Are You Ready for the Wave of Opportunities Created by Data Virtualization?

Businesses across industries are inundated with customer and operational data, and data teams are burdened with a growing number of data access requests from technical and non-technical data consumers. As cloud data lakes grow, the challenge for many organizations will be providing access to that data for exploratory BI and interactive analytics. In this webinar, learn how the Dremio Open Data Lakehouse provides direct access to data in Azure Data Lake Storage, and how Power BI and Dremio work together to democratize analytics for a wide range of data consumers.

Master Data Management (MDM) can provide significant value to the organization in creating consistent key data assets such as Customer, Product, Supplier, Patient, and the list goes on. But getting MDM “right” requires a strategic mix of Data Architecture, business process, and Data Governance. Join this webinar to learn how to find the “sweet spot” between technology, design, process, and people for your MDM initiative.

As enterprises become more agile, centralization appears more and more a thing of the past waterfall world. This need is equally true with data platforms. This paradigm drove us to build a Data Mesh, this next generation of data platforms for PayPal Credit. This talk will detail quickly go over the evolution of data platforms, highlight the problems of current data platforms, and explain why we decided to build a Data Mesh. I will detail the four principles of the Data Mesh, how we got started, and describe some current and future challenges and how we plan to solve them. Part of the Expert Hour series.

Search capability is ingrained into our daily life. How many arguments these days are settled with the conclusion, “Just Google it”??! We all expect some type of search functionality in every application and website. Meanwhile, advances in computer vision, natural language processing, large language models, and generative AI have made it possible to extract semantic properties from unstructured data in the form of vector embeddings. It can be daunting to query this kind of data, which combines K-Nearest Neighbors algorithms and lexical search, unless you have the right tool for the job. When you fail, performance suffers. Web users typically expect search results under one second.