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Steps to mount storage in Databricks.

Spark/Big Datamedium0.5 min readPremium

**Why Mount vs. URI**: Mounts provide a stable path (`/mnt/data`) and handle auth once; URIs (`s3://bucket/path`) require per-access credentials. For multi-workspace, Unity Catalog external locations replace mounts. **Steps (Azure ADLS)**: (1) Create service principal; grant...

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Why Mount vs. URI: Mounts provide a stable path (/mnt/data) and handle auth once; URIs (s3://bucket/path) require per-access credentials. For multi-workspace, Unity Catalog external locations replace mounts.

Steps (Azure ADLS): (1) Create service principal; grant Storage Blob Data Contributor. (2) Mount: dbutils.fs.mount(source="abfss://container@account.dfs.core.windows.net/", mount_point="/mnt/mydata", extra_configs={...}). (3) For key-based: use fs.azure.account.key.* in extra_configs.

AWS S3: Use IAM instance profile on cluster; s3a:// works without explicit keys. Or: assume role via spark.hadoop.fs.s3a.aws.credentials.provider.

Scalability Trade-offs: Mounts are per-workspace; init script ensures all clusters get them. Too many mounts slow startup.

Cost Implications: Use secret scopes (dbutils.secrets.get) for keys—never in notebooks. Migrate to Unity Catalog external locations for governance.

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