warehouse
Category Description Examples
Enterprise Data
Warehouse
All of the data that an
organization stores for its
users so they can perform
their job functions
● Oracle
● Teradata
● Hive
● Data lakes
11. Data Landscape - ETL
Category Description Examples
ETL
Extract - takes data from
external sources
Transform - change data
Load - Move it to a data
warehouse
● Informatica PowerCenter
● IBM InfoSphere
● Talend
● Alooma
● Matillion
12. Data Landscape - Data Prep
Category Description Examples
Data Prep
3 subcategories:
- Data Masking
- MDM
- Data Wrangling
● Alteryx
● Paxata
● Trifacta
13. Data Landscape - Data Virtualization
Category Description Examples
Data
Virtualization
Simplifies access to an
organization's data for the
applications and users that
consume the data
● VMWare
● Denodo
● Delphix
14. Data Landscape - BI
Category Description Examples
Business
Intelligence (BI)
Process to analyze their data
and create ac5onable
takeaways that impact the
company’s performance
● Tableau
● Looker
● Microstrategy
● Power BI
15. Data Landscape - Visualization
Category Description Examples
Visualization
Effort to help people
understand the significance of
data by placing it in a visual
context.
● Qlik
● Microsoft Power BI
● SFDC Einstein
16. Data Landscape - MDM
Category Description Examples
Master Data
Management
Tracks the most essential
company-wide data points,
and provides insights
related to company
operations, clients, and
goals.
● IBM InfoSphere
● SAP NetWeaver
● Informatica MDM
● Oracle MDM
17. Data Landscape - Governance
Category Description Examples
Governance
Helps an organization build
and deploy their governance
policies as well as to track
compliance
● Collibra
● Informatica
● SAP Master Data
Governance
18. Data Landscape - Data Science Tools
Category Description Examples
Data Science
Tools
Allow Data Scientists to run
complex analysis and
algorithms using advanced
languages such as R and
Python
● Dataiku
● Domino Data Lab
● AWS Lambda
● Jupyter
