In this example, I have created two identical tables and loaded one with csv file while other with parquet file. Difficult to debug: If you run into some copy error related with data type,data size or data value then in absence of any utility which can help you in previewing parquet file debugging becomes a big challenge.Upon a complete walkthrough of the article, you’ll clearly understand each data type and the kind of data you can store in it. This article provides you with an in-depth guide about Amazon Redshift Numeric data types. So if you want to modify some column dataype in Redshift Table then it will fail like you cannot load INTEGER column in a file into a VARCHAR column into a Redshift table. With the correct data type assigned to your business data in Amazon Redshift, you can store, manipulate, and query data with ease. Inbuilt Schema info: Parquet comes with inbuilt info on file-columns metadata.No Format Options available: Presently, almost none of the options available in copy works with parquet like MAXERROR, IGNOREHEADER etc.Challenges with Parquet files in Redshift Copy So chances of facing charset related issues or junk characters while loading a parquet file are less. Preserves Charset: Parquet file when generated with specific charset preserves it properly.
Remember 1 CSV is loaded by 1 Slice only hence it is suggested to split CSV file into multiples of total slices in cluster before loading. Depending on the slices you have in your redshift cluster, the 128 MB file parts shall be processed in parallel during copy.
Saves Space: Parquet by default is highly compressed format so it saves space on S3.Advantages of using PARQUET files in Redshift Copy You don't have to supply any other information like delimiter, header etc. "FORMAT AS PARQUET" informs redshift that it is parquet file.
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