Denodo TechTalks: Product Deep-Dive Series

Data governance strategies play a crucial role in streamlining workflows, enhancing data quality, and accelerating time-to-insight. 

Join us for an enlightening session featuring Antonio Castelo from Collibra and Raúl Beiroa from Denodo. Together, they will explore how this integration fosters seamless communication and collaboration, streamlines governance processes, enforces policies, and ensures efficient data access. Whether you’re a data architect, data steward, or business intelligence analyst, this webinar offers invaluable insights for optimizing your data ecosystem.

Data governance strategies play a crucial role in streamlining workflows, enhancing data quality, and accelerating time-to-insight. 

Join us for an enlightening session featuring Antonio Castelo from Collibra and Raúl Beiroa from Denodo. Together, they will explore how this integration fosters seamless communication and collaboration, streamlines governance processes, enforces policies, and ensures efficient data access. Whether you’re a data architect, data steward, or business intelligence analyst, this webinar offers invaluable insights for optimizing your data ecosystem.

Data governance strategies play a crucial role in streamlining workflows, enhancing data quality, and accelerating time-to-insight. 

Join us for an enlightening session featuring Antonio Castelo from Collibra and Raúl Beiroa from Denodo. Together, they will explore how this integration fosters seamless communication and collaboration, streamlines governance processes, enforces policies, and ensures efficient data access. Whether you’re a data architect, data steward, or business intelligence analyst, this webinar offers invaluable insights for optimizing your data ecosystem.

When a new version of enterprise software is released, it is not a matter of just getting the new software. Any organization needs to be ready for it from multiple other aspects such as infrastructure, team, and related costs. This requires planning and interactions between the different stakeholders months in advance.

When a new version of enterprise software is released, it is not a matter of just getting the new software. Any organization needs to be ready for it from multiple other aspects such as infrastructure, team, and related costs. This requires planning and interactions between the different stakeholders months in advance.

When a new version of enterprise software is released, it is not a matter of just getting the new software. Any organization needs to be ready for it from multiple other aspects such as infrastructure, team, and related costs. This requires planning and interactions between the different stakeholders months in advance.

Data quality (DQ) is ensuring that data is fit for the purpose it is used. Poor DQ may come from human errors, technical conversion errors or inappropriate usage of data. Data quality initiatives aim to improve DQ within an enterprise to support the business. But both experience as well as science show that there is something wrong with how enterprises run data quality initiatives.

Data quality (DQ) is ensuring that data is fit for the purpose it is used. Poor DQ may come from human errors, technical conversion errors or inappropriate usage of data. Data quality initiatives aim to improve DQ within an enterprise to support the business. But both experience as well as science show that there is something wrong with how enterprises run data quality initiatives.

Data quality (DQ) is ensuring that data is fit for the purpose it is used. Poor DQ may come from human errors, technical conversion errors or inappropriate usage of data. Data quality initiatives aim to improve DQ within an enterprise to support the business. But both experience as well as science show that there is something wrong with how enterprises run data quality initiatives.

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? 

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? 

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.

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.

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.

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.

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In Data Services and Data Mesh Projects, decoupling the work of the Developer from that of the Data Architect or data products owner helps in speeding up the overall delivery of the project. It can be achieved by using Top-Down design. Do you want to follow Top-Down design in the Data Services or Data Mesh projects? Do you want to transform an existing Data Model into a Data Service Model easily? In the context of the Data Mesh project, do you want to make several teams create data products independently?

Real-time monitoring is the delivery of continuously updated data about systems, processes or events. Such monitoring enables quick detection of anomalies, performance issues and critical events. In addition to that, the other concern in a multi-user environment is to achieve a balance in the workload by distributing the available resources. In this session, we will learn about how Denodo addresses both these topics.

Over the years, customers have implemented BI solutions using SAP Business Objects that span from departmental solutions to enterprise-wide deployments. For many customers, these BI solutions have been and are critical to the operations and management of the business. The users, through IT support, have become accustomed to ad-hoc reporting using Universes along tools like Web Intelligence and Crystal Reports. Based on recent news from SAP, these customers will now have to look for a alternate architectures that does not include traditional SAP Business Objects.

Ensuring consistent, accurate, and complete data is essential for any organization that relies on data to drive business decisions. SQL Stored Procedures are a powerful tool that allow to bundle multiple operations in a single unit for code reusability and have easier maintenance while also providing other features such as transaction management and exception handling.

Building reusable, trusted and consumable Data products is the new way the best data teams are delivering high-quality data, quicker and easier than ever before…

What is a Data Product? It's a reusable data asset, built to deliver a trusted dataset, for a specific purpose.

Teams that use data products spend less time searching for data, ensuring data quality, or building new data pipelines, and those time savings become significant when added up across your data ecosystem and lifecycle.

In today's fast-paced, data-driven world, organizations need real-time data pipelines and streaming applications to make informed decisions. Apache Kafka, a distributed streaming platform, provides a powerful solution for building such applications and, at the same time, gives the ability to scale without downtime and to work with high volumes of data. At the heart of Apache Kafka lies Kafka Topics, which enable communication between clients and brokers in the Kafka cluster.

Having a clear understanding of the roles needed to manage a Modern Data Architecture would be critical for ensuring that your organization can meet its business goals. Who needs to be involved? What are the roles and responsibilities of each team member? What are the tools that they need to use?

Join us for this session with María Sousa, Technical Enablement Director at Denodo, to get guidance and best practices on how to structure these roles within your organization to ensure the most effective use of Denodo and have a starting point to implement your Operational Model.

Can you follow agile methodologies in Denodo development? What are the activities of the different teams in a sprint? What are the best ways to manage your development, testing, and promotion?

Join us for this session with Princess Jamelyn Ramos, Technical Consultant at Denodo, to get insights on how to plan and manage your sprint activities in Denodo development with recommended best practices.

Watch On-Demand and Learn:

• Sprint timeline and activities
• Suggested automation for continuous integration
• Promotion process and continuous delivery

With the appearance of cloud object storage services like AWS S3 or Azure ADLS, the data lake has seen an upturn in usage as some of the challenges of the original idea were addressed. However, companies across the globe still find it challenging to adopt data lakes into the corporate data ecosystem. While almost infinite in storage, data retrieval from these sources and integration of the data with the corporate ecosystem is still an arduous task for data engineers.

In recent years, there has been a significant push towards decentralized data organizations where different domains are partially or fully responsible for exposing their own data for analytics.

Join us in this session with Daniel Tenreiro, Sales Engineer at Denodo, in which he will share important design guidelines and best practices that can be used to implement many of the decentralization principles, such as the ones defined by the popular data mesh paradigm, using the Denodo Platform, powered by data virtualization.

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Securitizing data is one of the most important tasks in an organization. Denodo can offer a wide range of data, gathering data from different sources, and delivering this data to many client applications and final users. When returning the data, Denodo provides several options to securitize the data, and just let the right users and client applications read this data. One of the options for achieving this purpose is using fine-grained privileges over the view.

Security of data in an organization is becoming more and more important. With a plethora of tools for storing and accessing many different data sets, securing an organization's data assets can quickly become a daunting task. Not only will this be hard on database administrators and data engineers, but it could also lead to mistakes and in the worst case data breaches.

Are you looking to define a development strategy for your organization based on the best practices? Thinking about how to establish a structure for your project? Are you modeling the metadata in the right way?

Join Varun Prasan Keshar (Data Engineer, Denodo) to get directions on how different development strategies can be implemented with Denodo along with the Best Practices.

This session would revolve around the following topics:

• Organizing Projects with Denodo
• Structuring the Metadata
• View Development

What is the best way to manage metadata promotion? How do organizations seamlessly promote the metadata, users, and their permissions? How can administrators guarantee the stability of the service in the event of failure?

Join us for this session with María Sordo, Technical Account Manager, Denodo, and get key insights on how to promote the metadata using Solution Manager and walk away with best practices and practical tips.

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Data Scientists are often in need of data from many datasets to get proper insights for their AI / ML needs. Even if they figure out the location of their datasets, accessing, transforming and having the ability to store in a single place for frequent access is often difficult and time intensive. With Denodo Platform, you can easily apply the transformations and then make it available for your end users. Additionally, you can also automate this process using our Scheduler jobs.

Watch on-demand & Learn how Denodo helps you:

Self-service Analytics BI is often quoted by many - ie, allow users to discover and access data without having to ask IT to create a data mart, or by allowing users to directly export/copy the data from the data sources themselves into their analytics tools and systems.

Companies with corporate data lakes also need a strategy for how to best integrate them with their overall data fabric. To take full advantage of a data lake, data architects must determine what data belongs in the Lake vs. other sources, how end users are going to find and connect to the data they need as well as the best way to leverage the processing power of the data lake. This webinar will provide you with a deep dive look at how the Denodo Platform for data virtualization enables companies to maximize their investment in their corporate data lake.

Achieving the goals of real time analytics in today's increasingly complex enterprise data environments poses numerous challenges. Most organizations today require efficient access to integrated data assets across a wide variety of on-prem and cloud data sources.

Many businesses are moving to the Cloud. This process can take many years with data spanning On-Prem and Cloud. When Denodo needs to be deployed in a Hybrid Cloud Architecture, how should one implement that?

Join this session to get a deep dive look at how to create a shared Virtual Database that exposes a consistent Semantic Model using Denodo’s Interfaces. Both On Prem and Cloud will have their own Virtual Databases.

Attend this webinar to learn:

In Gartner's 2020 research paper: Demystifying the Data Fabric, Gartner discusses that the core of the data fabric is able to consolidate many diverse data sources in an efficient manner by allowing trusted data to be delivered from all relevant data sources to all relevant data consumers through one common layer.

Change is the only constant and it is very important for enterprises to keep up with the changing times in an agile fashion. To ensure faster time to market, quick business insights and rapid data driven decision making, it is important that the Data Delivery channel is optimized in the best way possible.

Data Scientists spend most of their time looking for the right data and massaging it into a usable format. How can the Denodo platform enable the data scientist and data engineer?