In a world where data is everywhere, the real challenge isn’t collecting it – it’s making it useful. Organizations today need more than just access to data. They need high-quality, reusable, and trusted data products that are easy to discover, govern, and consume. That’s exactly what IBM delivers through Data Product Hub, a key component of IBM watsonx.data intelligence. watsonx.data lakehouse and Data Product Hub work together to transform fragmented data assets into strategic products that power analytics, AI/ML use cases, and business decisions at scale. IBM work together to transform fragmented data assets into strategic products that power analytics, AI/ML use cases, and business decisions at scale.
The backbone: IBM Watsonx.data with Medallion Architecture
While Data Product Hub is where data products live and thrive, IBM Watsonx.data lakehouse is where they’re born and refined. Built on an hybrid, open Lakehouse architecture, IBM Watsonx.data enables you to store and query data across multiple formats and environments – without the complexity of moving it. The offering streamlines the full lifecycle of data for AI, bringing together integration, governance, and management across all data types and environments – from on-premises to hybrid and multi-cloud.An organization can implement medallion architecture, a layered approach that structures data in three tiers:
This architecture helps organizations incrementally improve data quality, so the further up the stack you go, the more valuable and reusable the data becomes.
Turning medallion zones assets into data products
Now here’s where things get exciting. Each layer of the medallion architecture feeds directly into Data Product Hub – giving users a central place to find and use data at the level of quality they need. So, a data . With this visibility into the data product’s maturity, the consumer can make an informed decision about whether to subscribe to it.
And because data products created in Data Product Hub come along with rich metadata like key features, data contract, recommended usage and lineage information, users can understand exactly what they’re using – and trust it. The data contract attached to the data product allows data producers and data consumers to get visibility into the terms of use and service level agreement associated with data product. This enables transparent sharing of data product between the two personas. In addition to this, a data producer can deliver the data product to a data consumer through customizable delivery methods. I have described below the different delivery methods using which a data product can be delivered.
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Direct access to watsonx.data asset: This delivery method allows data consumer to get direct read access of the assets packaged as a data product in Watsonx.data lakehouse. When using this delivery method, data doesn’t move, which leads to enhanced security during data product delivery. The data product can be accessed through Presto leverage to access the data product. Data consumers who want to perform BI analysis using tools like power BI, Tableau etc. can leverage this delivery method to connect the data product to the tool of their choice using JDBC/ODBC.
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Using this delivery method, a data consumer can get the data product delivered as a table in their own lakehouse. This delivery method enable data consumer to pull a snapshot of data that is hosted outside of their lakehouse. This delivery method enhances the implementation of medallion architecture as the consumer can select the zone for data product delivery.
Now that we have understood how Data Product Hub and Watsonx.data Lakehouse work with each other from data product creation to data product delivery, let’s understand reusable data product and the benefits it brings to the customer in the next section.
Why reusable data products are a game changer:
High-quality, reusable data products solve some of the biggest data challenges organizations face today:
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Teams can reuse data products across departments and domains, reducing duplication and boosting consistency. Data product follows a lifecycle, allowing users to create a new version whenever its underlying data assets change
Together, Data Product Hub and Watsonx.data make the Watsonx.data lakehouse self-serve and accessible to both business and technical users – while enabling organizations to manage usage efficiently and giving data consumers the flexibility to access data products via either Presto or Spark engine.
When you combine the medallion architecture of Watsonx.data lakehouse with the curated, business-ready interface of Data Product Hub, you unlock a new level of data usability across the enterprise. This approach transforms how organizations:
In short – less wrangling, more doing. If you’re on a journey to modernize your data strategy, it might be time to stop thinking in tables – and start thinking in data products. To learn more about Data Product Hub or Watsonx.data, access our on-demand webinar here.