Watch the full session: Denodo DataFest 2016 sessions: https://goo.gl/Bvmvc9
Data prep and data blending are terms that have come to prominence over the last year or two. On the surface, they appear to offer functionality similar to data virtualization…but there are important differences!
In this session, you will learn:
• How data virtualization complements or contrasts technologies such as data prep and data blending
• Pros and cons of functionality provided by data prep, data catalog and data blending tools
• When and how to use these different technologies to be most effective
This session is part of the Denodo DataFest 2016 event. You can also watch more Denodo DataFest sessions on demand here: https://goo.gl/VXb6M6
Data Integration Alternatives: When to use Data Virtualization, ETL, and ESBDenodo
Data integration is paramount, in this presentation you will find three different paradigms: using client-side tools, creating traditional data warehouses and the data virtualization solution - the logical data warehouse, comparing each other and positioning data virtualization as an integral part of any future-proof IT infrastructure.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/1q94Ka.
Performance Acceleration: Summaries, Recommendation, MPP and moreDenodo
Watch full webinar here: https://bit.ly/3nLHayP
Performance is critical for an organization across the board. Developers can optimize execution with Summaries, MPP, Data Movement, and more. Business users rely on the Recommendation engine to guide them to the right data. Let’s discover and learn about various performance acceleration techniques in this session.
Modern Data Management for Federal ModernizationDenodo
Watch full webinar here: https://bit.ly/2QaVfE7
Faster, more agile data management is at the heart of government modernization. However, Traditional data delivery systems are limited in realizing a modernized and future-proof data architecture.
This webinar will address how data virtualization can modernize existing systems and enable new data strategies. Join this session to learn how government agencies can use data virtualization to:
- Enable governed, inter-agency data sharing
- Simplify data acquisition, search and tagging
- Streamline data delivery for transition to cloud, data science initiatives, and more
Unlock Your Data for ML & AI using Data VirtualizationDenodo
How Denodo Complement’s Logical Data Lake in Cloud
● Denodo does not substitute data warehouses, data lakes,
ETLs...
● Denodo enables the use of all together plus other data
sources
○ In a logical data warehouse
○ In a logical data lake
○ They are very similar, the only difference is in the main
objective
● There are also use cases where Denodo can be used as data
source in a ETL flow
Watch this webinar in full here: https://buff.ly/2MVTKqL
Self-Service BI promises to remove the bottleneck that exists between IT and business users. The truth is, if data is handed over to a wide range of data consumers without proper guardrails in place, it can result in data anarchy.
Attend this session to learn why data virtualization:
• Is a must for implementing the right self-service BI
• Makes self-service BI useful for every business user
• Accelerates any self-service BI initiative
Watch full webinar here: https://bit.ly/2xc6IO0
To solve these challenges, according to Gartner "through 2022, 60% of all organizations will implement data virtualization as one key delivery style in their data integration architecture". It is clear that data virtualization has become a driving force for companies to implement agile, real-time and flexible enterprise data architecture.
In this session we will look at the data integration challenges solved by data virtualization, the main use cases and examine why this technology is growing so fastly. You will learn:
- What data virtualization really is
- How it differs from other enterprise data integration technologies
- Why data virtualization is finding enterprise-wide deployment inside some of the largest organizations
Data Mesh in Practice: How Europe’s Leading Online Platform for Fashion Goes ...Databricks
The Data Lake paradigm is often considered the scalable successor of the more curated Data Warehouse approach when it comes to democratization of data. However, many who went out to build a centralized Data Lake came out with a data swamp of unclear responsibilities, a lack of data ownership, and sub-par data availability.
Building a Logical Data Fabric using Data Virtualization (ASEAN)Denodo
Watch full webinar here: https://bit.ly/3FF1ubd
In the recent Building the Unified Data Warehouse and Data Lake report by leading industry analysts TDWI, we have discovered 64% of organizations stated the objective for a unified Data Warehouse and Data Lakes is to get more business value and 84% of organizations polled felt that a unified approach to Data Warehouses and Data Lakes was either extremely or moderately important.
In this session, you will learn how your organization can apply a logical data fabric and the associated technologies of machine learning, artificial intelligence, and data virtualization can reduce time to value. Hence, increasing the overall business value of your data assets.
KEY TAKEAWAYS:
- How a Logical Data Fabric is the right approach to assist organizations to unify their data.
- The advanced features of a Logical Data Fabric that assist with the democratization of data, providing an agile and governed approach to business analytics and data science.
- How a Logical Data Fabric with Data Virtualization enhances your legacy data integration landscape to simplify data access and encourage self-service.
Data Integration Alternatives: When to use Data Virtualization, ETL, and ESBDenodo
Data integration is paramount, in this presentation you will find three different paradigms: using client-side tools, creating traditional data warehouses and the data virtualization solution - the logical data warehouse, comparing each other and positioning data virtualization as an integral part of any future-proof IT infrastructure.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/1q94Ka.
Performance Acceleration: Summaries, Recommendation, MPP and moreDenodo
Watch full webinar here: https://bit.ly/3nLHayP
Performance is critical for an organization across the board. Developers can optimize execution with Summaries, MPP, Data Movement, and more. Business users rely on the Recommendation engine to guide them to the right data. Let’s discover and learn about various performance acceleration techniques in this session.
Modern Data Management for Federal ModernizationDenodo
Watch full webinar here: https://bit.ly/2QaVfE7
Faster, more agile data management is at the heart of government modernization. However, Traditional data delivery systems are limited in realizing a modernized and future-proof data architecture.
This webinar will address how data virtualization can modernize existing systems and enable new data strategies. Join this session to learn how government agencies can use data virtualization to:
- Enable governed, inter-agency data sharing
- Simplify data acquisition, search and tagging
- Streamline data delivery for transition to cloud, data science initiatives, and more
Unlock Your Data for ML & AI using Data VirtualizationDenodo
How Denodo Complement’s Logical Data Lake in Cloud
● Denodo does not substitute data warehouses, data lakes,
ETLs...
● Denodo enables the use of all together plus other data
sources
○ In a logical data warehouse
○ In a logical data lake
○ They are very similar, the only difference is in the main
objective
● There are also use cases where Denodo can be used as data
source in a ETL flow
Watch this webinar in full here: https://buff.ly/2MVTKqL
Self-Service BI promises to remove the bottleneck that exists between IT and business users. The truth is, if data is handed over to a wide range of data consumers without proper guardrails in place, it can result in data anarchy.
Attend this session to learn why data virtualization:
• Is a must for implementing the right self-service BI
• Makes self-service BI useful for every business user
• Accelerates any self-service BI initiative
Watch full webinar here: https://bit.ly/2xc6IO0
To solve these challenges, according to Gartner "through 2022, 60% of all organizations will implement data virtualization as one key delivery style in their data integration architecture". It is clear that data virtualization has become a driving force for companies to implement agile, real-time and flexible enterprise data architecture.
In this session we will look at the data integration challenges solved by data virtualization, the main use cases and examine why this technology is growing so fastly. You will learn:
- What data virtualization really is
- How it differs from other enterprise data integration technologies
- Why data virtualization is finding enterprise-wide deployment inside some of the largest organizations
Data Mesh in Practice: How Europe’s Leading Online Platform for Fashion Goes ...Databricks
The Data Lake paradigm is often considered the scalable successor of the more curated Data Warehouse approach when it comes to democratization of data. However, many who went out to build a centralized Data Lake came out with a data swamp of unclear responsibilities, a lack of data ownership, and sub-par data availability.
Building a Logical Data Fabric using Data Virtualization (ASEAN)Denodo
Watch full webinar here: https://bit.ly/3FF1ubd
In the recent Building the Unified Data Warehouse and Data Lake report by leading industry analysts TDWI, we have discovered 64% of organizations stated the objective for a unified Data Warehouse and Data Lakes is to get more business value and 84% of organizations polled felt that a unified approach to Data Warehouses and Data Lakes was either extremely or moderately important.
In this session, you will learn how your organization can apply a logical data fabric and the associated technologies of machine learning, artificial intelligence, and data virtualization can reduce time to value. Hence, increasing the overall business value of your data assets.
KEY TAKEAWAYS:
- How a Logical Data Fabric is the right approach to assist organizations to unify their data.
- The advanced features of a Logical Data Fabric that assist with the democratization of data, providing an agile and governed approach to business analytics and data science.
- How a Logical Data Fabric with Data Virtualization enhances your legacy data integration landscape to simplify data access and encourage self-service.
An Introduction to Data Virtualization in 2018Denodo
Watch full webinar on demand here: https://goo.gl/Rdrc1w
"Through 2020, 50% of enterprises will implement some form of data virtualization as one enterprise production option for data integration" according to Gartner. It is clear that data virtualization has become a driving force for companies to implement an agile, real-time and flexible enterprise data architecture.
Attend this session to learn:
• What data virtualization actually means and how it differs from traditional data integration approaches
• The all important use cases and key patterns of data virtualization
• What to expect in the upcoming sessions in the Packed Lunch Webinar Series, which will take a deeper dive into various challenges solved by data virtualization in big data analytics, cloud migration and various other scenarios
Agenda:
• Introduction & benefits of DV
• Summary & next steps
• Q&A
Simplifying Cloud Architectures with Data VirtualizationDenodo
Watch here: https://bit.ly/2yxLo6f
Moving applications and data to the Cloud is a priority for many organizations. The benefits - in terms of flexibility, agility, and cost savings - are driving Cloud adoption. However, the journey to the Cloud is not as easy as many people think. The process of moving application and data to the Cloud is challenging and can entail widespread disruption across the organization if not carefully managed. Even when systems are migrated to the Cloud, the resultant hybrid or multi-Cloud architecture is more complex for users to navigate, making it harder for them to get the data that they need to do their jobs.
Data Virtualization can help organizations at all stages of their journey to the Cloud - during migration and also in the resultant hybrid or multi-Cloud architectures. Attend this webinar to learn how Data Virtualization can:
- Help organizations manage risk and minimize the disruption caused as systems are moved to the Cloud
- Provide a single point of access for data that is both on-premise and in the Cloud, making it easier for users to find and access the data that they need
- Provide a security layer to protect and manage your data when it's distributed across hybrid or multi-Cloud architectures
Designing an Agile Fast Data Architecture for Big Data Ecosystem using Logica...Denodo
Autodesk designed a modern data architecture that heavily uses data virtualization to integrate both legacy data sources and contemporary big data analytics like Spark into a single unified logical data warehouse. In this presentation, you will learn how to build a logical data warehouse using data virtualization and create a single, unified enterprise-wide access and governance point for any data used within the company.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/Ab4PDB.
Big Data Day LA 2015 - Data Lake - Re Birth of Enterprise Data Thinking by Ra...Data Con LA
Why and How has the Big Data based Enterprise Data Lake solution based on No-SQL and SQL technologies has become significantly effective in solving enterprise data challenges than its predecessor EDW which had tried and failed to solve the same problem entirely based on SQL database only.
Data Mesh in Practice - How Europe's Leading Online Platform for Fashion Goes...Dr. Arif Wider
A talk presented by Max Schultze from Zalando and Arif Wider from ThoughtWorks at NDC Oslo 2020.
Abstract:
The Data Lake paradigm is often considered the scalable successor of the more curated Data Warehouse approach when it comes to democratization of data. However, many who went out to build a centralized Data Lake came out with a data swamp of unclear responsibilities, a lack of data ownership, and sub-par data availability.
At Zalando - europe’s biggest online fashion retailer - we realised that accessibility and availability at scale can only be guaranteed when moving more responsibilities to those who pick up the data and have the respective domain knowledge - the data owners - while keeping only data governance and metadata information central. Such a decentralized and domain focused approach has recently been coined a Data Mesh.
The Data Mesh paradigm promotes the concept of Data Products which go beyond sharing of files and towards guarantees of quality and acknowledgement of data ownership.
This talk will take you on a journey of how we went from a centralized Data Lake to embrace a distributed Data Mesh architecture and will outline the ongoing efforts to make creation of data products as simple as applying a template.
Fixing data science & Accelerating Artificial Super Intelligence DevelopmentManojKumarR41
This presentation discusses Challenges, Problems, Issues, Measures, Mistakes, Opportunities, Ideas, Technologies, Research and Visions around Data Science
HashGraph, Data Mesh, Data Trajectories, Citrix HDX and Anonos BigPrivacy
Combination of these 5 and few other ideas will ultimately lead us to the VGB Platform. Will soon come up with other document explaining the vision and how exactly work on the vision to gradually develop this Platform, which fixes Data Science Efforts Globally.
Analyst View of Data Virtualization: Conversations with Boulder Business Inte...Denodo
In this presentation, executives from Denodo preview the new Denodo Platform 6.0 release that delivers Dynamic Query Optimizer, cloud offering on Amazon Web Services, and self-service data discovery and search. Over 30 analysts, led by Claudia Imhoff, provide input on strategic direction and benefits of Denodo 6.0 to the data virtualization and the broader data integration market.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/DR6r3m.
Big Data Fabric for At-Scale Real-Time Analysis by Edwin RobbinsData Con LA
Abstract:- Companies are adopting big data for performing high-velocity real-time analytics on very large volumes of data to enable rapid analysis for business users using self-service and never-before-realized use cases. However, such projects have yielded limited value because these big data systems have become siloed from the rest of the enterprise systems holding critical business operational data. Big Data Fabric is a modern data architecture combining data virtualization, data prep, and lineage capabilities to seamlessly integrate at scale these huge, siloed volumes of structured and unstructured data with other enterprise data assets. This presentation will demonstrate with proven customer case studies in big data and IoT about the value of using big data fabric as a logical data lake for big data analytics.
Creating a Modern Data Architecture for Digital TransformationMongoDB
By managing Data in Motion, Data at Rest, and Data in Use differently, modern Information Management Solutions are enabling a whole range of architecture and design patterns that allow enterprises to fully harness the value in data flowing through their systems. In this session we explored some of the patterns (e.g. operational data lakes, CQRS, microservices and containerisation) that enable CIOs, CDOs and senior architects to tame the data challenge, and start to use data as a cross-enterprise asset.
Enterprise 360 - Graphs at the Center of a Data FabricPrecisely
Data fabric architectures are used to simplify and integrate data management across business functions to accelerate digital transformation. Creating a data fabric is a way to develop a data-centric view of your business which results in an Enterprise 360 perspective based on trusted data.
Industry analysts and vendors are increasingly finding that graph databases are a key enabling technology in support of
Data Fabric architectures that deliver trusted data.
During this on-demand webinar, we discuss how we help our customers implement a Data Fabric pattern using graph database technology in support of their key strategic objectives.
Denodo DataFest 2016: Big Data Virtualization in the CloudDenodo
Watch the full session: Denodo DataFest 2016 sessions: https://goo.gl/kahTgf
Many firms are adopting “cloud first” strategy and are migrating their on-premises technologies to the cloud. Logitech is one of them. They have adopted the AWS platform and big data on the cloud for all of their analytical needs, including Amazon Redshift and S3.
In this presentation, the Principal of Big Data and Analytics team at Logitech, Avinash Deshpande will present:
• The business rationale for migrating to the cloud
• How data virtualization enables the migration
• Running data virtualization itself in the cloud
This session also includes a panel discussion with:
• Avinash Deshpande, Principal – Big Data and Analytics at Logitech
• Kurt Jackson, Platform Lead at Autodesk
• Dan Young, Chief Data Architect at Indiana University
• Paul Moxon, Head of Product Management at Denodo (as moderator)
This session is part of the Denodo DataFest 2016 event. You can also watch more Denodo DataFest sessions on demand here: https://goo.gl/VXb6M6
Organizations have been collecting, storing, and accessing data from the beginning of computerization. Insights gained from analyzing the data enable them to identify new opportunities, improve core processes, enable continuous learning and differentiation, remain competitive, and thrive in an increasingly challenging business environment.
The well-established data architecture, consisting of a data warehouse, fed from multiple operational data stores, and fronted by BI tools, has served most organizations well. However, over the last two decades, with the explosion of internet-scale data, and the advent of new approaches to data and computational processing, this tried-and-true data architecture has come under strain, and has created both challenges and opportunities for organizations.
In this green paper, we will discuss modern approaches to data architecture that have evolved to address these challenges and provide a framework for companies to build a data architecture and better adapt to increasing demands of the modern business environment. This discussion of data architecture will be tied to the Data Maturity Journey introduced in EQengineered’s June 2021 green paper on Data Modernization.
Enabling digital transformation api ecosystems and data virtualizationDenodo
Watch the full webinar here: https://buff.ly/2KBKzLJ
Digital transformation, as cliché as it sounds, is on top of every decision maker’s strategic initiative list. And at the heart of any digital transformation, no matter the industry or the size of the company, there is an application programming interface (API) strategy. While API platforms enable companies to manage large numbers of APIs working in tandem, monitor their usage, and establish security between them, they are not optimized for data integration, so they cannot easily or quickly integrate large volumes of data between different systems. Data virtualization, however, can greatly enhance the capabilities of an API platform, increasing the benefits of an API-based architecture. With data virtualization as part of an API strategy, companies can streamline digital transformations of any size and scope.
Join us for this webinar to see these technologies in action in a demo and to get the answers to the following questions:
*How can data virtualization enhance the deployment and exposure of APIs?
*How does data virtualization work as a service container, as a source for microservices and as an API gateway?
*How can data virtualization create managed data services ecosystems in a thriving API economy?
*How are GetSmarter and others are leveraging data virtualization to facilitate API-based initiatives?
Partner Enablement: Key Differentiators of Denodo Platform 6.0 for the FieldDenodo
If you’re a Denodo Partner, this presentation is for you. Learn how to gain a competitive edge in the marketplace with Denodo Platform 6.0, and leverage off the new features and functionality.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/Qh8MeX.
Domain Driven Data: Apache Kafka® and the Data Meshconfluent
James Gollan, Confluent, Senior Solutions Engineer
From digital banking to industry 4.0 the nature of business is changing. Increasingly businesses are becoming software. And the lifeblood of software is data. Dealing with data at the enterprise level is tough, and their have been some missteps along the way.
This session will consider the increasingly popular idea of a 'data mesh' - the problems it solves and, perhaps most importantly, how an event streaming platform forms the bedrock of this new paradigm.
Recording to be available cnfl.io/meetup-hub
https://www.meetup.com/KafkaMelbourne/events/277076626/
Artificial intelligence and machine learning are currently all the rage. Every organisation is trying to jump on this bandwagon and cash in on their data reserves. At ThoughtWorks, we’d agree that this tech has huge potential — but as with all things, realising value depends on understanding how best to use it.
Augmentation, Collaboration, Governance: Defining the Future of Self-Service BIDenodo
Watch full webinar here: https://bit.ly/3zVJRRf
According to Dresner Advisory’s 2020 Self-Service Business Intelligence Market Study, 62% of the responding organizations say self-service BI is critical for their business. If we look deeper into the need for today’s self-service BI, it’s beyond some Executives and Business Users being enabled by IT for self-service dashboarding or report generation. Predictive analytics, self-service data preparation, collaborative data exploration are all different facets of new generation self-service BI. While democratization of data for self-service BI holds many benefits, strict data governance becomes increasingly important alongside.
In this session we will discuss:
- The latest trends and scopes of self-service BI
- The role of logical data fabric in self-service BI
- How Denodo enables self-service BI for a wide range of users - Customer case study on self-service BI
Hadoop meets Agile! - An Agile Big Data ModelUwe Printz
Big Data projects are a struggle, not only on the technical side but also on the organizational side. In this talk the author shares his experience and opinions from almost 5 years of Big Data projects and develops an Agile Big Data Model which reflects his ideas on how Big Data projects can be successful, even in large companies.
Talk held at the crossover meetup of the "Agile Stammtisch Rhein-Main" and the "Hadoop & Spark User Group Rhein-Main" at codecentric AG on 31.01.2017.
An Introduction to Data Virtualization in 2018Denodo
Watch full webinar on demand here: https://goo.gl/Rdrc1w
"Through 2020, 50% of enterprises will implement some form of data virtualization as one enterprise production option for data integration" according to Gartner. It is clear that data virtualization has become a driving force for companies to implement an agile, real-time and flexible enterprise data architecture.
Attend this session to learn:
• What data virtualization actually means and how it differs from traditional data integration approaches
• The all important use cases and key patterns of data virtualization
• What to expect in the upcoming sessions in the Packed Lunch Webinar Series, which will take a deeper dive into various challenges solved by data virtualization in big data analytics, cloud migration and various other scenarios
Agenda:
• Introduction & benefits of DV
• Summary & next steps
• Q&A
Simplifying Cloud Architectures with Data VirtualizationDenodo
Watch here: https://bit.ly/2yxLo6f
Moving applications and data to the Cloud is a priority for many organizations. The benefits - in terms of flexibility, agility, and cost savings - are driving Cloud adoption. However, the journey to the Cloud is not as easy as many people think. The process of moving application and data to the Cloud is challenging and can entail widespread disruption across the organization if not carefully managed. Even when systems are migrated to the Cloud, the resultant hybrid or multi-Cloud architecture is more complex for users to navigate, making it harder for them to get the data that they need to do their jobs.
Data Virtualization can help organizations at all stages of their journey to the Cloud - during migration and also in the resultant hybrid or multi-Cloud architectures. Attend this webinar to learn how Data Virtualization can:
- Help organizations manage risk and minimize the disruption caused as systems are moved to the Cloud
- Provide a single point of access for data that is both on-premise and in the Cloud, making it easier for users to find and access the data that they need
- Provide a security layer to protect and manage your data when it's distributed across hybrid or multi-Cloud architectures
Designing an Agile Fast Data Architecture for Big Data Ecosystem using Logica...Denodo
Autodesk designed a modern data architecture that heavily uses data virtualization to integrate both legacy data sources and contemporary big data analytics like Spark into a single unified logical data warehouse. In this presentation, you will learn how to build a logical data warehouse using data virtualization and create a single, unified enterprise-wide access and governance point for any data used within the company.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/Ab4PDB.
Big Data Day LA 2015 - Data Lake - Re Birth of Enterprise Data Thinking by Ra...Data Con LA
Why and How has the Big Data based Enterprise Data Lake solution based on No-SQL and SQL technologies has become significantly effective in solving enterprise data challenges than its predecessor EDW which had tried and failed to solve the same problem entirely based on SQL database only.
Data Mesh in Practice - How Europe's Leading Online Platform for Fashion Goes...Dr. Arif Wider
A talk presented by Max Schultze from Zalando and Arif Wider from ThoughtWorks at NDC Oslo 2020.
Abstract:
The Data Lake paradigm is often considered the scalable successor of the more curated Data Warehouse approach when it comes to democratization of data. However, many who went out to build a centralized Data Lake came out with a data swamp of unclear responsibilities, a lack of data ownership, and sub-par data availability.
At Zalando - europe’s biggest online fashion retailer - we realised that accessibility and availability at scale can only be guaranteed when moving more responsibilities to those who pick up the data and have the respective domain knowledge - the data owners - while keeping only data governance and metadata information central. Such a decentralized and domain focused approach has recently been coined a Data Mesh.
The Data Mesh paradigm promotes the concept of Data Products which go beyond sharing of files and towards guarantees of quality and acknowledgement of data ownership.
This talk will take you on a journey of how we went from a centralized Data Lake to embrace a distributed Data Mesh architecture and will outline the ongoing efforts to make creation of data products as simple as applying a template.
Fixing data science & Accelerating Artificial Super Intelligence DevelopmentManojKumarR41
This presentation discusses Challenges, Problems, Issues, Measures, Mistakes, Opportunities, Ideas, Technologies, Research and Visions around Data Science
HashGraph, Data Mesh, Data Trajectories, Citrix HDX and Anonos BigPrivacy
Combination of these 5 and few other ideas will ultimately lead us to the VGB Platform. Will soon come up with other document explaining the vision and how exactly work on the vision to gradually develop this Platform, which fixes Data Science Efforts Globally.
Analyst View of Data Virtualization: Conversations with Boulder Business Inte...Denodo
In this presentation, executives from Denodo preview the new Denodo Platform 6.0 release that delivers Dynamic Query Optimizer, cloud offering on Amazon Web Services, and self-service data discovery and search. Over 30 analysts, led by Claudia Imhoff, provide input on strategic direction and benefits of Denodo 6.0 to the data virtualization and the broader data integration market.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/DR6r3m.
Big Data Fabric for At-Scale Real-Time Analysis by Edwin RobbinsData Con LA
Abstract:- Companies are adopting big data for performing high-velocity real-time analytics on very large volumes of data to enable rapid analysis for business users using self-service and never-before-realized use cases. However, such projects have yielded limited value because these big data systems have become siloed from the rest of the enterprise systems holding critical business operational data. Big Data Fabric is a modern data architecture combining data virtualization, data prep, and lineage capabilities to seamlessly integrate at scale these huge, siloed volumes of structured and unstructured data with other enterprise data assets. This presentation will demonstrate with proven customer case studies in big data and IoT about the value of using big data fabric as a logical data lake for big data analytics.
Creating a Modern Data Architecture for Digital TransformationMongoDB
By managing Data in Motion, Data at Rest, and Data in Use differently, modern Information Management Solutions are enabling a whole range of architecture and design patterns that allow enterprises to fully harness the value in data flowing through their systems. In this session we explored some of the patterns (e.g. operational data lakes, CQRS, microservices and containerisation) that enable CIOs, CDOs and senior architects to tame the data challenge, and start to use data as a cross-enterprise asset.
Enterprise 360 - Graphs at the Center of a Data FabricPrecisely
Data fabric architectures are used to simplify and integrate data management across business functions to accelerate digital transformation. Creating a data fabric is a way to develop a data-centric view of your business which results in an Enterprise 360 perspective based on trusted data.
Industry analysts and vendors are increasingly finding that graph databases are a key enabling technology in support of
Data Fabric architectures that deliver trusted data.
During this on-demand webinar, we discuss how we help our customers implement a Data Fabric pattern using graph database technology in support of their key strategic objectives.
Denodo DataFest 2016: Big Data Virtualization in the CloudDenodo
Watch the full session: Denodo DataFest 2016 sessions: https://goo.gl/kahTgf
Many firms are adopting “cloud first” strategy and are migrating their on-premises technologies to the cloud. Logitech is one of them. They have adopted the AWS platform and big data on the cloud for all of their analytical needs, including Amazon Redshift and S3.
In this presentation, the Principal of Big Data and Analytics team at Logitech, Avinash Deshpande will present:
• The business rationale for migrating to the cloud
• How data virtualization enables the migration
• Running data virtualization itself in the cloud
This session also includes a panel discussion with:
• Avinash Deshpande, Principal – Big Data and Analytics at Logitech
• Kurt Jackson, Platform Lead at Autodesk
• Dan Young, Chief Data Architect at Indiana University
• Paul Moxon, Head of Product Management at Denodo (as moderator)
This session is part of the Denodo DataFest 2016 event. You can also watch more Denodo DataFest sessions on demand here: https://goo.gl/VXb6M6
Organizations have been collecting, storing, and accessing data from the beginning of computerization. Insights gained from analyzing the data enable them to identify new opportunities, improve core processes, enable continuous learning and differentiation, remain competitive, and thrive in an increasingly challenging business environment.
The well-established data architecture, consisting of a data warehouse, fed from multiple operational data stores, and fronted by BI tools, has served most organizations well. However, over the last two decades, with the explosion of internet-scale data, and the advent of new approaches to data and computational processing, this tried-and-true data architecture has come under strain, and has created both challenges and opportunities for organizations.
In this green paper, we will discuss modern approaches to data architecture that have evolved to address these challenges and provide a framework for companies to build a data architecture and better adapt to increasing demands of the modern business environment. This discussion of data architecture will be tied to the Data Maturity Journey introduced in EQengineered’s June 2021 green paper on Data Modernization.
Enabling digital transformation api ecosystems and data virtualizationDenodo
Watch the full webinar here: https://buff.ly/2KBKzLJ
Digital transformation, as cliché as it sounds, is on top of every decision maker’s strategic initiative list. And at the heart of any digital transformation, no matter the industry or the size of the company, there is an application programming interface (API) strategy. While API platforms enable companies to manage large numbers of APIs working in tandem, monitor their usage, and establish security between them, they are not optimized for data integration, so they cannot easily or quickly integrate large volumes of data between different systems. Data virtualization, however, can greatly enhance the capabilities of an API platform, increasing the benefits of an API-based architecture. With data virtualization as part of an API strategy, companies can streamline digital transformations of any size and scope.
Join us for this webinar to see these technologies in action in a demo and to get the answers to the following questions:
*How can data virtualization enhance the deployment and exposure of APIs?
*How does data virtualization work as a service container, as a source for microservices and as an API gateway?
*How can data virtualization create managed data services ecosystems in a thriving API economy?
*How are GetSmarter and others are leveraging data virtualization to facilitate API-based initiatives?
Partner Enablement: Key Differentiators of Denodo Platform 6.0 for the FieldDenodo
If you’re a Denodo Partner, this presentation is for you. Learn how to gain a competitive edge in the marketplace with Denodo Platform 6.0, and leverage off the new features and functionality.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/Qh8MeX.
Domain Driven Data: Apache Kafka® and the Data Meshconfluent
James Gollan, Confluent, Senior Solutions Engineer
From digital banking to industry 4.0 the nature of business is changing. Increasingly businesses are becoming software. And the lifeblood of software is data. Dealing with data at the enterprise level is tough, and their have been some missteps along the way.
This session will consider the increasingly popular idea of a 'data mesh' - the problems it solves and, perhaps most importantly, how an event streaming platform forms the bedrock of this new paradigm.
Recording to be available cnfl.io/meetup-hub
https://www.meetup.com/KafkaMelbourne/events/277076626/
Artificial intelligence and machine learning are currently all the rage. Every organisation is trying to jump on this bandwagon and cash in on their data reserves. At ThoughtWorks, we’d agree that this tech has huge potential — but as with all things, realising value depends on understanding how best to use it.
Augmentation, Collaboration, Governance: Defining the Future of Self-Service BIDenodo
Watch full webinar here: https://bit.ly/3zVJRRf
According to Dresner Advisory’s 2020 Self-Service Business Intelligence Market Study, 62% of the responding organizations say self-service BI is critical for their business. If we look deeper into the need for today’s self-service BI, it’s beyond some Executives and Business Users being enabled by IT for self-service dashboarding or report generation. Predictive analytics, self-service data preparation, collaborative data exploration are all different facets of new generation self-service BI. While democratization of data for self-service BI holds many benefits, strict data governance becomes increasingly important alongside.
In this session we will discuss:
- The latest trends and scopes of self-service BI
- The role of logical data fabric in self-service BI
- How Denodo enables self-service BI for a wide range of users - Customer case study on self-service BI
Hadoop meets Agile! - An Agile Big Data ModelUwe Printz
Big Data projects are a struggle, not only on the technical side but also on the organizational side. In this talk the author shares his experience and opinions from almost 5 years of Big Data projects and develops an Agile Big Data Model which reflects his ideas on how Big Data projects can be successful, even in large companies.
Talk held at the crossover meetup of the "Agile Stammtisch Rhein-Main" and the "Hadoop & Spark User Group Rhein-Main" at codecentric AG on 31.01.2017.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Myth Busters II: BI Tools and Data Virtualization are InterchangeableDenodo
Watch Here: https://bit.ly/2NcqU6F
We take on the 2nd myth about data virtualization and it’s one that suggests a BI tool can substitute a data virtualization software.
You might be thinking: If I can have multi-source queries and define a logical model in my reporting tool, why would I need a data virtualization software?
Reporting tools, no doubt important and necessary, focus on the visualization of data and it’s presentation to the business user. Data virtualization is a governed data access layer designed to connect to and provide transparency of all enterprise data.
Yet the myth suggests that these technologies are interchangeable. So we’re going to take it on!
Watch this webinar as we compare and contrast BI tools and data virtualization to draw a final conclusion.
The Great Lakes: How to Approach a Big Data ImplementationInside Analysis
The Briefing Room with Dr. Robin Bloor and Think Big, a Teradata Company
Live Webcast April 7, 2015
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=4114b87441ab7b2b4c52f6b24776e5a1
The more things change in Big Data, the more they stay the same. Indeed, there are many similarities between a Hadoop-based Data Lake and today’s modern Data Warehouse. Regardless of platform, information workers must still be able to turn their assets into action quickly, without taking a hit on governance or downstream performance.
Register for this episode of The Briefing Room to hear veteran Analyst Dr. Robin Bloor as he explains the challenges facing organizations who endeavor on Big Data projects. He’ll be briefed by Rick Stellwagen of Think Big, a Teradata Company, who will outline his company’s approach to handling Big Data implementations. Rick will discuss the role of the data lake, and how timely response of queries is critical for reporting and analysis.
Visit InsideAnalysis.com for more information.
Qiagram is a collaborative visual data exploration environment that enables investigator-initiated, hypothesis-driven data exploration, allowing business users as well as IT professionals to easily ask complex questions against complex data sets.
Solving Data Discovery Challenges at Lyft with Amundsen, an Open-source Metad...Databricks
Amundsen is the data discovery metadata platform that originated from Lyft which is recently donated to Linux Foundation AI. Since its open-sourced, Amundsen has been used and extended by many different companies within our community.
Big Data Tools: A Deep Dive into Essential ToolsFredReynolds2
Today, practically every firm uses big data to gain a competitive advantage in the market. With this in mind, freely available big data tools for analysis and processing are a cost-effective and beneficial choice for enterprises. Hadoop is the sector’s leading open-source initiative and big data tidal roller. Moreover, this is not the final chapter! Numerous other businesses pursue Hadoop’s free and open-source path.
Advanced Analytics and Machine Learning with Data VirtualizationDenodo
Watch full webinar here: https://bit.ly/3aXysas
Advanced data science techniques, like machine learning, have proven to be extremely useful to derive valuable insights from your data. Data Science platforms have become more approachable and user friendly. With all the advancements in the technology space, the Data Scientist is still spending most of the time massaging and manipulating the data into a usable data asset. How can we empower the data scientist? How can we make data more accessible, and foster a data sharing culture?
Join us, and we will show you how Data Virtualization can do just that, with an agile and AI/ML laced data management platform. It can empower your organization, foster a data sharing culture, and simplify the life of the data scientist.
Watch this webinar to learn:
- How data virtualization simplifies the life of the data scientist, by overcoming data access and manipulation hurdles.
- How integrated Denodo Data Science notebook provides for a unified environment
- How Denodo uses AI/ML internally to drive the value of the data and expose insights
- How customers have used Data Virtualization in their Data Science initiatives.
Warehousing Your Hits - The Why and How of Owning Your DataScott Arbeitman
These are the slides from my recent presentation at Melbourne' Web Analytics Wednesdays. I talk about transitioning from collecting your data in primary digital analytics systems to storing them in a data warehouse or data lake.
Quicker Insights and Sustainable Business Agility Powered By Data Virtualizat...Denodo
Watch full webinar here: https://bit.ly/3xj6fnm
Presented at Chief Data Officer Live 2021 A/NZ
The world is changing faster than ever. And for companies to compete and succeed they need to be agile in order to respond quickly to market changes and emerging opportunities. Data plays an integral role in achieving this business agility. However, given the complex nature of the enterprise data architecture finding and analysing data is an increasingly challenging task. Data virtualization is a modern data integration technique that integrates data in real-time, without having to physically replicate it.
Watch on-demand this session to understand what data virtualization is and how it:
- Delivers data in real-time, and without replication
- Creates a logical architecture to provide a single view of truth
- Centralises the data governance and security framework
- Democratises data for faster decision making and business agility
Enterprise Monitoring and Auditing in DenodoDenodo
Watch full webinar here: https://buff.ly/3P3l4oK
Proper monitoring of an enterprise system is critical to understanding its capacity and growth, anticipating potential issues, and even understanding key ROI metrics. This also facilitates the implementation of policies and user access audits which are key to optimizing the resource utilization in an organization. Do you want to learn more about the new Denodo features for monitoring, auditing, and visualizing enterprise monitoring data?
Join us for the session with Vijayalakshmi Mani, Data Engineer at Denodo, to understand how the new features and components help in monitoring your Denodo Servers and the resource utilizations and how to extract the most out of the logs that the Denodo Platform generates including FinOps information.
Watch on-demand and Learn:
- What is a Denodo Monitor and what’s new in it?
- How to visualize the Denodo Monitor Information and use of Diagnostics & Monitoring Tool
- Introduction to the new Denodo Dashboard
- Demonstration on the Denodo Dashboard
Lunch and Learn ANZ: Mastering Cloud Data Cost Control: A FinOps ApproachDenodo
Watch full webinar here: https://buff.ly/4bYOOgb
With the rise of cloud-first initiatives and pay-per-use systems, forecasting IT costs has become a challenge. It's easy to start small, but it's equally easy to get skyrocketing bills with little warning. FinOps is a discipline that tries to tackle these issues, by providing the framework to understand and optimize cloud costs in a more controlled manner. The Denodo Platform, being a middleware layer in charge of global data delivery, sits in a privileged position not only to help us understand where costs are coming from, but also to take action, manage, and reduce them.
Attend this session to learn:
- The importance of FinOps in a cloud architecture.
- How the Denodo Platform can help you collect and visualize key FinOps metrics to understand where your costs are coming from?
- What actions and controls the Denodo Platform offers to keep costs at bay.
Achieving Self-Service Analytics with a Governed Data Services LayerDenodo
Watch full webinar here: https://buff.ly/3wBhxYb
In an increasingly distributed and complex data landscape, it is becoming increasingly difficult to govern and secure data effectively throughout the enterprise. Whether it be securing data across different repositories or monitoring access across different business units, the proliferation of data technologies and repositories across both on-premises and in the cloud is making the task unattainable. The challenge is only made greater by the ongoing pressure to offer self-service data access to business users.
Watch on-demand and learn:
- How to use a logical data fabric to build an enterprise-wide data access role model.
- Centralise security when data is spread across multiple systems residing both on-premises and in the cloud.
- Control and audit data access across different regions.
What you need to know about Generative AI and Data Management?Denodo
Watch full webinar here: https://buff.ly/3UXy0A2
It should be no surprise that Generative AI will have a profound impact to data management in years to come. Much like other areas of the technology sector, the opportunities presented by GenAI will accelerate our efforts around all aspects of data management, including self-service, automation, data governance and security. On the other hand, it is also becoming clearer that to unleash the true potential of AI assistants powered by GenAI, we need novel implementation strategies and a reimagined data architecture. This presents an exhilarating yet challenging future, demanding innovative thinking and methodologies in data management.
Join us on this webinar to learn about:
- The opportunities and challenges presented by GenAI today.
- Exploiting GenAI to democratize data management.
- How to augment GenAI applications with corporate data and knowledge.
- How to get started.
Mastering Data Compliance in a Dynamic Business LandscapeDenodo
Watch full webinar here: https://buff.ly/48rpLQ3
Join us for an enlightening webinar, "Mastering Data Compliance in a Dynamic Business Landscape," presented by Denodo Technologies and W5 Consulting. This session is tailored for business leaders and decision-makers who are navigating the complexities of data compliance in an ever-evolving business environment.
This webinar will focus on why data compliance is crucial for your business. Discover how to turn compliance into a competitive advantage, enhancing operational efficiency and market trust. We'll also address the risks of non-compliance, including financial penalties and the loss of customer trust, and provide strategies to proactively overcome these challenges.
Key Takeaways:
- How can your business leverage data management practices to stay agile and compliant in a rapidly changing regulatory landscape?
- Keys to balancing data accessibility with security and privacy in today's data-driven environment.
- What are the common pitfalls in achieving compliance with regulations like GDPR, CCPA, and HIPAA, and how can your business avoid them?
We will go beyond the technical aspects and delve into how you can strategically position your organization in the realm of data management and compliance. Learn how to craft a data compliance strategy that aligns with your business goals, enhances operational efficiency, and builds stakeholder trust.
Denodo Partner Connect: Business Value Demo with Denodo Demo LiteDenodo
Watch full webinar here: https://buff.ly/3OCQvGk
In this session, Denodo Sales Engineer, Yik Chuan Tan, will guide you through the art of delivering a compelling demo of the Denodo Platform with Denodo Demo Lite. Watch to uncover the significant functionalities that set Denodo apart and learn how to effectively win over potential customers.
In this session, we will cover:
Understanding the Denodo Platform & Tailoring Your Demo to Prospect Needs: By gaining a comprehensive understanding of the Denodo Platform, its architecture, and how it addresses data management challenges, you can customize your demo to align with the specific needs and pain points of your prospects, including:
- seamless data integration with real-time access
- data security and governance
- self-service data discovery
- advanced analytics and reporting
- performance optimization scalability and deployment
Watch this Denodo demo session and acquire the skills and knowledge necessary to captivate your prospects. Whether you're a seasoned technical professional or new to the field, this session will equip you with the skills to deliver compelling demos that lead to successful conversions.
Expert Panel: Overcoming Challenges with Distributed Data to Maximize Busines...Denodo
Watch full webinar here: https://buff.ly/3wdI1il
As organizations compete in new markets and new channels, business data requirements include new data platforms and applications. Migration to the cloud typically adds more distributed data when operations set up their own data platforms. This spreads important data across on-premises and cloud-based data platforms. As a result, data silos proliferate and become difficult to access, integrate, manage, and govern. Many organizations are using cloud data platforms to consolidate data, but distributed environments are unlikely to go away.
Organizations need holistic data strategies for unifying distributed data environments to improve data access and data governance, optimize costs and performance, and take advantage of modern technologies as they arrive. This TDWI Expert Panel will focus on overcoming challenges with distributed data to maximize business value.
Key topics this panel will address include:
- Developing the right strategy for your use cases and workloads in distributed data environments, such as data fabrics, data virtualization, and data mesh
- Deciding whether to consolidate data silos or bridge them with distributed data technologies
- Enabling easier self-service access and analytics across a distributed data environment
- Maximizing the value of data catalogs and other data intelligence technologies for distributed data environments
- Monitoring and data observability for spotting problems and ensuring business satisfaction
Watch full webinar here: https://buff.ly/3UE5K5l
The ability to recognize and flag sensitive information within corporate datasets is essential for compliance with emerging privacy laws, for completing a privacy impact assessment (PIA) or data subject access request (DSAR), and also for cyber-insurance compliance. During this session, we will discuss data privacy laws, the challenges they present, and how they can be applied with modern tools.
Join us for the session driven by Mark Rowan, CEO at Data Sentinel, and Bhavita Jaiswal, SE at Denodo, who will show how a data classification engine augments Data Catalog to support data governance and compliance objectives.
Watch on-demand & Learn:
- Changing landscape of data privacy laws and compliance requirements
- How to create a data classification framework
- How Data Sentinel classifies data and this can be integrated into Denodo
- Using the enhanced data classifications via consuming tools such as Data Catalog and Power BI
Знакомство с виртуализацией данных для профессионалов в области данныхDenodo
Watch full webinar here: https://buff.ly/3OETC08
По данным аналитической компании Gartner, "к 2022 году 60% предприятий включат виртуализацию данных в качестве основного метода доставки данных в свою интеграционную архитектуру". Компания Gartner назвала Denodo лидером в Магическом квадранте 2020 года по инструментам интеграции данных.
В ходе этого 1,5-часового занятия вы узнаете, как виртуализация данных революционизирует бизнес и ИТ-подход к доступу, доставке, потреблению, управлению и защите данных, независимо от возраста вашей технологии, формата данных или их местонахождения. Эта зрелая технология устраняет разрыв между ИТ и бизнес-пользователями и обеспечивает значительную экономию средств и времени.
**ФОРМАТ
Онлайн-семинар продолжительностью 1 час 30 минут.
Благодаря записи вы можете выполнять упражнения в своем собственном темпе.
**ДЛЯ КОГО ЭТОТ СЕМИНАР?
ИТ-менеджеры / архитекторы
Специалисты по анализу данных / аналитики
CDO
**СОДЕРЖАНИЕ
В программе: введение в суть виртуализации данных, примеры использования, реальные примеры из практики клиентов и демонстрация возможностей платформы Denodo Platform:
Интеграция и предоставление данных быстро и легко с помощью платформы Denodo Platform 8.0
Оптимизатор запросов Denodo предоставляет данные в режиме реального времени, по запросу, даже для очень больших наборов данных
Выставлять данные в качестве "сервисов данных" для потребления различными пользователями и инструментами
Каталог данных: Открывайте и документируйте данные с помощью нашего Каталога данных
пространства для самостоятельного доступа к данным.
Виртуализация данных играет ключевую роль в управлении и обеспечении безопасности данных в вашей организации
**ПОВЕСТКА
Введение в виртуализацию данных
Примеры использования и примеры из практики клиентов
Архитектура - Управление и безопасность
Производительность
Демо
Следующие шаги: как самостоятельно протестировать и внедрить платформу
Интерактивная сессия вопросов и ответов
Data Democratization: A Secret Sauce to Say Goodbye to Data FragmentationDenodo
Watch full webinar here: https://buff.ly/41Zf31D
Despite recent and evolving technological advances, the vast amounts of data that exist in a typical enterprise is not always available to all stakeholders when they need it. In modern enterprises, there are broad sets of users, with varying levels of skill sets, who strive to make data-driven decisions daily but struggle to gain access to the data needed in a timely manner.
Join our webinar to learn how to:
- Unlock the Power of Your Data: Discover how data democratization can transform your organization by giving every user access to the data they need, when they need it.
- Say 'Goodbye' to Data Fragmentation: Learn practical strategies to break down data silos and foster a more collaborative and efficient data environment.
- Realize the Full Potential of Your Data: Hear success stories about industry leaders who have embraced data democratization and witnessed tangible results.
Denodo Partner Connect - Technical Webinar - Ask Me AnythingDenodo
Watch full webinar here: https://buff.ly/48ZpEf1
In this session, we will cover a deeper dive into the Denodo Platform 8.0 Certified Architect Associate (DEN80EDUCAA) exam by answering any questions that have developed since the previous session.
Additionally, we invite partners to bring any general questions related to Denodo, the Denodo Platform, or data management.
Lunch and Learn ANZ: Key Takeaways for 2023!Denodo
Watch full webinar here: https://buff.ly/3SnH5QY
2023 is coming to an end where organisations dependency on trusted, accurate, secure and contextual data only grows more challenging. The perpetual aspect in seeking new architectures, processes, organisational team structures to "get the business their data" and reduce the operating costs continues unabated. While confidence from the business in what "value" is being derived or "to be" delivered from these investments in data, is being heavily scrutinised. 2023 saw significant new releases from vendors, focusing on the Data Fabric.
At this session we will look at these topics and key takeaways for 2023, including;
- Data management and data integration market highlights for 2023
- Key achievements for Denodo in their journey as a leader in this market
- A few case studies from Australian organisations in how they are delivering strategic business value through Denodo's Data Fabric platform and what they have been doing differently
It’s a Wrap! 2023 – A Groundbreaking Year for AI and The Way ForwardDenodo
Watch full webinar here: https://buff.ly/3S4Y49o
A little over a year ago, we would not have expected the disruptions caused by the rise of Generative AI. If 2023 was a groundbreaking year for AI, what will 2024 bring? More importantly, what can you do now to take advantage of these trends and ensure you are future-proof?
For example:
- Generative AI will become more powerful and user-friendly, enabling novel and realistic content creation and automation.
- Data Architectures will need to adapt to feed these powerful new models.
- Data ecosystems are moving to the cloud, but there is a growing need to maintain control of costs and optimize workloads better.
Join us for a discussion on the most significant trends in the Data & AI space, and how you can prepare to ride this wave!
Quels sont les facteurs-clés de succès pour appliquer au mieux le RGPD à votr...Denodo
Watch full webinar here: https://buff.ly/3O7rd2R
Afin d’être conformes au RGPD, les entreprises ont besoin d'avoir une vue d'ensemble sur toutes leurs données et d'établir des contrôles de sécurité sur toute l'infrastructure. La virtualisation des données de Denodo permet de rassembler les multiples sources de données, de les rendre accessibles à partir d'une seule couche, et offre des capacités de monitoring pour surveiller les changements.
Pour cela, Square IT Services a développé pour l’un de ses grands clients français prestigieux dans le secteur du luxe une interface utilisateur ergonomique qui lui permet de consulter les informations personnelles de ses clients, vérifier leur éligibilité à pratiquer leur droit à l'oubli, et de désactiver leurs différents canaux de notification. Elle dispose aussi d'une fonctionnalité d'audit qui permet de tracer l'historique des opérations effectuées, et lui permet donc de retrouver notamment la date à laquelle la personne a été anonymisée.
L'ensemble des informations remontées au niveau de l'application sont récupérées à partir des APIs REST exposées par Denodo.
Dans ce webinar, nous allons détailler l’ensemble des fonctionnalités de l’application DPO-Cockpit autour d’une démo, et expliquer à chaque étape le rôle central de Denodo pour réussir à simplifier la gestion du RGPD tout en étant compliant.
Les points clés abordés:
- Contexte client face aux enjeux du RGPD
- Défis et challenges rencontrés
- Options et choix retenu (Denodo)
- Démarche: architecture de la solution proposée
- Démo de l'outil: fonctionnalités principales
Lunch and Learn ANZ: Achieving Self-Service Analytics with a Governed Data Se...Denodo
Watch full webinar here: https://buff.ly/48zzN2h
In an increasingly distributed and complex data landscape, it is becoming increasingly difficult to govern and secure data effectively throughout the enterprise. Whether it be securing data across different repositories or monitoring access across different business units, the proliferation of data technologies and repositories across both on-premises and in the cloud is making the task unattainable. The challenge is only made greater by the ongoing pressure to offer self-service data access to business users.
Tune in and learn:
- How to use a logical data fabric to build an enterprise-wide data access role model.
- Centralise security when data is spread across multiple systems residing both on-premises and in the cloud.
- Control and audit data access across different regions.
How to Build Your Data Marketplace with Data Virtualization?Denodo
Watch full webinar here: https://buff.ly/4aAi0cS
Organizations continue to collect mounds of data and it is spread over different locations and in different formats. The challenge is navigating the vastness and complexity of the modern data ecosystem to find the right data to suit your specific business purpose. Data is an important corporate asset and it needs to be leveraged but also protected.
By adopting an alternate approach to data management and adapting a logical data architecture, data can be democratized while providing centralized control within a distributed data landscape. The web-based Data Catalog tool acts as a single access point for secure enterprise-wide data access and governance. This corporate data marketplace provides visibility into your data ecosystem and allows data to be shared without compromising data security policies.
Catch this live webinar to understand how this approach can transform how you leverage data across the business:
- Empower the knowledge worker with data and increase productivity
- Promote data accuracy and trust to encourage re-use of important data assets
- Apply consistent security and governance policies across the enterprise data landscape
Webinar #2 - Transforming Challenges into Opportunities for Credit UnionsDenodo
Watch full webinar here: https://buff.ly/3vhzqL5
Join our exclusive webinar series designed to empower credit unions with transformative insights into the untapped potential of data. Explore how data can be a strategic asset, enabling credit unions to overcome challenges and foster substantial growth.
This webinar will delve into how data can serve as a catalyst for addressing key challenges faced by credit unions, propelling them towards a future of enhanced efficiency and growth.
Enabling Data Catalog users with advanced usabilityDenodo
Watch full webinar here: https://buff.ly/48A4Yu1
Data catalogs are increasingly important in any modern data-driven organization. They are essential to manage and make the most of the huge amount of data that any organization uses. As this information is continuously growing in size and complexity, data catalogs are key to providing Data Discovery, Data Governance, and Data Lineage capabilities.
Join us for the session driven by David Fernandez, Senior Technical Account Manager at Denodo, to review the latest features aimed at improving the usability of the Denodo Data Catalog.
Watch on-demand & Learn:
- Enhanced search capabilities using multiple terms.
- How to create workflows to manage internal requests.
- How to leverage the AI capabilities of Data Catalog to generate SQL queries from natural language.
Watch full webinar here: https://buff.ly/3vjrn0s
The purpose of the Denodo Platform 8.0 Certified Architect Associate (DEN80EDUCAA) exam is to provide organizations that use Denodo Platform 8.0 with a means of identifying suitably qualified data architects who understand the role and position of the Denodo Platform within their broader information architecture.
This exam covers the following technical topics and subject areas:
- Denodo Platform functionality, including
- Governance and metadata management
- Security
- Performance optimization
- Caching
- Defining Denodo Platform use scenarios
Along with some sample questions, a Denodo Sales Engineer will help you prepare for exam topics and ace the exam.
Join us now to start your journey toward becoming a Certified Denodo Architect Associate!
GenAI y el futuro de la gestión de datos: mitos y realidadesDenodo
Watch full webinar here: https://buff.ly/3NLMSNM
El Generative AI y los Large Language Models (LLMs), encabezados por GPT de OpenAI, han supuesto la mayor revolución en el mundo de la computación de los últimos años. Pero ¿Cómo afectan realmente a la gestión de datos? ¿Reemplazarán los LLMs al profesional de la gestion de datos? ¿Cuánto hay de mito y cuánto de realidad?
En esta sesión revisaremos:
- Que es la Generative AI y por qué es importante para la gestión de datos
- Presente y futuro de aplicación de genAI en el mundo de los datos
- Cómo preparar tu organización para la adopción de genAI
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
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.
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
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...
Denodo DataFest 2016: Comparing and Contrasting Data Virtualization With Data Prep, Data Blending, and Other Technologies
1. O C T O B E R 1 8 , 2 0 1 6 S A N F R A N C I S C O B A Y A R E A , C A
#DenodoDataFest
RAPID, AGILE DATA STRATEGIES
For Accelerating Analytics, Cloud, and Big Data Initiatives.
2. Comparing and Contrasting Data
Virtualization with Data Prep, Data
Blending, Data Catalog and Other
Technologies
Paul Moxon
Head of Product Management, Denodo
3. Agenda
1.Business Intelligence ‘Swim Lanes’
2.Data Prep – What is it and how does it work?
3.All you want to know about Data Blending
4.Data Catalogs – What, When, and How
5.Mapping to the Swim Lanes
6.Where Does Data Virtualization Fit?
7.Q&A
3
4. Business Intelligence ‘Swim Lanes’
4
• Task focused
• Productivity
• Self-service
• Quick and easy access
to data
• Automation (or
simplification) of data
gathering
• Tactical
• Team/Departmental
• Drives business
operations
• Shared data
• Process oriented
• Strategic
• Executive and KPI
dashboards
• Drives strategic
decisions
• Managed, governed
data
• Consistent data
6. Data preparation is the process of gathering,
combining, structuring and organizing data so it
can be analyzed as part of business intelligence or
analytics process.
7. Leading Data Prep Vendors
• Trifacta
• Paxata
• Alteryx
• Datameer
• Talend Data Preparation Desktop
• Informatica Rev
• SAS Data Loader
• IBM Watson
7
8. How Does It Work?
Interactive Data Prep process:
1. First data is ingested from data sources (or just a sample of data)
2. The user can define transformations to prepare the data
a. De-duplication, cleansing, combining data, pivoting, splitting rows/columns,
etc.
3. Run the transformation and export the data
a. Local file (typically CSV) or into Hadoop (Hive table or CSV file)
b. Alternatively export to BI Tool (e.g. Tableau Data Extract file)
Operationalize:
1. Schedule data prep transformations to generate new data files (à la ETL)
2. Publish results to collaboration environment
8
10. Pros:
• Ease of use
• Iterative data transformation
• Very good with delimited files
• Sampling makes tools responsive
• Data profiling help detect ‘suspect’
data
Cons:
• Ad-hoc rather than operational
• Reuse is limited to collaborative data
sets
• Performance
• Consistency and governance – data
chaos?
Pros and Cons of Data Prep
10
11. Data Prep is great for ad-hoc discovery
and analytics
• “I need to combine this with that and run
it through my analytics application…”
Not so good for consistent, repeatable
integration
• (Think: BI swim lanes)
But…
• Data Prep provides valuable knowledge
that can be used in systematic data
integration
Data Prep and Systematic Data Integration
11
14. Data blending is about working with multiple sources of
data by preparing them and joining them together for a
specific use case at a specific time. It’s different from data
integration, because data blending is about solving a
specific use case, whereas data integration typically gives
you a single source of truth…
15. Leading Data Blending ‘Vendors’
• Tableau
• Microstrategy
• SAP Business Objects
• IBM Cognos
• Qlik View
• etc.
15
16. How Does it Work?
Defining the data blending ‘model’:
1. Connect to data sources
a. Databases, Data Warehouse (via ODBC or JDBC), Files (Excel, CSV, etc.),
Hadoop, NoSQL, etc.
2. Select data you want to use – a sample is usually loaded
3. Build model using graphical tool to create Joins, Unions, etc.
4. Run the model for the full data set
5. Build your report or dashboard
Operationalize:
1. Model can be saved and expose as a ‘data source’ (usually in a ‘server’)
2. Accessed by other users
16
18. Pros:
• Built into BI/visualization tools
• Graphical query designer
• Provides semantic layer on top of
data sources
• Quick time from ‘data to analysis’ i.e.
removes wait for IT to provision a
data mart or similar
Cons:
• Ad-hoc rather than operational
• Specific to each BI/visualization tool
• Performance
• Consistency and governance
Pros and Cons of Data Blending
18
19. Francois Ajenstat, Chief Product Officer, Tableau
There are two flows; the ad-hoc and the operational…where we are
coming from is…I just want to integrate these two sources. It's not
formalized, per se, it's not a project. I just want to connect this and
this and I want to analyze it. How do we go from data to analysis as
quickly as possible? And when you want to formalize it, operationalize
it, make it repeatable, then [you use other tools].
19
21. Data Catalogs provide capabilities that enable any user –
from analysts to data scientists to developers – to discover,
understand, and consume data sources. Data Catalogs
typically include a crowdsourcing model of metadata and
annotations, and allow all users to contribute their
knowledge to build a community and culture of data.
22. Leading Data Catalog Vendors
• Alation/Teradata
• Cambridge Semantics Anzo Platform
• Informatica Enterprise Information Catalog
• Microsoft Azure Data Catalog
• Waterline Data
22
23. How Does it Work?
Building catalog:
1. Connect to data sources and consumers
a. Extract and analyze ‘technical’ metadata
b. Sample data and build data profile
2. Use NLP and ML for ‘auto-titling’ – based on defined business glossary
3. Use expert sourcing to validate catalog entries
4. Use crowd sourcing to build veracity profile
Accessing catalog:
1. Search tools for ‘natural language’ searches
2. APIs for tool integration
23
25. Pros:
• Great for analyzing data source and
inferring meaning from technical
metadata
• Gather ‘tribal knowledge’ about data
within organization
• Allow curation of metadata
• Provide single tool to find – and
understand - data
Cons:
• Do not address ‘data provisioning’ –
you need another tool for this
• File-based data?
Pros and Cons of Data Blending
25