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Google Drive

Overview

You can create a Google Drive Source Connector to read from multiple files within a Google Drive folder using your Google account. We do not currently support Shared Drives.
To set up this Source Connector, your Google account will need access to the Google folder you are connecting to.
The schema for this Source Connector is defined by the newest file in the folder. 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. We do not read Google Sheets from a Google Drive Connector. To read from a Google Sheet, please set up a Google Sheets Connector.
If you have multi-factor authentication enabled for your Google account, you may need to recreate the Source Connector when the authentication expires. Re-authentication for existing connections coming soon.

Prerequisites

Required information:
  1. 1.
    Google Drive Folder URL
  2. 2.
    Google Account Username & Password

Creating a Google Drive Source Connector

Step 1: Click New Connector
Step 2: Under the System prompt, click Google Drive
Step 3: Enter a Connector Name
Step 4: Select Source Connector
Step 4: Enter the Google Drive Folder URL
The schema for the Source Connector is defined by the newest file in the folder. All files must have the same schema (number and order of columns). Any files not matching the original schema will be ignored.

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. 1.
    Include all files: If this option is chosen, all of the files in the folder will be processed in chronological order.
  2. 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. 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. 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. 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. 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. 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. 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 Osmos Pipeline. An identical record that was already processed in a previous 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. 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 data read, 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