Databricks Databricks-Certified-Professional-Data-Engineer Exam With Confidence Using Practice Dumps

"Can Manage" privileges on the required cluster

Workspace Admin privileges, cluster creation allowed. "Can Attach To" privileges on the required cluster

Cluster creation allowed. "Can Attach To" privileges on the required cluster

"Can Restart" privileges on the required cluster

Cluster creation allowed. "Can Restart" privileges on the required cluster

Set spark.sql.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, and then write to parquet.

Set spark.sql.shuffle.partitions to 2,048 partitions (1TB*1024*1024/512), ingest the data, execute the narrow transformations, optimize the data by sorting it (which automatically repartitions the data), and then write to parquet.

Set spark.sql.adaptive.advisoryPartitionSizeInBytes to 512 MB bytes, ingest the data, execute the narrow transformations, coalesce to 2,048 partitions (1TB*1024*1024/512), and then write to parquet.

Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB* 1024*1024/512), and then write to parquet.

Set spark.sql.shuffle.partitions to 512, ingest the data, execute the narrow transformations, and then write to parquet.

Because access to production data will always be verified using passthrough credentials it is safe to mount data to any Databricks development environment.

All developer, testing and production code and data should exist in a single unified workspace; creating separate environments for testing and development further reduces risks.

In environments where interactive code will be executed, production data should only be accessible with read permissions; creating isolated databases for each environment further reduces risks.

Because delta Lake versions all data and supports time travel, it is not possible for user error or malicious actors to permanently delete production data, as such it is generally safe to mount production data anywhere.