Supported File Formats

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This section contains information on the fie formats and compression schemes that are supported for input to and output of Dataprep by Trifacta.

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: During import, the Dataprep by Trifacta application identifies file formats based on the extension of the filename. If no extension is provided, the Dataprep by Trifacta application assumes that the submitted file is a text file of some kind. Non-text file formats, such as Avro and Parquet, require filename extensions.

NOTE: Filenames that include special characters can cause problems during import or when publishing to a file-based datastore.

Forbidden characters in import filenames:

The following list of characters present issues in the listed area of the product. If you encounter issues, the following listings may provide some guidance on where the issue occurred.

Tip: You should avoid using any of these characters in your import filenames. This list may not be complete for all available running environments.

General:
```
"/"
```
Seb browser:
```
"\"
```
Excel filenames:
```
"#"
```

Native Input File Formats

Dataprep by Trifacta can read and import directly these file formats:

CSV
JSON v1, including nested

NOTE: JSON files can be read natively but often require additional work to properly structure into tabular format. Depending on how the Dataprep by Trifacta application is configured (v1 or v2), JSON files may require conversion before they are available for use in the application. See "Converted file formats" below.

NOTE: Dataprep by Trifacta requires that JSON files be submitted with one valid JSON object per line. Consistently malformed JSON objects or objects that overlap linebreaks might cause import to fail. See Initial Parsing Steps in the User Guide
Plain Text
LOG
TSV
Parquet

NOTE: When working with datasets sourced from Parquet files, lineage information and the $sourcerownumber reference are not supported.

Avro

NOTE: When working with datasets sourced from Avro files, lineage information and the $sourcerownumber reference are not supported.

Google Sheets

NOTE: Individual users must enable access to their Google Drive. No data other than Google Sheets is read from Google Drive.
See Import Google Sheets Data.

For more information on data is handled initially, see Initial Parsing Steps in the User Guide.

Converted file formats

Files of the following type are not read into the product in their native format. Instead, these file types are converted using the Conversion Service into a file format that is natively supported, stored in the base storage layer, and then ingested for use in the product.

NOTE: Compressed files that require conversion of the underlying file format are not supported for use in the product.

Converted file formats:

Excel (XLS/XLSX)

NOTE: Other Excel-related formats, such as XLSM format, are not supported.

Tip: You may import multiple worksheets from a single workbook at one time. See Import Excel Data in the User Guide.

Google Sheets

Tip: You may import multiple sheets from a single Google Sheet at one time. See Import Google Sheets Data in the User Guide.

PDF
- PDF is supported for import only.
- See Import PDF Data in the User Guide.

JSON v2

Notes on JSON:

There are two methods of ingesting JSON files for use in the product.

JSON v2 - This newer version reads the JSON source file through the Conversion Service, which stores a restructured version of the data in tabular format on the base storage layer for quick and simple use within the application.

Tip: This method is enabled by default and is recommended. For more information, see Working with JSON v2.
JSON v1 - This older version reads JSON files directly into the platform as text files. However, this method often requires additional work to restructure the data into tabular format. For more information, see Working with JSON v1.

Native Output File Formats

Dataprep by Trifacta can write to these file formats:

CSV
JSON

Avro

BigQuery Table

Compression Algorithms

When a file is imported, the Dataprep by Trifacta application attempts to infer the compression algorithm in use based on the filename extension. For example, .gz files are assumed to be compressed with GZIP.

NOTE: Import of a compressed file whose underlying format requires conversion through the Conversion Service is not supported.

Read Native File Formats

	GZIP	BZIP	Snappy	Notes
CSV	Supported	Supported	Supported
JSON v2	Not supported	Not supported	Not supported	A converted file format. See above.
JSON v1	Supported	Supported	Supported	Not a converted file format. See above.
Avro			Supported

Write Native File Formats

	GZIP	BZIP	Snappy
CSV	Supported	Supported	Not supported
JSON	Supported	Supported	Not supported

Snappy compression formats

Dataprep by Trifacta supports the following variants of Snappy compression format:

File extension	Format name	Notes
.sz	Framing2 format	See: https://github.com/google/snappy/blob/master/framing_format.txt
.snappy	Hadoop-snappy format	See: https://code.google.com/p/hadoop-snappy/ NOTE: Xerial's snappy-java format, which is also written with a `.snappy` file extension by default, is not supported.

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