NOTE: To work with formats that are proprietary to a desktop application, such as Microsoft Excel, you do not need the supporting application installed on your desktop.

Filenames:

NOTE: Filenames that include special characters can cause problems during import or when publishing to a file-based datastore. Do not use the slash (/) character in your filenames.

Native Input File Formats:

 can read and import directly these file formats:

 

NOTE: supports Hive connectivity, which can be used to read data for Hadoop file formats that are not listed here, such as Parquet. For more information, please view the documentation for your Hive version.

For more information on data is handled initially, see Initial Parsing Steps.

 

Native Output File Formats:

 can write to these file formats:

 

 

 

Compression Algorithms:

 

 

NOTE: Importing a compressed file with a high compression ratio can overload the available memory for the application. In such cases, you can uncompress the file before uploading.

Or, if that fails, you should contact your administrator about increasing the Java Heap Size memory.


NOTE: GZIP files on Hadoop are not split across multiple nodes. As a result, jobs can crash when processing it through a single Hadoop task. This is a known issue with GZIP on Hadoop.

Where possible, limit the size of your GZIP files to 100 MB of less, or use BZIP2 as an alternative compression method. As a workaround, you can try to run the job on the unzipped file. You may also disable profiling for the job. See Run Job Page.

Read Native File Formats:

 GZIPBZIPSnappy
CSV SupportedSupportedSupported
JSONSupportedSupportedSupported
Avro  Supported
Hive  Supported

Write Native File Formats:

 GZIPBZIPSnappy
CSVSupportedSupportedSupported
JSONSupportedSupportedSupported
Avro  Supported; always on
Hive  Supported; always on