Enabling a Data Mesh Architecture and Data Sharing Culture with Denodo
0 likes172 views
Watch full webinar here: https://bit.ly/3wATS7b Data Mesh presents a new distributed and decentralized paradigm for data management, where autonomous domains expose their own data as "data products" to the rest of the organization. It tries to reduce bottlenecks derived from an excessive dependance on centralized IT teams, and capitalizes on the specialized data knowledge that domain users already have. However, Data Mesh literature leaves the implementation of these ideas very open to each organization. Watch on-demand and learn: - Deep dive into the key ideas of Data Mesh - Understand how Denodo can help you implement a Data Mesh - Hear directly from a Denodo client, Landsbankinn, their journey from a traditional analytic architecture to Data Mesh using Denodo
1 of 36
Downloaded 28 times
Recommended
Data Mesh
Data MeshPiethein Strengholt Presentation on Data Mesh: The paradigm shift is a new type of eco-system architecture, which is a shift left towards a modern distributed architecture in which it allows domain-specific data and views “data-as-a-product,” enabling each domain to handle its own data pipelines.
Data Mesh in Practice - How Europe's Leading Online Platform for Fashion Goes...
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.
Data Mesh Part 4 Monolith to Mesh
Data Mesh Part 4 Monolith to MeshJeffrey T. Pollock This is Part 4 of the GoldenGate series on Data Mesh - a series of webinars helping customers understand how to move off of old-fashioned monolithic data integration architecture and get ready for more agile, cost-effective, event-driven solutions. The Data Mesh is a kind of Data Fabric that emphasizes business-led data products running on event-driven streaming architectures, serverless, and microservices based platforms. These emerging solutions are essential for enterprises that run data-driven services on multi-cloud, multi-vendor ecosystems.
Join this session to get a fresh look at Data Mesh; we'll start with core architecture principles (vendor agnostic) and transition into detailed examples of how Oracle's GoldenGate platform is providing capabilities today. We will discuss essential technical characteristics of a Data Mesh solution, and the benefits that business owners can expect by moving IT in this direction. For more background on Data Mesh, Part 1, 2, and 3 are on the GoldenGate YouTube channel: https://www.youtube.com/playlist?list=PLbqmhpwYrlZJ-583p3KQGDAd6038i1ywe
Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/)
Mr. Pollock is an expert technology leader for data platforms, big data, data integration and governance. Jeff has been CTO at California startups and a senior exec at Fortune 100 tech vendors. He is currently Oracle VP of Products and Cloud Services for Data Replication, Streaming Data and Database Migrations. While at IBM, he was head of all Information Integration, Replication and Governance products, and previously Jeff was an independent architect for US Defense Department, VP of Technology at Cerebra and CTO of Modulant – he has been engineering artificial intelligence based data platforms since 2001. As a business consultant, Mr. Pollock was a Head Architect at Ernst & Young’s Center for Technology Enablement. Jeff is also the author of “Semantic Web for Dummies” and "Adaptive Information,” a frequent keynote at industry conferences, author for books and industry journals, formerly a contributing member of W3C and OASIS, and an engineering instructor with UC Berkeley’s Extension for object-oriented systems, software development process and enterprise architecture.
DW Migration Webinar-March 2022.pptx
DW Migration Webinar-March 2022.pptxDatabricks The document discusses migrating a data warehouse to the Databricks Lakehouse Platform. It outlines why legacy data warehouses are struggling, how the Databricks Platform addresses these issues, and key considerations for modern analytics and data warehousing. The document then provides an overview of the migration methodology, approach, strategies, and key takeaways for moving to a lakehouse on Databricks.
Architect’s Open-Source Guide for a Data Mesh Architecture
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
Data Mesh for Dinner
Data Mesh for DinnerKent Graziano Data mesh is a decentralized approach to managing and accessing analytical data at scale. It distributes responsibility for data pipelines and quality to domain experts. The key principles are domain-centric ownership, treating data as a product, and using a common self-service infrastructure platform. Snowflake is well-suited for implementing a data mesh with its capabilities for sharing data and functions securely across accounts and clouds, with built-in governance and a data marketplace for discovery. A data mesh implemented on Snowflake's data cloud can support truly global and multi-cloud data sharing and management according to data mesh principles.
Data Catalog for Better Data Discovery and Governance
Data Catalog for Better Data Discovery and GovernanceDenodo Watch full webinar here: https://buff.ly/2Vq9FR0
Data catalogs are en vogue answering critical data governance questions like “Where all does my data reside?” “What other entities are associated with my data?” “What are the definitions of the data fields?” and “Who accesses the data?” Data catalogs maintain the necessary business metadata to answer these questions and many more. But that’s not enough. For it to be useful, data catalogs need to deliver these answers to the business users right within the applications they use.
In this session, you will learn:
*How data catalogs enable enterprise-wide data governance regimes
*What key capability requirements should you expect in data catalogs
*How data virtualization combines dynamic data catalogs with delivery
Building a modern data warehouse
Building a modern data warehouseJames Serra Embarking on building a modern data warehouse in the cloud can be an overwhelming experience due to the sheer number of products that can be used, especially when the use cases for many products overlap others. In this talk I will cover the use cases of many of the Microsoft products that you can use when building a modern data warehouse, broken down into four areas: ingest, store, prep, and model & serve. It’s a complicated story that I will try to simplify, giving blunt opinions of when to use what products and the pros/cons of each.
RWDG Slides: What is a Data Steward to do?
RWDG Slides: What is a Data Steward to do?DATAVERSITY Most people recognize that Data Stewards play an essential role in their Data Governance and Information Governance programs. However, the manner in which Data Stewards are used is not the same from organization to organization. How you use Data Stewards depends on your goals for Data Governance.
Join Bob Seiner for this month’s RWDG webinar where he will share different ways to activate Data Stewards based on the purpose of your program. Bob will talk about options to extend existing Data Steward activity and how to build new functionality into the role of your Data Stewards.
In this webinar, Bob will discuss:
- The crucial role of the Data Steward in Data Governance
- Different types of Data Stewards and what they do
- Aligning Data Steward activities with program goals
- Improving existing Data Steward actions
- Finding new ways to use your Data Stewards
Challenges in Building a Data Pipeline
Challenges in Building a Data PipelineManish Kumar The document discusses challenges in building a data pipeline including making it highly scalable, available with low latency and zero data loss while supporting multiple data sources. It covers expectations around real-time vs batch processing and streaming vs batch data. Implementation approaches like ETL vs ELT are examined along with replication modes, challenges around schema changes and NoSQL. Effective implementations should address transformations, security, replays, monitoring and more. Reference architectures like Lambda and Kappa are briefly outlined.
Getting Started with Delta Lake on Databricks
Getting Started with Delta Lake on DatabricksKnoldus Inc. In this session, we'll cover how with delta lake we can store the data and tables in databricks in an optimized way
An Analytics Engineer’s Guide to Streaming With Amy Chen | Current 2022
An Analytics Engineer’s Guide to Streaming With Amy Chen | Current 2022HostedbyConfluent An Analytics Engineer’s Guide to Streaming With Amy Chen | Current 2022
What happens to the modern data stack (MDS) and analytics as a whole when streaming becomes accessible? For years, the MDS has been centered around batch-based workflows with dbt at its core, introducing software engineering best practices to analysts. But now with even major data warehouses like Snowflake getting in the game, expanding their streaming capabilities, what does that mean?
In this talk, we will explore what streaming in a batch-based analytics world should look like. How does that change your thoughts about implementing testing and performance optimization in your data pipelines? Do you still need dbt? And the question that we are all asking: do you really need a real-time dashboard?
Activate Data Governance Using the Data Catalog
Activate Data Governance Using the Data CatalogDATAVERSITY This document discusses activating data governance using a data catalog. It compares active vs passive data governance, with active embedding governance into people's work through a catalog. The catalog plays a key role by allowing stewards to document definition, production, and usage of data in a centralized place. For governance to be effective, metadata from various sources must be consolidated and maintained in the catalog.
Operational Data Vault
Operational Data VaultEmpowered Holdings, LLC The document discusses operational data warehousing and the Data Vault model. It begins with an agenda for the presentation and introduction of the speaker. It then provides a short review of the Data Vault model. The remainder of the document discusses operational data warehousing, how the Data Vault model is well-suited for this purpose, and the benefits it provides including flexibility, scalability, and productivity. It also discusses how tools and technologies are advancing to support automation and self-service business intelligence using an operational data warehouse architecture based on the Data Vault model.
Reference Data Management
Reference Data ManagementProfinit Reference data is something we often encounter in our projects. In our experience, it is often underestimated and does not get enough attention. In the webinar, we want to make you aware of some interesting aspects of ‘reference data’ such as how it relates to MDM, which it’s often mixed with.
Data Warehousing Trends, Best Practices, and Future Outlook
Data Warehousing Trends, Best Practices, and Future OutlookJames Serra Over the last decade, the 3Vs of data - Volume, Velocity & Variety has grown massively. The Big Data revolution has completely changed the way companies collect, analyze & store data. Advancements in cloud-based data warehousing technologies have empowered companies to fully leverage big data without heavy investments both in terms of time and resources. But, that doesn’t mean building and managing a cloud data warehouse isn’t accompanied by any challenges. From deciding on a service provider to the design architecture, deploying a data warehouse tailored to your business needs is a strenuous undertaking. Looking to deploy a data warehouse to scale your company’s data infrastructure or still on the fence? In this presentation you will gain insights into the current Data Warehousing trends, best practices, and future outlook. Learn how to build your data warehouse with the help of real-life use-cases and discussion on commonly faced challenges. In this session you will learn:
- Choosing the best solution - Data Lake vs. Data Warehouse vs. Data Mart
- Choosing the best Data Warehouse design methodologies: Data Vault vs. Kimball vs. Inmon
- Step by step approach to building an effective data warehouse architecture
- Common reasons for the failure of data warehouse implementations and how to avoid them
Azure data platform overview
Azure data platform overviewJames Serra This document provides an overview and summary of the author's background and expertise. It states that the author has over 30 years of experience in IT working on many BI and data warehouse projects. It also lists that the author has experience as a developer, DBA, architect, and consultant. It provides certifications held and publications authored as well as noting previous recognition as an SQL Server MVP.
Data Pipline Observability meetup 
Data Pipline Observability meetup Omid Vahdaty This document discusses the need for observability in data pipelines. It notes that real data pipelines often fail or take a long time to rerun without providing any insight into what went wrong. This is because of frequent code, data, dependency, and infrastructure changes. The document recommends taking a production engineering approach to observability using metrics, logging, and alerting tools. It also suggests experiment management and encapsulating reporting in notebooks. Most importantly, it stresses measuring everything through metrics at all stages of data ingestion and processing to better understand where issues occur.
Data Lake Overview
Data Lake OverviewJames Serra The data lake has become extremely popular, but there is still confusion on how it should be used. In this presentation I will cover common big data architectures that use the data lake, the characteristics and benefits of a data lake, and how it works in conjunction with a relational data warehouse. Then I’ll go into details on using Azure Data Lake Store Gen2 as your data lake, and various typical use cases of the data lake. As a bonus I’ll talk about how to organize a data lake and discuss the various products that can be used in a modern data warehouse.
Architecting a datalake
Architecting a datalakeLaurent Leturgez This document discusses architecting a data lake. It begins by introducing the speaker and topic. It then defines a data lake as a repository that stores enterprise data in its raw format including structured, semi-structured, and unstructured data. The document outlines some key aspects to consider when architecting a data lake such as design, security, data movement, processing, and discovery. It provides an example design and discusses solutions from vendors like AWS, Azure, and GCP. Finally, it includes an example implementation using Azure services for an IoT project that predicts parts failures in trucks.
Data strategy demistifying data
Data strategy demistifying dataHans Verstraeten Develop a data strategy, building blocks of a good data strategy, visualize your data and use it to discover new sources of value
Big data architectures and the data lake
Big data architectures and the data lakeJames Serra The document provides an overview of big data architectures and the data lake concept. It discusses why organizations are adopting data lakes to handle increasing data volumes and varieties. The key aspects covered include:
- Defining top-down and bottom-up approaches to data management
- Explaining what a data lake is and how Hadoop can function as the data lake
- Describing how a modern data warehouse combines features of a traditional data warehouse and data lake
- Discussing how federated querying allows data to be accessed across multiple sources
- Highlighting benefits of implementing big data solutions in the cloud
- Comparing shared-nothing, massively parallel processing (MPP) architectures to symmetric multi-processing (
Enabling a Data Mesh Architecture with Data Virtualization
Enabling a Data Mesh Architecture with Data VirtualizationDenodo Watch full webinar here: https://bit.ly/3rwWhyv
The Data Mesh architectural design was first proposed in 2019 by Zhamak Dehghani, principal technology consultant at Thoughtworks, a technology company that is closely associated with the development of distributed agile methodology. A data mesh is a distributed, de-centralized data infrastructure in which multiple autonomous domains manage and expose their own data, called “data products,” to the rest of the organization.
Organizations leverage data mesh architecture when they experience shortcomings in highly centralized architectures, such as the lack domain-specific expertise in data teams, the inflexibility of centralized data repositories in meeting the specific needs of different departments within large organizations, and the slow nature of centralized data infrastructures in provisioning data and responding to changes.
In this session, Pablo Alvarez, Global Director of Product Management at Denodo, explains how data virtualization is your best bet for implementing an effective data mesh architecture.
You will learn:
- How data mesh architecture not only enables better performance and agility, but also self-service data access
- The requirements for “data products” in the data mesh world, and how data virtualization supports them
- How data virtualization enables domains in a data mesh to be truly autonomous
- Why a data lake is not automatically a data mesh
- How to implement a simple, functional data mesh architecture using data virtualization
Modernizing to a Cloud Data Architecture
Modernizing to a Cloud Data ArchitectureDatabricks Organizations with on-premises Hadoop infrastructure are bogged down by system complexity, unscalable infrastructure, and the increasing burden on DevOps to manage legacy architectures. Costs and resource utilization continue to go up while innovation has flatlined. In this session, you will learn why, now more than ever, enterprises are looking for cloud alternatives to Hadoop and are migrating off of the architecture in large numbers. You will also learn how elastic compute models’ benefits help one customer scale their analytics and AI workloads and best practices from their experience on a successful migration of their data and workloads to the cloud.
Data Observability.pptx
Data Observability.pptxSonaSamad1 The document discusses data observability at Intuit and how it helps prevent data incidents. It introduces Intuit's data ecosystem and challenges around data quality. It then describes Intuit's data observability model of curing, detecting, preventing and eradicating issues through techniques like data quality checks, anomaly detection, infrastructure monitoring and data triage at multiple layers. These techniques help reduce incorrect reporting, incidents, troubleshooting time and ensure SLAs are met by improving data reliability across Intuit's data platforms and products.
Snowflake essentials
Snowflake essentialsqureshihamid Snowflake concepts & hands on expertise to help get you started on implementing Data warehouses using Snowflake. Necessary information and skills that will help you master Snowflake essentials.
Data Warehouse or Data Lake, Which Do I Choose?
Data Warehouse or Data Lake, Which Do I Choose?DATAVERSITY Today’s data-driven companies have a choice to make – where do we store our data? As the move to the cloud continues to be a driving factor, the choice becomes either the data warehouse (Snowflake et al) or the data lake (AWS S3 et al). There are pro’s and con’s for each approach. While the data warehouse will give you strong data management with analytics, they don’t do well with semi-structured and unstructured data with tightly coupled storage and compute, not to mention expensive vendor lock-in. On the other hand, data lakes allow you to store all kinds of data and are extremely affordable, but they’re only meant for storage and by themselves provide no direct value to an organization.
Enter the Open Data Lakehouse, the next evolution of the data stack that gives you the openness and flexibility of the data lake with the key aspects of the data warehouse like management and transaction support.
In this webinar, you’ll hear from Ali LeClerc who will discuss the data landscape and why many companies are moving to an open data lakehouse. Ali will share more perspective on how you should think about what fits best based on your use case and workloads, and how some real world customers are using Presto, a SQL query engine, to bring analytics to the data lakehouse.
Microsoft Data Platform - What's included
Microsoft Data Platform - What's includedJames Serra This document provides an overview of a speaker and their upcoming presentation on Microsoft's data platform. The speaker is a 30-year IT veteran who has worked in various roles including BI architect, developer, and consultant. Their presentation will cover collecting and managing data, transforming and analyzing data, and visualizing and making decisions from data. It will also discuss Microsoft's various product offerings for data warehousing and big data solutions.
Denodo: Enabling a Data Mesh Architecture and Data Sharing Culture at Landsba...
Denodo: Enabling a Data Mesh Architecture and Data Sharing Culture at Landsba...Denodo This document discusses Landsbankinn's implementation of a logical data warehouse and data mesh architecture using Denodo over five years. It summarizes that:
1) Landsbankinn initially implemented a logical data warehouse with Denodo to create a single point of access, business logic, and control for reporting across heterogeneous data sources.
2) Over the next two years, they expanded this to include additional data consumers and sources without requiring ETL.
3) They then realized a data mesh approach where domains owned and published their data was better, reducing complexity and meetings for mapping changes.
4) The current approach has domains developing and sharing views via a data mesh while the logical data warehouse combines them, improving governance and flexibility
How to Place Data at the Center of Digital Transformation in BFSI
How to Place Data at the Center of Digital Transformation in BFSIDenodo Watch full webinar here: https://bit.ly/3j7E9Jo
Consumers are increasingly using digital banking tools and insurance models, and these numbers will only continue to grow. Financial and insurance organizations have to adapt to the new and always changing situation while complying with new regulations, such as IFRS17, and embracing ESG criteria.
At the heart of any digital transformation is data. Therefore, it is not a stretch to say that data management and analytics strategies differentiate many of the leaders from the laggards in the banking, financial services and insurance (BFSI) industry. BFSI organizations still relying on slow, traditional systems and data management processes will find themselves falling behind their competition. In addition, as many adopt cloud strategies, these traditional approaches fill the cloud modernization process with downtime and end user frustration. In fact, according to a McKinsey article, cloud combined with distributed data infrastructure will define how consumers and providers adopt digital insurance models for the next decade.
Hear how the BFSI industry is leveraging data virtualization to deploy data fabric or data mesh architectures for enterprise-wide digital transformation.
Join this webinar to learn:
- The latest trends in BFSI for 2023 and how data and analytics is reshaping the industry
- How a logical data architecture can help you capitalize on your data
- How Denodo customers digitally transformed themselves using the Denodo Platform
More Related Content
What's hot (20)
RWDG Slides: What is a Data Steward to do?
RWDG Slides: What is a Data Steward to do?DATAVERSITY Most people recognize that Data Stewards play an essential role in their Data Governance and Information Governance programs. However, the manner in which Data Stewards are used is not the same from organization to organization. How you use Data Stewards depends on your goals for Data Governance.
Join Bob Seiner for this month’s RWDG webinar where he will share different ways to activate Data Stewards based on the purpose of your program. Bob will talk about options to extend existing Data Steward activity and how to build new functionality into the role of your Data Stewards.
In this webinar, Bob will discuss:
- The crucial role of the Data Steward in Data Governance
- Different types of Data Stewards and what they do
- Aligning Data Steward activities with program goals
- Improving existing Data Steward actions
- Finding new ways to use your Data Stewards
Challenges in Building a Data Pipeline
Challenges in Building a Data PipelineManish Kumar The document discusses challenges in building a data pipeline including making it highly scalable, available with low latency and zero data loss while supporting multiple data sources. It covers expectations around real-time vs batch processing and streaming vs batch data. Implementation approaches like ETL vs ELT are examined along with replication modes, challenges around schema changes and NoSQL. Effective implementations should address transformations, security, replays, monitoring and more. Reference architectures like Lambda and Kappa are briefly outlined.
Getting Started with Delta Lake on Databricks
Getting Started with Delta Lake on DatabricksKnoldus Inc. In this session, we'll cover how with delta lake we can store the data and tables in databricks in an optimized way
An Analytics Engineer’s Guide to Streaming With Amy Chen | Current 2022
An Analytics Engineer’s Guide to Streaming With Amy Chen | Current 2022HostedbyConfluent An Analytics Engineer’s Guide to Streaming With Amy Chen | Current 2022
What happens to the modern data stack (MDS) and analytics as a whole when streaming becomes accessible? For years, the MDS has been centered around batch-based workflows with dbt at its core, introducing software engineering best practices to analysts. But now with even major data warehouses like Snowflake getting in the game, expanding their streaming capabilities, what does that mean?
In this talk, we will explore what streaming in a batch-based analytics world should look like. How does that change your thoughts about implementing testing and performance optimization in your data pipelines? Do you still need dbt? And the question that we are all asking: do you really need a real-time dashboard?
Activate Data Governance Using the Data Catalog
Activate Data Governance Using the Data CatalogDATAVERSITY This document discusses activating data governance using a data catalog. It compares active vs passive data governance, with active embedding governance into people's work through a catalog. The catalog plays a key role by allowing stewards to document definition, production, and usage of data in a centralized place. For governance to be effective, metadata from various sources must be consolidated and maintained in the catalog.
Operational Data Vault
Operational Data VaultEmpowered Holdings, LLC The document discusses operational data warehousing and the Data Vault model. It begins with an agenda for the presentation and introduction of the speaker. It then provides a short review of the Data Vault model. The remainder of the document discusses operational data warehousing, how the Data Vault model is well-suited for this purpose, and the benefits it provides including flexibility, scalability, and productivity. It also discusses how tools and technologies are advancing to support automation and self-service business intelligence using an operational data warehouse architecture based on the Data Vault model.
Reference Data Management
Reference Data ManagementProfinit Reference data is something we often encounter in our projects. In our experience, it is often underestimated and does not get enough attention. In the webinar, we want to make you aware of some interesting aspects of ‘reference data’ such as how it relates to MDM, which it’s often mixed with.
Data Warehousing Trends, Best Practices, and Future Outlook
Data Warehousing Trends, Best Practices, and Future OutlookJames Serra Over the last decade, the 3Vs of data - Volume, Velocity & Variety has grown massively. The Big Data revolution has completely changed the way companies collect, analyze & store data. Advancements in cloud-based data warehousing technologies have empowered companies to fully leverage big data without heavy investments both in terms of time and resources. But, that doesn’t mean building and managing a cloud data warehouse isn’t accompanied by any challenges. From deciding on a service provider to the design architecture, deploying a data warehouse tailored to your business needs is a strenuous undertaking. Looking to deploy a data warehouse to scale your company’s data infrastructure or still on the fence? In this presentation you will gain insights into the current Data Warehousing trends, best practices, and future outlook. Learn how to build your data warehouse with the help of real-life use-cases and discussion on commonly faced challenges. In this session you will learn:
- Choosing the best solution - Data Lake vs. Data Warehouse vs. Data Mart
- Choosing the best Data Warehouse design methodologies: Data Vault vs. Kimball vs. Inmon
- Step by step approach to building an effective data warehouse architecture
- Common reasons for the failure of data warehouse implementations and how to avoid them
Azure data platform overview
Azure data platform overviewJames Serra This document provides an overview and summary of the author's background and expertise. It states that the author has over 30 years of experience in IT working on many BI and data warehouse projects. It also lists that the author has experience as a developer, DBA, architect, and consultant. It provides certifications held and publications authored as well as noting previous recognition as an SQL Server MVP.
Data Pipline Observability meetup
Data Pipline Observability meetup Omid Vahdaty This document discusses the need for observability in data pipelines. It notes that real data pipelines often fail or take a long time to rerun without providing any insight into what went wrong. This is because of frequent code, data, dependency, and infrastructure changes. The document recommends taking a production engineering approach to observability using metrics, logging, and alerting tools. It also suggests experiment management and encapsulating reporting in notebooks. Most importantly, it stresses measuring everything through metrics at all stages of data ingestion and processing to better understand where issues occur.
Data Lake Overview
Data Lake OverviewJames Serra The data lake has become extremely popular, but there is still confusion on how it should be used. In this presentation I will cover common big data architectures that use the data lake, the characteristics and benefits of a data lake, and how it works in conjunction with a relational data warehouse. Then I’ll go into details on using Azure Data Lake Store Gen2 as your data lake, and various typical use cases of the data lake. As a bonus I’ll talk about how to organize a data lake and discuss the various products that can be used in a modern data warehouse.
Architecting a datalake
Architecting a datalakeLaurent Leturgez This document discusses architecting a data lake. It begins by introducing the speaker and topic. It then defines a data lake as a repository that stores enterprise data in its raw format including structured, semi-structured, and unstructured data. The document outlines some key aspects to consider when architecting a data lake such as design, security, data movement, processing, and discovery. It provides an example design and discusses solutions from vendors like AWS, Azure, and GCP. Finally, it includes an example implementation using Azure services for an IoT project that predicts parts failures in trucks.
Data strategy demistifying data
Data strategy demistifying dataHans Verstraeten Develop a data strategy, building blocks of a good data strategy, visualize your data and use it to discover new sources of value
Big data architectures and the data lake
Big data architectures and the data lakeJames Serra The document provides an overview of big data architectures and the data lake concept. It discusses why organizations are adopting data lakes to handle increasing data volumes and varieties. The key aspects covered include:
- Defining top-down and bottom-up approaches to data management
- Explaining what a data lake is and how Hadoop can function as the data lake
- Describing how a modern data warehouse combines features of a traditional data warehouse and data lake
- Discussing how federated querying allows data to be accessed across multiple sources
- Highlighting benefits of implementing big data solutions in the cloud
- Comparing shared-nothing, massively parallel processing (MPP) architectures to symmetric multi-processing (
Enabling a Data Mesh Architecture with Data Virtualization
Enabling a Data Mesh Architecture with Data VirtualizationDenodo Watch full webinar here: https://bit.ly/3rwWhyv
The Data Mesh architectural design was first proposed in 2019 by Zhamak Dehghani, principal technology consultant at Thoughtworks, a technology company that is closely associated with the development of distributed agile methodology. A data mesh is a distributed, de-centralized data infrastructure in which multiple autonomous domains manage and expose their own data, called “data products,” to the rest of the organization.
Organizations leverage data mesh architecture when they experience shortcomings in highly centralized architectures, such as the lack domain-specific expertise in data teams, the inflexibility of centralized data repositories in meeting the specific needs of different departments within large organizations, and the slow nature of centralized data infrastructures in provisioning data and responding to changes.
In this session, Pablo Alvarez, Global Director of Product Management at Denodo, explains how data virtualization is your best bet for implementing an effective data mesh architecture.
You will learn:
- How data mesh architecture not only enables better performance and agility, but also self-service data access
- The requirements for “data products” in the data mesh world, and how data virtualization supports them
- How data virtualization enables domains in a data mesh to be truly autonomous
- Why a data lake is not automatically a data mesh
- How to implement a simple, functional data mesh architecture using data virtualization
Modernizing to a Cloud Data Architecture
Modernizing to a Cloud Data ArchitectureDatabricks Organizations with on-premises Hadoop infrastructure are bogged down by system complexity, unscalable infrastructure, and the increasing burden on DevOps to manage legacy architectures. Costs and resource utilization continue to go up while innovation has flatlined. In this session, you will learn why, now more than ever, enterprises are looking for cloud alternatives to Hadoop and are migrating off of the architecture in large numbers. You will also learn how elastic compute models’ benefits help one customer scale their analytics and AI workloads and best practices from their experience on a successful migration of their data and workloads to the cloud.
Data Observability.pptx
Data Observability.pptxSonaSamad1 The document discusses data observability at Intuit and how it helps prevent data incidents. It introduces Intuit's data ecosystem and challenges around data quality. It then describes Intuit's data observability model of curing, detecting, preventing and eradicating issues through techniques like data quality checks, anomaly detection, infrastructure monitoring and data triage at multiple layers. These techniques help reduce incorrect reporting, incidents, troubleshooting time and ensure SLAs are met by improving data reliability across Intuit's data platforms and products.
Snowflake essentials
Snowflake essentialsqureshihamid Snowflake concepts & hands on expertise to help get you started on implementing Data warehouses using Snowflake. Necessary information and skills that will help you master Snowflake essentials.
Data Warehouse or Data Lake, Which Do I Choose?
Data Warehouse or Data Lake, Which Do I Choose?DATAVERSITY Today’s data-driven companies have a choice to make – where do we store our data? As the move to the cloud continues to be a driving factor, the choice becomes either the data warehouse (Snowflake et al) or the data lake (AWS S3 et al). There are pro’s and con’s for each approach. While the data warehouse will give you strong data management with analytics, they don’t do well with semi-structured and unstructured data with tightly coupled storage and compute, not to mention expensive vendor lock-in. On the other hand, data lakes allow you to store all kinds of data and are extremely affordable, but they’re only meant for storage and by themselves provide no direct value to an organization.
Enter the Open Data Lakehouse, the next evolution of the data stack that gives you the openness and flexibility of the data lake with the key aspects of the data warehouse like management and transaction support.
In this webinar, you’ll hear from Ali LeClerc who will discuss the data landscape and why many companies are moving to an open data lakehouse. Ali will share more perspective on how you should think about what fits best based on your use case and workloads, and how some real world customers are using Presto, a SQL query engine, to bring analytics to the data lakehouse.
Microsoft Data Platform - What's included
Microsoft Data Platform - What's includedJames Serra This document provides an overview of a speaker and their upcoming presentation on Microsoft's data platform. The speaker is a 30-year IT veteran who has worked in various roles including BI architect, developer, and consultant. Their presentation will cover collecting and managing data, transforming and analyzing data, and visualizing and making decisions from data. It will also discuss Microsoft's various product offerings for data warehousing and big data solutions.
Similar to Enabling a Data Mesh Architecture and Data Sharing Culture with Denodo (20)
Denodo: Enabling a Data Mesh Architecture and Data Sharing Culture at Landsba...
Denodo: Enabling a Data Mesh Architecture and Data Sharing Culture at Landsba...Denodo This document discusses Landsbankinn's implementation of a logical data warehouse and data mesh architecture using Denodo over five years. It summarizes that:
1) Landsbankinn initially implemented a logical data warehouse with Denodo to create a single point of access, business logic, and control for reporting across heterogeneous data sources.
2) Over the next two years, they expanded this to include additional data consumers and sources without requiring ETL.
3) They then realized a data mesh approach where domains owned and published their data was better, reducing complexity and meetings for mapping changes.
4) The current approach has domains developing and sharing views via a data mesh while the logical data warehouse combines them, improving governance and flexibility
How to Place Data at the Center of Digital Transformation in BFSI
How to Place Data at the Center of Digital Transformation in BFSIDenodo Watch full webinar here: https://bit.ly/3j7E9Jo
Consumers are increasingly using digital banking tools and insurance models, and these numbers will only continue to grow. Financial and insurance organizations have to adapt to the new and always changing situation while complying with new regulations, such as IFRS17, and embracing ESG criteria.
At the heart of any digital transformation is data. Therefore, it is not a stretch to say that data management and analytics strategies differentiate many of the leaders from the laggards in the banking, financial services and insurance (BFSI) industry. BFSI organizations still relying on slow, traditional systems and data management processes will find themselves falling behind their competition. In addition, as many adopt cloud strategies, these traditional approaches fill the cloud modernization process with downtime and end user frustration. In fact, according to a McKinsey article, cloud combined with distributed data infrastructure will define how consumers and providers adopt digital insurance models for the next decade.
Hear how the BFSI industry is leveraging data virtualization to deploy data fabric or data mesh architectures for enterprise-wide digital transformation.
Join this webinar to learn:
- The latest trends in BFSI for 2023 and how data and analytics is reshaping the industry
- How a logical data architecture can help you capitalize on your data
- How Denodo customers digitally transformed themselves using the Denodo Platform
Logical Data Fabric and Data Mesh – Driving Business Outcomes
Logical Data Fabric and Data Mesh – Driving Business OutcomesDenodo Watch full webinar here: https://buff.ly/3qgGjtA
Presented at TDWI VIRTUAL SUMMIT - Modernizing Data Management
While the technological advances of the past decade have addressed the scale of data processing and data storage, they have failed to address scale in other dimensions: proliferation of sources of data, diversity of data types and user persona, and speed of response to change. The essence of the data mesh and data fabric approaches is that it puts the customer first and focuses on outcomes instead of outputs.
In this session, Saptarshi Sengupta, Senior Director of Product Marketing at Denodo, will address key considerations and provide his insights on why some companies are succeeding with these approaches while others are not.
Watch On-Demand and Learn:
- Why a logical approach is necessary and how it aligns with data fabric and data mesh
- How some of the large enterprises are using logical data fabric and data mesh for their data and analytics needs
- Tips to create a good data management modernization roadmap for your organization
Accelerate Self-Service Analytics with Data Virtualization and Visualization
Accelerate Self-Service Analytics with Data Virtualization and VisualizationDenodo Watch full webinar here: https://bit.ly/3fpitC3
Enterprise organizations are shifting to self-service analytics as business users need real-time access to holistic and consistent views of data regardless of its location, source or type for arriving at critical decisions.
Data Virtualization and Data Visualization work together through a universal semantic layer. Learn how they enable self-service data discovery and improve performance of your reports and dashboards.
In this session, you will learn:
- Challenges faced by business users
- How data virtualization enables self-service analytics
- Use case and lessons from customer success
- Overview of the highlight features in Tableau
Accelerate Self-Service Analytics with Data Virtualization and Visualization
Accelerate Self-Service Analytics with Data Virtualization and VisualizationDenodo Watch full webinar here: https://bit.ly/39AhUB7
Enterprise organizations are shifting to self-service analytics as business users need real-time access to holistic and consistent views of data regardless of its location, source or type for arriving at critical decisions.
Data Virtualization and Data Visualization work together through a universal semantic layer. Learn how they enable self-service data discovery and improve performance of your reports and dashboards.
In this session, you will learn:
- Challenges faced by business users
- How data virtualization enables self-service analytics
- Use case and lessons from customer success
- Overview of the highlight features in Tableau
Date warehousing concepts
Date warehousing conceptspcherukumalla The document provides an overview of key concepts in data warehousing and business intelligence, including:
1) It defines data warehousing concepts such as the characteristics of a data warehouse (subject-oriented, integrated, time-variant, non-volatile), grain/granularity, and the differences between OLTP and data warehouse systems.
2) It discusses the evolution of business intelligence and key components of a data warehouse such as the source systems, staging area, presentation area, and access tools.
3) It covers dimensional modeling concepts like star schemas, snowflake schemas, and slowly and rapidly changing dimensions.
Bierschenk Senior Project
Bierschenk Senior ProjectRyan Bierschenk The document discusses the creation of a data warehouse for MIDFLORIDA to help them monitor for compliance with the Bank Secrecy Act. Key points:
1) The data warehouse will utilize Kimball's methodology to build dimensional data marts from transactional data in order to facilitate analysis and reporting on suspicious banking activities.
2) Dimensions like customers, accounts, and products will be slowly changing to track historical changes, while facts tables will aggregate transactions into daily and monthly snapshots.
3) SAP BusinessObjects tools like Data Integrator, Universe Designer, and Desktop Intelligence will be used to extract, transform, load, and report on the data.
4) Historical data will be automatically
Transforming Data Management in the Cloud with the Denodo Platform
Transforming Data Management in the Cloud with the Denodo PlatformDenodo Watch full webinar here: https://buff.ly/3Cgntpf
Data that is increasingly distributed across on-premises and multiple cloud platforms offers both opportunities and challenges for insights-driven business decision making. This is true for any industry and any organization, small or large. While modern data architectures such as data fabric or data mesh offer innovative solutions to extract maximum value from organizational data, in many instances physical replication or consolidation of data makes those solutions ineffective.
In this session Paul Moxon, SVP of Data Architecture and Chief Evangelist at Denodo will present a number of customer examples illustrating how organizations have successfully implemented a logical data architecture.
dw_concepts_2_day_course.ppt
dw_concepts_2_day_course.pptDougSchoemaker The document provides an overview of key data warehousing concepts. It defines a data warehouse as a single, consistent store of data obtained from various sources and made available to users in a format they can understand for business decision making. The document outlines some common questions end users may have that a data warehouse can help answer. It also discusses the differences between online transaction processing (OLTP) systems and data warehouses, including that data warehouses integrate historical data from various sources and are optimized for analysis rather than transactions.
Data Lake Acceleration vs. Data Virtualization - What’s the difference?
Data Lake Acceleration vs. Data Virtualization - What’s the difference?Denodo Watch full webinar here: https://bit.ly/3hgOSwm
Data Lake technologies have been in constant evolution in recent years, with each iteration primising to fix what previous ones failed to accomplish. Several data lake engines are hitting the market with better ingestion, governance, and acceleration capabilities that aim to create the ultimate data repository. But isn't that the promise of a logical architecture with data virtualization too? So, what’s the difference between the two technologies? Are they friends or foes? This session will explore the details.
Datawarehousing & DSS
Datawarehousing & DSSDeepali Raut The document discusses various concepts related to database design and data warehousing. It describes how DBMS minimize problems like data redundancy, isolation, and inconsistency through techniques like normalization, indexing, and using data dictionaries. It then discusses data warehousing concepts like the need for data warehouses, their key characteristics of being subject-oriented, integrated, and time-variant. Common data warehouse architectures and components like the ETL process, OLAP, and decision support systems are also summarized.
Enabling Self-Service Analytics with Logical Data Warehouse
Enabling Self-Service Analytics with Logical Data WarehouseDenodo Watch full webinar here: https://buff.ly/2GNO8PC
What makes data scientists happy? Of course data. They want it fast and flexible, and they want to do it themselves. But most classic data warehouses (DW) and data lakes are not easy to deal with for agile data access. A more practical solution is the logical data warehouse (LDW), which has shown to be a more agile foundation for delivering and transforming data and makes it easy to quickly plug in new data sources.
Attend this webinar to learn:
* How easily new data sources can be made available for analytics and data science
* How your organization can successfully migrate to a flexible LDW architecture in a step-by-step fashion
* How LDWs help integrate self-service analytics with classic forms of business intelligence
Data Warehousing Datamining Concepts
Data Warehousing Datamining Conceptsraulmisir This document provides an overview of a course on data warehousing, data mining, and decision support. It discusses what data warehousing is, how it differs from operational transaction processing systems, and the processes involved like data extraction, transformation, loading and refreshing the warehouse. It also covers warehouse architecture, design considerations, and multidimensional data modeling. Examples from Walmart's data warehouse implementation are provided to illustrate real-world warehouse concepts and capabilities.
Dataware housing
Dataware housingwork The document provides an overview of data warehousing and data mining. It discusses what a data warehouse is, how it is structured, and how it can help organizations make better decisions by integrating data from multiple sources and facilitating online analytical processing (OLAP). It also covers key components of a data warehousing architecture like the data manager, data acquisition, metadata repository, and middleware that connect the data warehouse to operational databases and analytical tools.
13500892 data-warehousing-and-data-mining
13500892 data-warehousing-and-data-miningNgaire Taylor This document provides an overview of a course on data warehousing, decision support, and data mining. It discusses key topics that will be covered, including data warehousing architecture, loading and transforming data, online analytical processing (OLAP), and data mining techniques. The course aims to explain how these tools and techniques can help organizations make better data-driven decisions.
Gulabs Ppt On Data Warehousing And Mining
Gulabs Ppt On Data Warehousing And Mininggulab sharma The document provides an overview of data warehousing, decision support, and OLAP. It discusses how a data warehouse can integrate data from various operational sources to provide a single point of access for analysis. It also compares the differences between operational databases designed for transactions versus data warehouses designed for analytics and decision making. Key points covered include data extraction, transformation and loading into the warehouse, as well as refresh strategies to propagate changes from source systems.
Data warehousing and online analytical processing
Data warehousing and online analytical processingVijayasankariS The document discusses data warehousing and online analytical processing (OLAP). It defines a data warehouse as a subject-oriented, integrated, time-variant and non-volatile collection of data used to support management decision making. It describes key concepts such as data warehouse modeling using data cubes and dimensions, extraction, transformation and loading of data, and common OLAP operations. The document also provides examples of star schemas and how they are used to model data warehouses.
More from Denodo (20)
Enterprise Monitoring and Auditing in Denodo
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 Approach
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 Layer
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?
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 Landscape
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 Lite
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...
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
Drive Data Privacy Regulatory Compliance
Drive Data Privacy Regulatory ComplianceDenodo 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 данных
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 Fragmentation
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 Anything
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!
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 Forward
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...
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...
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?
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 Unions
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 usability
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.
Denodo Partner Connect: Technical Webinar - Architect Associate Certification...
Denodo Partner Connect: Technical Webinar - Architect Associate Certification...Denodo 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 realidades
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
Recently uploaded (20)
Veterinary Anatomy, The Regional Gross Anatomy of Domestic Animals (VetBooks....
Veterinary Anatomy, The Regional Gross Anatomy of Domestic Animals (VetBooks....JazmnAltamirano1 Veterinary Anatomy, The Regional Gross Anatomy of Domestic Animals (VetBooks.ir).pdf
Understanding Large Language Model Hallucinations: Exploring Causes, Detectio...
Understanding Large Language Model Hallucinations: Exploring Causes, Detectio...Tamanna36 This presentation delves into Large Language Model (LLM) hallucinations—incorrect or fabricated outputs that undermine reliability. It covers their causes (e.g., data limitations, transformer architecture), detection methods (like semantic entropy), prevention strategies (fine-tuning, RAG), and ethical concerns (misinformation, bias). The role of tokens and MLOps in managing hallucinations is explored, alongside the feasibility of hallucination-free LLMs. Designed for researchers, developers, and AI enthusiasts, it offers insights and practical approaches to enhance LLM accuracy and trustworthiness in critical applications like healthcare and legal systems.
apidays New York 2025 - From UX to AX by Karin Hendrikse (Netlify)
apidays New York 2025 - From UX to AX by Karin Hendrikse (Netlify)apidays From UX to AX: Designing for an AI Agent World
Karin Hendrikse, Senior Software Engineer at Netlify
apidays New York 2025
API Management for Surfing the Next Innovation Waves: GenAI and Open Banking
Convene 360 Madison, New York
May 14 & 15, 2025
------
Check out our conferences at https://www.apidays.global/
Do you want to sponsor or talk at one of our conferences?
https://apidays.typeform.com/to/ILJeAaV8
Learn more on APIscene, the global media made by the community for the community:
https://www.apiscene.io
Explore the API ecosystem with the API Landscape:
https://apilandscape.apiscene.io/
Glary Utilities Pro 5.157.0.183 Crack + Key Download [Latest]![Glary Utilities Pro 5.157.0.183 Crack + Key Download [Latest]](https://cdn.slidesharecdn.com/ss_thumbnails/artificialintelligence17-250529071922-ef6fe98e-thumbnail.jpg?width=560&fit=bounds)
Glary Utilities Pro 5.157.0.183 Crack + Key Download [Latest]Designer Copy Link & Paste in Google👉👉👉 https://alipc.pro/dl/
Glary Utilities Pro Crack Glary Utilities Pro Crack Free Download is an amazing collection of system tools and utilities to fix, speed up, maintain and protect your PC.
Ethical Frameworks for Trustworthy AI – Opportunities for Researchers in Huma...
Ethical Frameworks for Trustworthy AI – Opportunities for Researchers in Huma...Karim Baïna Artificial Intelligence (AI) is reshaping societies and raising complex ethical, legal, and geopolitical questions. This talk explores the foundations and limits of Trustworthy AI through the lens of global frameworks such as the EU’s HLEG guidelines, UNESCO’s human rights-based approach, OECD recommendations, and NIST’s taxonomy of AI security risks.
We analyze key principles like fairness, transparency, privacy, robustness, and accountability — not only as ideals, but in terms of their practical implementation and tensions. Special attention is given to real-world contexts such as Morocco’s deployment of 4,000 intelligent cameras and the country’s positioning in AI readiness indexes. These examples raise critical issues about surveillance, accountability, and ethical governance in the Global South.
Rather than relying on standardized terms or ethical "checklists", this presentation advocates for a grounded, interdisciplinary, and context-aware approach to responsible AI — one that balances innovation with human rights, and technological ambition with social responsibility.
This rich Trustworthy and Responsible AI frameworks context is a serious opportunity for Human and Social Sciences Researchers : either operate as gatekeepers, reinforcing existing ethical constraints, or become revolutionaries, pioneering new paradigms that redefine how AI interacts with society, knowledge production, and policymaking ?
apidays New York 2025 - To tune or not to tune by Anamitra Dutta Majumdar (In...
apidays New York 2025 - To tune or not to tune by Anamitra Dutta Majumdar (In...apidays To tune or not to tune : Benefits and security pitfalls of fine-tuning
Anamitra Dutta Majumdar, Principal Engineer at Intuit
apidays New York 2025
API Management for Surfing the Next Innovation Waves: GenAI and Open Banking
Convene 360 Madison, New York
May 14 & 15, 2025
------
Check out our conferences at https://www.apidays.global/
Do you want to sponsor or talk at one of our conferences?
https://apidays.typeform.com/to/ILJeAaV8
Learn more on APIscene, the global media made by the community for the community:
https://www.apiscene.io
Explore the API ecosystem with the API Landscape:
https://apilandscape.apiscene.io/
Brain, Bytes & Bias: ML Interview Questions You Can’t Miss!
Brain, Bytes & Bias: ML Interview Questions You Can’t Miss!yashikanigam1 Preparing for a machine learning role? Get ready to tackle real-world problem-solving questions! From regression vs. classification to the ETL process, expect a deep dive into algorithms and data pipelines. Most live courses for professionals and best online professional certificates now include mock interviews and case studies to gear you up. Mastering these ML interview questions not only helps in cracking top tech interviews but also builds your confidence.
At Tutort Academy, we train you with real-time scenarios and curated interview prep for success.
Block chauin techncology by engineer saniya samreen
Block chauin techncology by engineer saniya samreenShoyeb16 Block chauin techncology by engineer saniya samreen
time_series_forecasting_constructor_uni.pptx
time_series_forecasting_constructor_uni.pptxstefanopinto1113 The final presentation of our time series forecasting project for the "Data Science for Society and Business" Master's program at Constructor University Bremen
15 Benefits of Data Analytics in Business Growth.pdf
15 Benefits of Data Analytics in Business Growth.pdfAffinityCore Explore how data analytics boosts business growth with insights that improve decision-making, customer targeting, operations, and long-term profitability.
Geospatial Data_ Unlocking the Power for Smarter Urban Planning.docx
Geospatial Data_ Unlocking the Power for Smarter Urban Planning.docxsofiawilliams5966 Geospatial data refers to information that is associated with a specific location on the Earth’s surface.
apidays New York 2025 - Build for ALL of Your Users by Anthony Lusardi (liblab)
apidays New York 2025 - Build for ALL of Your Users by Anthony Lusardi (liblab)apidays Build for ALL of Your Users
Anthony Lusardi, Developer Relations Engineer at liblab
apidays New York 2025
API Management for Surfing the Next Innovation Waves: GenAI and Open Banking
Convene 360 Madison, New York
May 14 & 15, 2025
------
Check out our conferences at https://www.apidays.global/
Do you want to sponsor or talk at one of our conferences?
https://apidays.typeform.com/to/ILJeAaV8
Learn more on APIscene, the global media made by the community for the community:
https://www.apiscene.io
Explore the API ecosystem with the API Landscape:
https://apilandscape.apiscene.io/
Faces of the Future The Impact of a Data Science Course in Kerala.pdf
Faces of the Future The Impact of a Data Science Course in Kerala.pdfjzyphoenix "Faces of the Future: The Impact of a Data Science Course in Kerala," highlights how data science education is empowering diverse learners across Kerala. It explores course structure, student success stories, career outcomes, and the growing tech ecosystem supporting future data professionals.
Enabling a Data Mesh Architecture and Data Sharing Culture with Denodo
- 3. ▪ ▪ ▪ ▪ ▪
- 4. ▪ ▪ ▪ ▪ ▪ ▪
- 10. ▪ ▪ ▪ ▪ ▪ ▪
- 13. ▪ ▪ ▪ ▪
- 14. ▪ ▪ ▪ ▪ ▪ ▪
- 16. ▪ ▪ ▪ ▪
- 17. ▪ ▪ ▪ ▪ ▪ ▪ ▪
- 18. ▪ ▪ ▪ ▪
- 19. ▪ ▪ ▪ ▪ ▪ ▪ ▪
- 20. ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪
- 21. ▪ ▪ ▪ ▪ ▪ ▪ ▪
- 22. ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪
- 26. Landsbankinn 26 • Leading financial institution in Iceland • 40% Market share Individual Banking • 33% Market share Corporate Banking • Best ESG risk ratings amongst European banks (Sustainalytics 2021) • Best bank at the Icelandic consumer satisfaction ratings (Ánægjuvogin / Stjórnvísi 2021)
- 27. SAS environment Year Zero - Before Data Virtualization 27 ▪ Too many query points ▪ Heterogenous technologies ▪ Complex source systems ▪ Scattered business rules ▪ Semantic layers in BI ▪ Business logics in DB views ▪ Many points of access control ▪ Audit points all over the place ▪ Each system has its own access control KPI DB Source DBs New DWH Old DWH Markets DB Views BO reporting Self-service BI PDF statements MS Office Integration Views Views Views General Reporting KPI Self-Service data Analytics Reports Analytics Server Risk Reporting Monitoring / Audit Business security Business rules Board Other DBs SAP BO Semantic Layer Data Sources Semantic Layer
- 28. Year 1 - The Logical Data Warehouse ▪ Unique point of query ▪ “Need data? LDW has the answer!” ▪ For reporting, analytics, APIs, … ▪ Unique point of truth ▪ Business logic repository ▪ Lineage available ▪ Unique point of access control ▪ Unified access to the data ▪ Unique point of auditing KPI DB Source DBs New DWH Old DWH Markets DB BO reporting Self-service BI MS Office Integration General Reporting KPI Self-Service data Analytics Reports Analytics Server Risk Reporting Board Other DBs Data Sources Logical Data Warehouse w/ Denodo Monitoring / Audit Business security Business rules PDF statements
- 29. Years 2 and 3 - Expansion and Modernization 29 ▪ Addition of data consumers ▪ Tableau ▪ REST / Restful APIs ▪ Addition of more data sources ▪ Where ETL is not required ▪ When history is provided in source ▪ Logical data pipelines ▪ Reduces the number of ETL jobs ▪ EDW gets data from LDW BO Reporting Tableau RestWS to Excel General Reporting KPI Self-Service data Analytics Reports Analytics Server Risk Reporting Board Data Sources Logical Data Warehouse w/ Denodo KPI DB Source DBs New DWH Old DWH Markets DB Other DBs Flat files Excel SaaS REST SOAP WWW Customers Domains Operational systems Monitoring / Audit Business security Business rules Customer statements
- 30. Year 4 - A flawed model 30 ▪ Source data is cryptic ▪ Data comes from software vendors ▪ Lots of meetings needed to establish the data mapping ▪ Domains know their source ▪ How to find data in the source ▪ When source changes ▪ Domains resort to creating views in the source ▪ Loss of lineage and governance ▪ What we wanted to get rid of in the first place LDW Source DBs Domains Operational systems Views
- 31. Year 4 - Implementing a Data Mesh model 31 ▪ A simplified process 1. Domains provided with a development space 2. LDW developers combine views 3. Domains publish data 4. Operational systems access the data ▪ Top-down modelling ▪ Using interface views (data contracts) Source system Base Data Mesh Domain A developer Business systems LDW developer LDW Requests (interface contracts) Shares Combines LDW Source DBs Operational systems Domains Domain B developer Requests (interface contracts) Publication Data Mesh Publishes LDW
- 32. Year 4 - Benefits of the Data Mesh w/ Denodo 32 ▪ Delegate the ownership of data to the domains ▪ Data is in the hands of its creator ▪ Give better overview of the pipeline ▪ Views lifecycle managed by the source developer ▪ Reduce data pipelines ▪ Fewer ETL jobs when available LDW Source DBs Operational systems Domains Savings domain Loans domain Cards domain Claims domain EDW domain LDW developer CRM Loan Online bank
- 33. 33 KPI DB New DWH Markets DB Other DBs Flat files SaaS REST SOAP WWW Source DBs Customers Risk Reporting Business Board ▪ Data Mesh conquers the bank ▪ Self-service for business ▪ Data API Year 5 - What lies ahead Logical Data Warehouse w/ Denodo API Self- Service Self- Service Self- Service Data Mesh Data Mesh Data Mesh Data Mesh Data Mesh Data Mesh Data Mesh Data Mesh Data Mesh Op. Systems Data Mesh