# Use columnar storage formats
[Apache Parquet](https://parquet.apache.org) and [ORC](https://orc.apache.org/) are columnar storage formats that are optimized for fast retrieval of data and used in AWS analytical applications.
Columnar storage formats have the following characteristics that make them suitable for using with Athena:
+ *Compression by column, with compression algorithm selected for the column data type* to save storage space in Amazon S3 and reduce disk space and I/O during query processing.
+ *Predicate pushdown* in Parquet and ORC enables Athena queries to fetch only the blocks it needs, improving query performance. When an Athena query obtains specific column values from your data, it uses statistics from data block predicates, such as max/min values, to determine whether to read or skip the block.
+ *Splitting of data * in Parquet and ORC allows Athena to split the reading of data to multiple readers and increase parallelism during its query processing.
To convert your existing raw data from other storage formats to Parquet or ORC, you can run [CREATE TABLE AS SELECT (CTAS)](ctas.md) queries in Athena and specify a data storage format as Parquet or ORC, or use the AWS Glue Crawler.
## Choose between Parquet and ORC
The choice between ORC (Optimized Row Columnar) and Parquet depends on your specific usage requirements.
Apache Parquet provides efficient data compression and encoding schemes and is ideal for running complex queries and processing large amounts of data. Parquet is optimized for use with [Apache Arrow](https://arrow.apache.org/), which can be advantageous if you use tools that are Arrow related.
ORC provides an efficient way to store Hive data. ORC files are often smaller than Parquet files, and ORC indexes can make querying faster. In addition, ORC supports complex types such as structs, maps, and lists.
When choosing between Parquet and ORC, consider the following:
**Query performance** – Because Parquet supports a wider range of query types, Parquet might be a better choice if you plan to perform complex queries.
**Complex data types** – If you are using complex data types, ORC might be a better choice as it supports a wider range of complex data types.
**File size** – If disk space is a concern, ORC usually results in smaller files, which can reduce storage costs.
**Compression** – Both Parquet and ORC provide good compression, but the best format for you can depend on your specific use case.
**Evolution** – Both Parquet and ORC support schema evolution, which means you can add, remove, or modify columns over time.
Both Parquet and ORC are good choices for big data applications, but consider the requirements of your scenario before choosing. You might want to perform benchmarks on your data and queries to see which format performs better for your use case.
## Convert to columnar formats
Options for easily converting source data such as JSON or CSV into a columnar format include using [CREATE TABLE AS](ctas.md) queries or running jobs in AWS Glue.
+ You can use `CREATE TABLE AS` (CTAS) queries to convert data into Parquet or ORC in one step. For an example, see [Example: Writing query results to a different format](https://docs.aws.amazon.com/athena/latest/ug/ctas-examples.html#ctas-example-format) on the [Examples of CTAS queries](ctas-examples.md) page.
+ For information about using Athena for ETL to transform data from CSV to Parquet, see [Use CTAS and INSERT INTO for ETL and data analysis](ctas-insert-into-etl.md).
+ For information about running an AWS Glue job to transform CSV data to Parquet, see the section "Transform the data from CSV to Parquet format" in the AWS Big Data blog post [Build a data lake foundation with AWS Glue and Amazon S3](https://aws.amazon.com/blogs/big-data/build-a-data-lake-foundation-with-aws-glue-and-amazon-s3/). AWS Glue supports using the same technique to convert CSV data to ORC, or JSON data to either Parquet or ORC.