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Narrator: \[Upbeat tech music intro\] Data has never been more accessible.

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Narrator: Yet turning it into something the business and AI can trust and use

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Narrator: remains a challenge.

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Narrator: Data products help organizations deliver trusted, reusable data for

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Narrator: business and technical teams alike.

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Narrator: As organizations push to enable data self-service and scale

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Narrator: AI, they need a consistent way to deliver data with clear

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Narrator: meaning, context, and governance.

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Narrator: That is where data products play a critical role.

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Narrator: Without a unified approach, however, users and AI agents often

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Narrator: face fragmented access, inconsistent definitions, and limited

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Narrator: governance.

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Narrator: Denodo addresses this with a unified semantic layer, centralized

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Narrator: governance, and real-time access to distributed data.

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Narrator: In this video, we walk through the data product lifecycle from the

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Narrator: perspectives of three key roles: the business user, the data

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Narrator: product owner, and the developer, showing how Denodo simplifies

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Narrator: each stage to accelerate time to value.

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Eliana Nordstrom: \[Screen showing Denodo Data Marketplace\] I'm Eliana Nordstrom,  
a marketing analytics manager.

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Eliana Nordstrom: My company's online course portal needs a more engaging user experience  
to boost enrollment and guide users to the right courses.

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Eliana Nordstrom: To answer this, I turn to the Denodo Data Marketplace.

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Eliana Nordstrom: I search for a certified data product that could provide the insight I need,

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Eliana Nordstrom: specifically information on user web events.

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Eliana Nordstrom: To find it, I chat directly with the Denodo assistant, and

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Eliana Nordstrom: I quickly locate a promising data product called Customer Web Events.

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Eliana Nordstrom: From here, I explore its definition, the properties it contains,

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Eliana Nordstrom: how it's been built, and where the data comes from.

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Eliana Nordstrom: Now, I preview the data itself to get a sense of its content.

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Eliana Nordstrom: Everything looks useful, but I notice two key gaps for my analysis.

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Eliana Nordstrom: First, there's no customer profiling information,

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Eliana Nordstrom: details that could help me segment users and understand behavior patterns.

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Eliana Nordstrom: Second, the dataset only covers the past six months.

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Eliana Nordstrom: For the analysis I'm planning, I need a longer historical view,

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Eliana Nordstrom: ideally the past two years.

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Eliana Nordstrom: Recognizing these limitations, I submit a change request in the marketplace.

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Eliana Nordstrom: I specify the two enhancements I need. First, extend the data

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Eliana Nordstrom: coverage period from six months to two years.

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Eliana Nordstrom: Second, add customer profiling fields to enrich the dataset.

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Eliana Nordstrom: With the request submitted, the process is set in motion to refine this data

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Eliana Nordstrom: product so it can deliver the insights I need to improve the course

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Eliana Nordstrom: selection experience for the users.

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Alexis Cheng: \[Notification alert\] Hi, this is Alexis Cheng, the data product owner  
responsible for customer web events.

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Alexis Cheng: I receive an email notification and head to the Denodo Data Marketplace

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Alexis Cheng: to review the request from Eliana.

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Alexis Cheng: I examine the details and mark the ticket as in progress.

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Alexis Cheng: I then send Eliana a quick message letting her know I'm starting work.

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Alexis Cheng: The next step is to make these changes in the Denodo Design Studio,

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Alexis Cheng: where all data product models are built and maintained.

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Alexis Cheng: I begin by creating a new workspace. This is a dedicated feature branch  
connected to Git.

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Alexis Cheng: This branch enables my team to develop and test changes in isolation

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Alexis Cheng: without affecting other developments.

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Alexis Cheng: With the workspace ready, I switch into it and open the customer web events

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Alexis Cheng: data product definition.

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Alexis Cheng: Here, I add the new fields needed to include the customer profiling

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Alexis Cheng: Eliana is requesting.

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Alexis Cheng: I leverage AI capabilities to generate clear, consistent descriptions for

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Alexis Cheng: these fields and to automatically classify the data.

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Alexis Cheng: \[UI showing automated field classification\] During classification, the system

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Alexis Cheng: detects a field containing email information and applies a PII email tag.

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Alexis Cheng: This tag is linked to a global security policy, which ensures that sensitive

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Alexis Cheng: information will be masked for any roles that should not see it.

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Alexis Cheng: I quickly navigate to the policy to confirm the masking rules,

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Alexis Cheng: reinforcing the product's compliance with governance standards.

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Alexis Cheng: With the updated definition complete, essentially the data contract for the

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Alexis Cheng: published view, I commit the changes to the Git repository and push them

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Alexis Cheng: to the feature branch.

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Alexis Cheng: The modifications are now ready for the next phase of development, where they'll

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Alexis Cheng: be implemented and tested before moving into production.

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Suresh: With the updated definition in place, the changes now move to me, Suresh.

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Suresh: As the data product developer, I will implement them in the workspace

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Suresh: Alexis created.

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Suresh: The historical web events data is stored in an S3 bucket formatted as an Iceberg file.

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Suresh: I therefore leverage the Denodo Lakehouse Accelerator.

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Suresh: With it, I can explore and onboard the data assets straight from the S3 path.

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Suresh: The Lakehouse Accelerator enables me to query, manipulate, and integrate any

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Suresh: dataset in the lake just like any other source.

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Suresh: Once completed, the Iceberg data becomes a base view in the virtual model.

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Suresh: I then create a derived view on top of it, adding semantic clarity, renaming it

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Suresh: Web Events Historical to reflect its contents.

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Suresh: Using Denodo's GenAI integration, I quickly generate descriptive field names

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Suresh: and metadata. This accelerates documentation and ensures the dataset is

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Suresh: business-friendly for future users.

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Suresh: With the historical data ready, I move to combine it with the existing current

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Suresh: web events dataset, which is stored in BigQuery.

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Suresh: Using a graphical union view, I simply drag and drop both datasets together.

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Suresh: Denodo automatically aligns the structures, and the new integrated view now

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Suresh: contains both historical and current event data, seamlessly blending in

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Suresh: real-time sources from the data lake and the cloud warehouse.

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Suresh: The next step is to add customer profiling data, which resides in a Postgres

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Suresh: database in the marketing domain.

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Suresh: This source has not yet been connected to Denodo, so I set up the new connection

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Suresh: using credentials stored in our corporate credentials vault.

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Suresh: Then describe source location and assign it to the correct folder in the

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Suresh: metadata tree.

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Suresh: And finally, introspect the Postgres catalog to bring the relevant asset,

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Suresh: which is, in this case, customer profile.

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Suresh: I bring the profile structure into the virtual model and again use AI to

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Suresh: standardize and enhance the field names for clarity.

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Suresh: With both datasets, web events and customer profiles now available, I create

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Suresh: a join view. GenAI assists by automatically determining the correct join

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Suresh: condition between the datasets.

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Suresh: The resulting customer web events integrated view combines the user behavior data

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Suresh: with rich profile attributes, meeting the requirements from Eliana's original

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Suresh: request and as defined by Alexis in the interface view or data model contract.

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Suresh: Satisfied that the new definition meets all criteria, I replace the old

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Suresh: implementation with this updated version.

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Suresh: The status of the data product changes from a warning state to fully implemented.

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Suresh: With the development work complete, I commit and push the changes to the

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Suresh: feature branch in Git.

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Suresh: The final step is a merge request to bring these changes from the feature branch

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Suresh: into the main branch, ensuring the updated data product is ready for

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Suresh: deployment to production.

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Alexis Cheng: \[Screen transitions to Solution Manager\] Hi, this is Alexis again. With the

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Alexis Cheng: new data product successfully implemented in the development environment,

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Alexis Cheng: the final step is to promote it into production, making it available for the

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Alexis Cheng: business to use.

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Alexis Cheng: Using the Denodo Solution Manager, I will deploy the updated customer web

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Alexis Cheng: events data product into the production environment.

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Alexis Cheng: I begin by creating a new revision. This revision is a complete package

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Alexis Cheng: of the changes made, containing all the VQL code and definitions that

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Alexis Cheng: make up the updated product.

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Alexis Cheng: I select the full data product with all its dependencies automatically included,

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Alexis Cheng: and assign the revision a descriptive name.

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Alexis Cheng: Once the revision is saved, it appears in the list of available revisions.

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Alexis Cheng: I validate it against the production environment to confirm everything is ready.

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Alexis Cheng: The checks pass, so I proceed with the deployment. With a simple click, the

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Alexis Cheng: revision is deployed into production.

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Alexis Cheng: Behind the scenes, Solution Manager handles all the details, moving the code

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Alexis Cheng: into the production Denodo cluster.

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Alexis Cheng: I can monitor the progress from the deployments menu, watching as the status

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Alexis Cheng: changes from deploying to completed.

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Alexis Cheng: Now the updated customer web events product is live and ready for consumption.

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Alexis Cheng: The last step for me is to notify the business that the request has been

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Alexis Cheng: fulfilled. So first, I mark the ticket as completed.

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Alexis Cheng: And then send Eliana a confirmation message letting her know the enhanced

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Alexis Cheng: data product is now available for her to use.

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Eliana Nordstrom: I am back in the Denodo Data Marketplace to check on the request I submitted

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Eliana Nordstrom: earlier. I can see that the ticket has been completed and the enhancements

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Eliana Nordstrom: I asked for are now in place.

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Eliana Nordstrom: I open the updated customer web events data product to confirm everything

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Eliana Nordstrom: meets my requirements. Right away, I notice the new customer profiling

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Eliana Nordstrom: fields I had requested have been added.

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Eliana Nordstrom: Sampling the data, I also see that some sensitive information is masked

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Eliana Nordstrom: to me. Exploring the data lineage, I spot the changes behind the scenes.

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Eliana Nordstrom: Current event data from BigQuery, historical data from the S3 data lake,

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Eliana Nordstrom: and the newly added profile data are all integrated into a single product.

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Eliana Nordstrom: When querying, this data is coming in real time from these data sources with

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Eliana Nordstrom: zero copy. I check the coverage period and confirm that the dataset now

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Eliana Nordstrom: spans the past two to three years.

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Eliana Nordstrom: With the updated product ready, I use the option to open it directly in

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Eliana Nordstrom: Power BI. From here, I can start building the reports and dashboards that

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Eliana Nordstrom: will help me uncover the insights I need.

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Narrator: Data products are not only valuable for analytics, but also crucial for scaling

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Narrator: agentic AI. However, as agents are built in isolation, they tend to proliferate

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Narrator: without coordination, resulting in fragmented access and disjointed semantics.

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Narrator: Denodo addresses this by offering live, unified, and governed data access

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Narrator: through a zero copy approach. By providing data as reusable governed data

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Narrator: products, Denodo separates AI builders from underlying data complexities.

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Narrator: AI scales effectively when intelligent applications can leverage trusted data

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Narrator: products built on a unified semantic foundation. Contact Denodo to explore

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Narrator: the right approach for your organization. \[Upbeat music fades out\]



 

  

Data fragmentation is the enemy of agility. To scale AI and self-service analytics, organizations need more than just "access"—they need a repeatable framework.

This demo shows the **Denodo** approach to efficient, governed [**Data Product**](https://www.denodo.com/en/solutions/by-technology/data-products) delivery.

In this deep-dive walkthrough, we showcase how the Denodo Platform serves as the foundational engine for your **Data Products**. We demonstrate how to move from a business requirement to a fully implemented, PII-masked, and "zero-copy" data asset.

**Key Highlights of the Denodo Data Product Demo:**

- **Entity Mapping:** See how Denodo maps disparate sources (S3, BigQuery, Postgres) into a single, logical **Data Product**.
- **AI-Enhanced Metadata:** Observe how GenAI within Denodo automates the documentation of your data assets.
- **Secure Delivery:** Witness automated PII masking and global security policies applied to the product level.



 

 

 

  

### Role

 AI DeveloperArchitectBusiness UserDeveloper 



### Solutions

- AI
- Data Governance
- Data Products





 

 

 



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