I have some CSV files located in an S3 bucket. I want to convert all CSV files to Parquet to save cost and improve performance. What approach should I use?

To convert CSV files in S3 to Parquet efficiently, you can use AWS Glue or an EMR Spark job. With Glue, create a crawler to catalog the CSV tables, then write a Glue ETL script that reads the CSV, applies schema inference or a predefined schema, and writes the data back to S3 in Parquet format. Glue handles partitioning, compression (e.g., Snappy), and can run on a schedule or trigger. Alternatively, launch an EMR cluster with Spark, mount the S3 bucket, and run a simple Spark job: val df = spark.read.option("header", "true").csv("s3://bucket/input/"); df.write.mode("overwrite").parquet("s3://bucket/output/"). Spark automatically optimizes the write, and you can partition by a key to improve query performance. Both approaches reduce storage costs (Parquet is columnar and compressed) and speed up downstream analytics (e.g., Athena, Redshift Spectrum).