First of all apache superset does not support builtin driver for databricks , for this we need to install sqlalchemy driver here is the connection string for data bricks and apache superset
databricks+pyhive://token:{token value}@{host url}:443/default
also need to provide the protocol
{"connect_args":{"http_path":"sql/protocolv1/o/xxxxxxxx"}}
2) Select “User setting.”
3)Go to the “Access Tokens” tab. Then, a “Generate New Token” button will appear. Simply click it.
1) In case of the code is located in a different workstation, we must first build a component for it and then integrate it into the module.
2) In case of code is located in the same workstation, we may import and utilise it quickly
1- Reduced costs: You can reduce up to 80% on your cloud bill by using Databricks’ managed clusters. 2- Increased productivity: Databricks help us to build and manage big data pipelines. 3- Increased security: Databricks provide us many of features to help you secure your data/information, there is a role-based access control and encrypted communication.
1- Cluster creation failures: This can happen if you don’t have enough credits or if your subscription doesn’t allow for more clusters.
2- Spark errors: Spark errors throw if you’re using an unsupported version of Spark or if your code is incompatible with the Databricks runtime.
3- Network errors: Network errors can occur if there’s a problem with your network configuration or if you’re trying to access Databricks from an unsupported location.