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

Delta Lake outperforms legacy Hadoop formats because it leverages Parquet-based storage , data skipping , and file pruning . According to Databricks documentation, Delta Lake automatically stores detailed statistics (min/max values and file-level metadata) in the transaction log. During query planning, the engine uses these statistics to skip entire files that do not match query filters , a process called data skipping and file pruning . Additionally, Delta uses a vectorized Parquet reader , which reduces I/O and CPU overhead. Together, these optimizations allow Delta to scan significantly less data and produce more efficient physical query plans (e.g., Shuffle Hash Join instead of Sort Merge Join). The performance gain is due to efficient data skipping, not the inherent superiority of join type.

The DELETE operation in Delta Lake is ACID compliant, which means that once the operation is successful, the records are logically removed from the table. However, the underlying files that contained these records may still exist and be accessible via time travel to older versions of the table. To ensure that these records are physically removed and compliance with GDPR is maintained, a VACUUM command should be used to clean up these data files after a certain retention period. The VACUUM command will remove the files from the storage layer, and after this, the records will no longer be accessible.