As enterprises race to harness the power of Generative AI (Gen AI), they often overlook the key factor; data readiness. While the promise of AI-driven insights, automation, and innovation is attractive, the quality of these outcomes depends on how well-prepared your...
Choosing an Iceberg catalog: a beginner’s journey 🧊💥🔧
Icebergs reduce our lock-in, with a whole new world of flexibility Iceberg tables offer a lot of the great features we know and love in Snowflake and Databricks, with the added benefit of (ostensible) vendor-agnosticism and therefore reduced lock-in. Good news! Every...
Comparing AzureML MLOps v2 Accelerator and Azure Databricks MLOps Stacks Accelerator 🚀
Machine Learning Operations (MLOps) is a set of practices that combines Machine Learning (ML), DevOps, and Data Engineering to automate the end-to-end machine learning lifecycle. In this blog post, we will compare two popular MLOps accelerators: AzureML MLOps v2 and...
Raiders of the Last Data Silos: Unleashing the Power of Databricks Lakehouse Federation 🚀
Photo by Intricate Explorer on Unsplash In the vast expanse of the data universe, organizations are on a constant quest to conquer their data assets. Much like the intrepid Indiana Jones (other explorers are available), they face the daunting task of navigating the...
Why Databricks Delta Sharing is the Future of Data Sharing
Data sharing is a powerful way to collaborate with your customers, partners, and suppliers on data-driven insights and solutions. However, traditional data sharing methods often involve copying, moving, or replicating data across different platforms, which can be...
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