Two of the most popular sources of data to include in a database are Google Sheets and CSV files. Preset supports both of these data sources, as described below.
Connect a Google Sheet
In order to connect a Google Sheet, you will need Administrator permissions on your workspace. Let's start by learning how to include data from a publicly-shared Google Sheet in Preset.
This is a Google Sheet that has the permission setting of Anyone with the link. To learn how to connect Preset to a privately-shared restricted Google Sheet, please see Google Sheets (Private) Connection.
Set up the Database
In the Toolbar, hover your cursor over the plus + icon, then Data, and then select Connect Google Sheet.
The Connect a database window appears.
In the Display Name field, enter a meaningful name for the new connection to your public Google Sheet. In this example, we simply used the default Google Sheets.
In the Type of Google Sheets Allowed field, retain the default selection of Publicly shared sheets only.
Copy the Google Sheet Link
For this walkthrough, we are going to use a Google Sheet that contains data about Netflix titles. You can view the sample Netflix GSheet here .
Set access permissions
Before continuing, make sure that the Google sheet has its permission set for
anyone with a link.
Users will only be able to access a public google sheet if you provide them with the exact sharable URL. Public google sheets won't appear on search engine results, etc.
Copy the permalink to your clipboard
Connect Google Sheet as a Table
Our next step is to connect the Google Sheet as a table to the database.
Return to Preset and, in the Google Sheet name field, enter a name for a table — in this example, we called our table "Netflix Data".
In the URL field, paste the Google Sheet URL from your clipboard.
To create additional tables, select + Add Sheet and repeat the process.
When done, select Connect.
Tip: Make sure your column names do not contain any special characters such as colon [:] and quotation marks ["] to successfully connect your Google Sheet.
Step 3 of 3 will appear. At this stage, you can configure Advanced options, if needed. To learn more, see Advanced Connection Settings.
To finalize the connection, select Finish.
In order to create charts, though, one last step is required: add a dataset from your newly-created Google Sheet database.
Add a Dataset to Preset
Start by navigating to Datasets. From the Data screen, select the Datasets tab.
Then select + Dataset.
The Add dataset panel appears.
In the Database field, select the database name that you defined earlier in the Display Name field.
In the Schema field, select a schema (if applicable).
In the See Table Schema field, select a table that you created in Step 3 above.
Great, you are now ready to start creating charts using your Google Sheet data!
Upload a CSV File
In addition to adding Google Sheet data to a database, you can add comma-separated value based data (CSV file). In order to do so, you will need Administrator or Primary Contributor permissions on your workspace.
Let's see how this is done.
Connect a Database and Enable Uploads
The first step is to connect a database to contain the uploaded CSV file.
Be sure to add a database!
Please note that a CSV cannot be uploaded to one of the sample databases provided in your Preset account, so be sure to add your own database to make use of the CSV upload feature!
In order for the database to accept the CSV, you need to enable data uploads. Start by browsing to the Databases screen and then select the Edit pencil icon for your database.
The Edit database panel appears. Select the Advanced tab and then expand the Security panel.
In the Schemas Allowed for CSV Upload field, enter the name(s) of any schemas in your database that should be available for CSV data uploads. If more than one, seperate each by a comma.
In the Allow data upload field, select the checkbox.
Lastly, select Finish.
To confirm, in the Databases screen under the CSV upload column header, ensure that a checkmark is displayed for your database row entry.
In the example below, CSV data upload has been enabled for the "BigQuery - Financial" database:
Configure the CSV Upload
In the Toolbar, select Upload file to database and then, in the sub-menu, select Upload CSV.
Alternatively, you can hover your cursor over the plus + icon, then Data, and then select Upload CSV to database from any screen in Preset.
The CSV to Database configuration screen appears.
In the Table Name field, enter a memorable name for the table to be created from the CSV data.
In the CSV File* field, select Choose File and select the CSV file on your local or network system.
In the Database field, select the database to use.
In the Schema field, select the schema associated with the selected database.
In the Delimiter* field, specify the symbol used to delimit the data. For a whitespace, use \s+.
In the Table Exists* field, select what to do if a table already exists: Fail (do nothing), Replace (drop and recreate table), or Append (insert data).
In the Header Row field, enter 0 if your CSV data already has a header row or leave empty if there is no header row present.
In the Index Column field, enter the column to use as row labels for the dataframe. Leave empty if there is no index column.
In the Mangle Duplicate Columns field, select the checkbox to specify duplicate columns as "X.0, X.1".
In the Skip Initial Space field, select to skil spaces after the delimiter.
In the Skip Rows field, enter the number of rows to skip at the start of the file.
In the Rows to Read field, enter the number of rows to read.
In the Skip Blank Lines field, select to skip blank lines instead of interpreting them as NaN values.
In the Parse Dates field, enter a comma-separated list of columns that should be parsed as dates.
In the Infer Datetime Format field, select to use Pandas to automatically interpret the datetime format.
In the Decimal Character field, enter a character to interpret as a decimal point.
In the Dataframe Index field, select to write the dataframe index as a column,
In the Column Label(s) field, enter one or more column labels for index columns. If none are provided and if Dataframe Index is true, then index names are used.
Null Values: List of values that should be treated as null, in JSON format.
When done, select Save.
After selecting Save, you will be taken to the Datasets screen where your newly-loaded CSV and associated dataset appear.