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Where each Databricks environment takes its libraries

How serverless, pipelines, classic jobs and bundles each get their Python libraries.

From Ultra Transcenders DBX-DEP by Tony Rough (coming December 2026)

This table is the core of the chapter. Find the runtime first, and only then choose the mechanism, because several of them work in only one place. Figure 2.1 shows the main routes, including the key a bundle uses for each.

Four columns cover a serverless notebook, a serverless job task, a pipeline and a classic job task, each listing where its libraries come from and the rules that apply. A bundle sits above the last three, using environments and environment_key, environment, and libraries respectively.
Figure 2.1: Where each environment takes its libraries, and the bundle key that reaches it. Classic Environments mode (Beta) appears only in the table.
Environment Declare libraries in Init scripts Python restart JVM libraries
Serverless notebook Environment side pane, %pip, base environment No Apply restarts Python No JAR libraries in notebooks
Serverless job task Job environment spec (environments, environment_key) No Installed before code runs JAR dependencies on JAR tasks
Serverless pipeline Pipeline environment No restartPython() not supported Not supported
Classic pipeline Pipeline environment Yes, not recommended restartPython() not supported Not supported, even by init script
Classic, Cluster libraries mode Compute-scoped libraries, Dependent libraries, %pip Yes, not recommended restartPython() supported Maven and JARs, allowlisted in standard mode
Classic, Environments mode (Beta) Base environment, job environment spec No Apply restarts Python Spark JAR tasks not supported

Job and task settings in bundles

A bundle has two distinct places to declare a job task’s Python dependencies, and which applies depends on the compute.

```yaml

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This note is one section of Ultra Transcenders DBX-DEP: Databricks Data Engineer Professional, an independent study guide that explains every topic the exam covers by technology, with comparison tables, diagrams and the common traps, plus a glossary linked to the Databricks documentation.

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