What method can help ensure efficient data filtering in a Delta Lake table partitioned by date?

Utilizing statistics from the Delta Log is a highly effective method for ensuring efficient data filtering in a Delta Lake table partitioned by date. Delta Lake tracks various statistics about the partitions and the files within those partitions. This information can be leveraged to skip scanning entire partitions that do not meet the filter conditions defined in the queries.

When a query is executed, the Delta engine can reference the logs to quickly identify which partitions to access based on the metadata and statistics available. This significantly reduces the amount of data that must be processed, leading to faster query performance and optimized resource usage. Rather than performing a full scan of all partitions, leveraging the Delta Log allows for more efficient query execution, as only the relevant partitions that satisfy the filter condition are scanned.

The other options, while they may have benefits in specific contexts, do not directly target the improvement of data filtering through the mechanism provided by the Delta Log. For instance, running queries with fixed filter conditions can enhance performance but does not inherently leverage the efficiencies provided by partitioning and metadata management in Delta Lake. Similarly, selecting relevant columns and aggregating data can aid in reducing the volume of data processed but does not address partition filtering capability as directly as utilizing statistics from the Delta Log.