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What configurations are needed to pass parameters to a Databricks notebook?

Spark/Big Dataeasy0.3 min read

**Widgets**: `dbutils.widgets.text("param1", "default")`; `value = dbutils.widgets.get("param1")`. In Run Now: Base parameters. For Jobs: task parameters map to widgets. **JSON params**: `dbutils.widgets.text("params", "{}")`; `json.loads(dbutils.widgets.get("params"))` for...

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Frequency
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Asked at 1 company
Category
452
questions in Spark/Big Data
Difficulty Split
88E|81M|283H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Virtusa

Why This Question Matters

This easy-level Spark/Big Data question appears frequently in data engineering interviews at companies like Virtusa. While less common, it tests deeper understanding that distinguishes strong candidates.

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Expert Answer
66 words

Widgets: dbutils.widgets.text("param1", "default"); value = dbutils.widgets.get("param1"). In Run Now: Base parameters. For Jobs: task parameters map to widgets.

JSON params: dbutils.widgets.text("params", "{}"); json.loads(dbutils.widgets.get("params")) for complex config.

Why Parameterize: Same notebook for dev/prod; different dates, paths, env. Reproducibility and reuse.

Scalability Trade-offs: Too many widgets = clutter. Use JSON for >5 params.

Cost Implications: Parameterization enables job reuse; one notebook, many runs. Secret scope for sensitive params.

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