The keynote sessions for today included the Data-specific “Why a Data Lakehouse is the Best Approach for Scaling AI Workloads, Anywhere”, presented Paul Zikopoulos, Vice President, IBM Skills Vitality and Enablement, and Tarun Chopra, Vice President of Product Management for IBM Data and AI.
Paul explained the differences between data lakes and data warehouses, and described that a data lakehouse would be the hypothetical result of a tinder-like union of a data lake and a data warehouse. Data lakehouses combine the benefits of both and help resolve the problems hindering the ability of organizations to put analytics and AI to work at scale, and introduced us to watsonsx.data.
Paul discussed the data strategy that Brandon Beals, Director of Data & AI, Dot Foods, Inc., and his team are employing in his organization.
For the next part of the keynote session, Tarun then dove into IBM watsonx.data, a fit-for-purpose data store to scale AI workloads, and how leading companies are deploying it within their data strategy. Watsonx.data is an open, hybrid, and governed data store optimized to scale all data, analytics and AI workloads. It can be used for all your data, no matter where it resides, across hybrid-cloud through a single point of entry.
As watsonx.data is built on an open data lakehouse architecture, it is the only solution that gives you full flexibility, thanks to its open source, open standards, and interoperability, paired with IBM and third-party data science, BI, and data integration tools.
Tarun demoed the watson.x capability, and then invited Murali Madhangapoal, Senior Software Solution Architect from Intel, and Dan Gallivan, Director of Strategic Alliances at AWS, to talk about the challenges they are facing and how they are using this new architectural approach to address them.
My brief recap doesn’t do justice to the full presentation, and I encourage you to check out the replay of this keynote session that will be posted soon on our IBM TechXchange Conference 2023 website.
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