FTP

Overview
You can create an FTP Source Connector to read from multiple files within an FTP folder using a SSH Private Key or your SFTP password. 
The schema for this Source Connector is defined by the newest file in the folder that meets the connectors filter criteria, which defaults to all supported files within a directory. All files must have the same schema (number and order of columns). Any files not matching the original schema will be ignored.
Supported file formats: CSV, XLSX, XLS, TXT (comma separated), and ZIP files containing these files.
Prerequisites
Required information:
FTP Host
Port Number
Username
Password
Directory
Creating an FTP Source Connector
Step 1: Navigate to the Connectors list page, then click + New Connector 
Step 2: Under the System prompt, click FTP
Step 3: Enter a Connector Name. 
Step 4: Select Source Connector.
Step 5: Select the FTP protocol used by the server.
Step 6: Provide the FTP host. 
Step 7: Provide the port number.
Step 8: Provide your FTP username.
Step 9: Provide the directory path for the FTP folder you want to connect to. (Note: the path is case sensitive)
Authentication
If you selected SFTP as the server protocol, you have the option to authenticate with an SSH Private key or with a Username and Password. 
If you selected FTP as the server protocol, you can only authenticate with a Username and Password.  
SSH Private Key
If you choose to authenticate with an SSH Private Key, provide the SSH private key with the BEGIN prefix and END suffix, as shown below. Otherwise, provide your FTP Password.
Advanced Options
File Filtering
You can choose to process all source files, or filter the files based on the file name. Any files that do not meet the filter criteria will be ignored. Select one of the options:
1.Include all files: If this option is chosen, all of the files in the folder will be processed in chronological order.
2.Only include files that: If you choose this option, you can filter which files to process from the source folder based on three options:
File names starting with,
File names containing, or
File names ending with.
Any files that do not meet the filter criteria will be ignored.
If you provide a ZIP file with a name that contains the filter criteria, all files within the ZIP file will be processed (if the files match with the Connector’s schema). The file filter does not filter any files within a ZIP file.
File Headers
Within the source folder, all files can contain column header names or none of the files can contain column header names. Select one of the options:
1.All source files contain headers: If this option is selected, we will use the first row as column header names to label the schema within Osmos. Rows two and up will be read as data records.
2.No source files contain headers: If this option is selected, we autogenerate column names for the schema within Osmos. All rows, including the first row, will be read as data records.
Delimiter for TXT Files
The delimiter to use when reading files. Delimiters are selectable in the form of a dropdown list:
Comma ,
Tab      
Pipe |
Semicolon ;
There are then two available options for how these delimiters should be applied:
Selected delimiter applies to ..TXT file only...:By default, the delimiter selected from the dropdown list will only apply to .txt files, .csv (Comma-separated files) and .tsv (Tab-separated values) will continue to be processed according to their file extension designation.
Selected delimiter applies to all files in the folder...: Can be selected for situations when file extension designations should be ignored, and the delimiter selected from the dropdown menu should be the exclusive delimiter for all files processed by the connector.
Handle Invalid Characters
The source file may have characters that may not be valid. You can choose to keep all characters from the source, or to strip the null characters. Select one of the options:
1.Keep all characters from source: If this option is selected, we will retain all characters from the source file, replacing characters we cannot decode with the unicode undefined character.
2.Strip null characters: If this option is selected, we filter out all characters that are equal to 0. Useful when dealing with null-terminated strings.
Deduplication Method
We support three different deduplication methods. You can choose to deduplicate at file level, or record level. Select one of the following options:  
1.File level deduplication: If this option is selected, deduplication will be performed at a file level only. If a file name is changed, or the file itself is changed, the entire file will be processed in subsequent runs.
2.Record level deduplication across all historical data: When this is selected, in addition to file level deduplication, deduplication will be performed at a record level across all the files processed by this Pipeline. An identical record that was already processed in a previous Osmos Pipeline run will not be processed in the current file, nor will duplicated records within the same file.
Example: 
file_a.csv:
item, quantity
apple, 3
orange, 9
banana, 2
file_b.csv:
item, quantity
pear, 9
apple, 3
banana, 2
After processing file_a.csv, if we add file_b.csv to the same directory and run a job, only the row containing pear, 9 will be processed, as apple, 3 and banana, 2 were already seen when file_a.csv was processed. The same applies within the same file - if we'd added pear, 9 to file_a.csv instead of creating file_b.csv, the net result would be the same: pear, 9 would be the only new row.
​
3.Record level deduplication within individual files: When this is selected, in addition to file level deduplication, deduplication will be performed at a record level, but only within the same file. If the file being processed has the same record appearing multiple times, the record will be processed only once.
Example: 
file_a.csv:
item, quantity
apple, 3
orange, 9
banana, 2
file_b.csv:
item, quantity
pear, 9
apple, 3
banana, 2
After processing file_a.csv, if we add file_b.csv to the same directory and run a job, all three records in file_b.csv will be processed. If instead we'd added those records to file_a.csv, the duplicated records (apple, 3, banana, 2) would be skipped, and the new record pear, 9 would be the only new record processed.
Starting Cell
We support Starting Cell offset for spreadsheet type data (.csv, .xls, .xsv, etc.) in order to crop unnecessary information out of a dataset and to ensure headers are correctly mapped. 
The coordinates provided will serve as the starting location from which the data will be read. By default, The data read begins at coordinates (1,1) which will result in a read of all the data in the document. The example below shows in blue where the data has been read, and in white where data has been omitted, based on a configuration of Row 2 Column 2.
Note, that even with no Starting Cell offset in place (i.e. a Row 1, Column 1 configuration) only the first row containing data will begin the reading of the data, omitting any leading rows containing no data.
Leading rows that are completely void of data will be omitted
Sheet Names
By default, this connector will read the first sheet of a workbook as its data source. We also support the designation of specific sheets within a workbook to be read. Sheets designated here will be read exclusively, allowing the connector to skip non-relevant sheets, and to read multiple sheets from a single workbook.
Parser Webhook
We support the use of a parser webhook for the purpose of pre-processing data. This field allows for the designation of a webhook URL. The webhook protocol must also be designated here. Currently, only gRPC webhooks are supported.
A webhook must first be built and configured in order to be utilized by a connector, please contact Support for more information 
Last modified 2mo ago