Databricks Certified Data Engineer - Associate

Explanation

Z-Ordering is the correct optimization technique for this scenario because:

1. Problem Analysis: The team has already tuned file sizes, but rows meeting the condition are "sparsely located throughout each of the data files." This means the data is not well-clustered by the query predicate.

2. How Z-Ordering Helps:

   • Z-Ordering (also known as multidimensional clustering) physically co-locates rows with similar values across multiple columns in the same data files.
   • When rows with similar values for the query condition are stored together, Delta Lake can skip entire data files that don't contain relevant data.
   • This reduces the amount of data that needs to be read and processed.

3. Why Other Options Are Incorrect:

   • A. Data skipping: While related, data skipping is a benefit that comes from Z-Ordering, not a technique itself. Data skipping uses statistics to skip files, but it's more effective when data is well-clustered.
   • C. Bin-packing: This optimizes file sizes by combining small files, but the team already tuned file sizes.
   • D. Write as a Parquet file: Delta tables already use Parquet format internally; this wouldn't help with the sparsity issue.
   • E. Tuning the file size: The team already did this, so it's not a new optimization.

4. Practical Implementation:

   OPTIMIZE table_name
   ZORDER BY (column_used_in_condition);
   

   OPTIMIZE table_name
   ZORDER BY (column_used_in_condition);
   

   This command would physically reorganize the data to cluster rows with similar values for the specified column, making future queries on that column much faster.