Which distribution does Databricks support for installing custom Python code packages?

Which distribution does Databricks support for installing custom Python code packages?

• A. sbt
• B. npm
• C. Wheels
• D. jars

Answer: (C)

Databricks supports the installation of custom Python code packages primarily through the use of Wheels. Wheel is a built-package format for Python that provides an efficient way to package and distribute Python libraries. It is designed to facilitate easier installation, as it allows for faster installation compared to source distributions by eliminating the need for building from source. When you use Wheels, you can include pre-compiled code, which is particularly beneficial in environments like Databricks where execution speed and convenience are critical. Additionally, Wheel archives can also contain metadata that helps in handling package dependencies effectively. This makes them a preferred choice for packaging and deploying Python libraries in various environments, including Databricks clusters. The other options like sbt, npm, and jars pertain to different ecosystems—sbt is primarily used for Scala and Java projects, npm is related to Node.js for Javascript packages, and jars are specific to Java environments. They do not relate to the installation of Python packages in Databricks.

Databricks supports the installation of custom Python code packages primarily through the use of Wheels. Wheel is a built-package format for Python that provides an efficient way to package and distribute Python libraries. It is designed to facilitate easier installation, as it allows for faster installation compared to source distributions by eliminating the need for building from source.

When you use Wheels, you can include pre-compiled code, which is particularly beneficial in environments like Databricks where execution speed and convenience are critical. Additionally, Wheel archives can also contain metadata that helps in handling package dependencies effectively. This makes them a preferred choice for packaging and deploying Python libraries in various environments, including Databricks clusters.

The other options like sbt, npm, and jars pertain to different ecosystems—sbt is primarily used for Scala and Java projects, npm is related to Node.js for Javascript packages, and jars are specific to Java environments. They do not relate to the installation of Python packages in Databricks.