A great experience that combines ML-Runtimes - MLFlow and Spark. The ability to use Python, and SQL seamlessly in one platform. Since databricks notebooks can be saved as python scripts in the background it is amazing to have both notebook and script...
Trying to automate the infrastructure is difficult, and some things are impossible to automate as they are only available on the web console and not through APIs. Their support is beyond bad. We had to give up working with them as it wasted too much...
Firebolt makes it easy to index and query semi-structured JSON data, as well as import database snapshots, which allows us to aggregate data from many sources to answer questions, generate reports, and explore data.
The distinct architecture and querying language of Firebolt may necessitate users to acquire new skills and modify their workflows, which could impede the adoption process and act as an obstacle to some organizations.
A great experience that combines ML-Runtimes - MLFlow and Spark. The ability to use Python, and SQL seamlessly in one platform. Since databricks notebooks can be saved as python scripts in the background it is amazing to have both notebook and script...
Firebolt makes it easy to index and query semi-structured JSON data, as well as import database snapshots, which allows us to aggregate data from many sources to answer questions, generate reports, and explore data.
Trying to automate the infrastructure is difficult, and some things are impossible to automate as they are only available on the web console and not through APIs. Their support is beyond bad. We had to give up working with them as it wasted too much...
The distinct architecture and querying language of Firebolt may necessitate users to acquire new skills and modify their workflows, which could impede the adoption process and act as an obstacle to some organizations.