Curiosity about Databricks is growing. If you’re one of those folks, we can shed some light on a few of the top reasons organizations are considering if Databricks as part of their tech stack.
🚀 Large-Scale Data Workloads
Features such as parallel processing, unified streaming and batch workloads on one platform, and automatic optimization of compute resources are just a few of the ways Databricks is built to handle complex workloads without letting costs spiral out of control.
🤖 Bootstrapping & Scaling AI/ML AI projects often stall at the prototype stage due to the complexity and high expense of modeling, testing, and running ML jobs. Databricks offers a single platform to take AI from concept to full deployment, with AI templates that simplify identifying and executing AI use cases.
🛡️ Easing Data Governance The struggle to promote data access while maintaining compliance and security is real. Databricks' integrated Unity Catalog governs structured and unstructured data across platforms and multiple data environments, making data governance activities much more manageable.
☝️ Single Platform Databricks is a unified platform that has the ability to support the entire data lifecycle, not just data ingestions and transformation — optimal for companies looking to get away from a modular approach to their data stack.
