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In non-raw zones, read optimised, columnar formats such as Parquet and Databricks Delta Lake format are a good choice. Particularly in the curated zone analytical performance becomes essential and the advantages of predicate pushdown/file skipping and column pruning can save on time and cost. Tested on 100GB MOR table. A few partitions have 100% duplicate upsert log file, the other has parquet files only. For parquet files only partitions, the SNAPSHOT query is as efficient as the READ_OPTIMIZED query. The file split with log files is expensive but is expected. For one 50MB parquet file, the log file was ~1GB. Sep 04, 2018 · Note that Amazon Redshift Spectrum can utilize partition pruning through Amazon Athena if the datafiles are organized correctly. By naming nested S3 directories using a /key=value/ pattern, the key automatically appears in our dataset with the value shown, even if that column isn’t physically included in our Parquet files. Sep 04, 2018 · Note that Amazon Redshift Spectrum can utilize partition pruning through Amazon Athena if the datafiles are organized correctly. By naming nested S3 directories using a /key=value/ pattern, the key automatically appears in our dataset with the value shown, even if that column isn’t physically included in our Parquet files.