Podcast: Deciphering Data Architectures with James Serra
Learn about cutting-edge developments in AI and data science from the experts who know them best on ODSC’s Ai X Podcast. Each week we release an interview with a leading expert, core contributor, experienced practitioner, or acclaimed instructor who is helping to shape the future of the AI industry through their work or research.
In this episode, James Serra, author of “Deciphering Data Architectures: Choosing Between a Modern Data Warehouse, Data Fabric, Data Lakehouse, and Data Mesh” joins us to discuss his book and dive into the current state and possible future of data architectures.
James has been at the forefront of data architecture at Microsoft for the better part of the last nine years. Known for his thought leadership in the realm of Big Data and advanced analytics, James specializes in contemporary data architectures including the modern data warehouse, data lakehouse, data fabric, and data mesh. Beyond his technical achievements, James is a sought-after speaker and is a prolific voice in the data community through his blog, JamesSerra.com.
James Serra discusses data lakehouses, which merge data lakes and data warehouses. It lets you store a wide variety of data in a cost-effective way, like a data lake. But it also provides tools and structures to manage and analyze that data, similar to a data warehouse. This makes it a good fit for organizations that want to do both data exploration (looking for trends in all their data) and data analytics (answering specific questions).
In the interview, we discuss how James Serra’s experience as a data architect at Microsoft and background in big data and data warehousing influence the insights shared. He also details data fabric, data mesh, and other data architecture terms.
Start listening now to fully explore data architectures, and don’t forget to subscribe to ODSC’s Ai X Podcast to ensure you never miss an episode. Finally, like what you hear? Leave a review or share it with a friend! You can listen on Spotify, Apple, and SoundCloud.
Originally posted on OpenDataScience.com
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