Starburst requires a heavy investment in infrastructure; it needs heavy computing and storage. If your use case doesn't involve heavy processing or storage, then Starburst might not be the solution. It's more suitable for large ecosystems spread across regions, or companies with a massive employee base. In those cases, Starburst sounds good, but Dremio might be better for smaller organizations. I can't compare them directly, but Starburst definitely needs a good infrastructure investment, good infrastructure management, and skilled personnel. The product is nice, there's no doubt about that. It's very scalable, fast performing, and supports many catalogs and connectors that Dremio doesn't have. Dremio is limited to ten to fifteen connectors, while Starburst supports forty to fifty, so it has a much bigger ecosystem. In that way, Starburst wins. Between Dremio and Starburst, considering the connectors, Starburst gets a nine out of ten.
Mostly, we've worked with static data files, not on AI-driven or streaming data. The modeling team uses the analytics part, where each modeling user can work with a separate command. The tool's feasibility depends on the architecture you're currently working with. If you're dealing with data from multiple sources, like we are, then Starburst Enterprise could be very beneficial. As a first-time user, I recommend going through the documentation and setting up some proof of concept to see if it meets the needs of your projects. These steps can help you determine if the solution is the right tool for you. I rate the overall solution an eight out of ten.
Data Science Platforms empower data analysts to develop, evaluate, and deploy analytical models efficiently. They integrate data exploration, visualization, and predictive modeling in one cohesive environment.These platforms serve as indispensable tools for data-driven decision-making, providing intuitive interfaces and scalable computing power. They enable seamless collaboration between data scientists and business stakeholders, allowing actionable insights to drive strategic initiatives...
Starburst requires a heavy investment in infrastructure; it needs heavy computing and storage. If your use case doesn't involve heavy processing or storage, then Starburst might not be the solution. It's more suitable for large ecosystems spread across regions, or companies with a massive employee base. In those cases, Starburst sounds good, but Dremio might be better for smaller organizations. I can't compare them directly, but Starburst definitely needs a good infrastructure investment, good infrastructure management, and skilled personnel. The product is nice, there's no doubt about that. It's very scalable, fast performing, and supports many catalogs and connectors that Dremio doesn't have. Dremio is limited to ten to fifteen connectors, while Starburst supports forty to fifty, so it has a much bigger ecosystem. In that way, Starburst wins. Between Dremio and Starburst, considering the connectors, Starburst gets a nine out of ten.
Mostly, we've worked with static data files, not on AI-driven or streaming data. The modeling team uses the analytics part, where each modeling user can work with a separate command. The tool's feasibility depends on the architecture you're currently working with. If you're dealing with data from multiple sources, like we are, then Starburst Enterprise could be very beneficial. As a first-time user, I recommend going through the documentation and setting up some proof of concept to see if it meets the needs of your projects. These steps can help you determine if the solution is the right tool for you. I rate the overall solution an eight out of ten.