csv files into Parquet format using Python and Apache’s PyArrow package (see here for more details on using PyArrow). I’m working with a Civil Aviation dataset and converted our standard gzipped. There are a number of ways to create Parquet data, which is a common output from EMR clusters and other components in the Hadoop ecosystem. Now let’s look at how to configure the various components required to make this work. Here we rely on Amazon Redshift’s Spectrum feature, which allows Matillion ETL to query Parquet files in S3 directly once the crawler has identified and cataloged the files’ underlying data structure. This article is about how to use a Glue Crawler in conjunction with Matillion ETL for Amazon Redshift to access Parquet files. Once that’s done, table data can be accessed similar to the User-Defined External Table approach above, the only difference being that the data types were defined by the crawler rather than the user. Using this approach, the crawler creates the table entry in the external catalog on the user’s behalf after it determines the column data types.
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