How to load data from Timely to Snowflake destination

Begin by exporting the data from Timely. Depending on the data you need (such as timesheets, projects, or users), navigate to the appropriate section in Timely, and use its built-in export feature. Generally, this involves selecting the data range or type and exporting it as a CSV file, which is a common format for data transfer.

Once you have the CSV file, review and clean the data to ensure consistency and completeness. This might include removing empty rows, correcting data types, or adjusting date formats. Ensure that the CSV adheres to UTF-8 encoding and that any special characters are properly formatted.

Login to your Snowflake account and create a database and schema where you want to store the Timely data. Use the Snowflake UI or SQL commands to define a table structure that matches the columns of your CSV file. For example:
```sql
CREATE OR REPLACE TABLE timely_data (
column1 STRING,
column2 INTEGER,
column3 DATE
-- Add more columns as necessary based on your CSV structure
);
```

Use Snowflake's web interface or a command-line tool like SnowSQL to upload the CSV file to a Snowflake stage. A stage is a temporary storage location in Snowflake. First, create an internal stage:
```sql
CREATE OR REPLACE STAGE my_stage;
```
Then, use the PUT command to upload the CSV file:
```bash
snowsql -q "PUT file://path/to/your/file.csv @my_stage"
```

With the CSV file uploaded to the stage, load the data into your prepared table using the COPY INTO command. This command reads data from the stage and inserts it into the table:
```sql
COPY INTO timely_data
FROM @my_stage/file.csv
FILE_FORMAT = (TYPE = 'CSV', FIELD_OPTIONALLY_ENCLOSED_BY = '"', SKIP_HEADER = 1);
```

After the data is loaded, run a few queries to validate that the data has been correctly imported into Snowflake. Check for the correct number of rows, data integrity, and any potential data loss or corruption:
```sql
SELECT COUNT(*) FROM timely_data;
SELECT * FROM timely_data LIMIT 10;
```

Once you have verified the accuracy of the loaded data, clean up by removing any temporary files from the Snowflake stage to free up storage and maintain organization:
```sql
REMOVE @my_stage;
```

By following these steps, you can efficiently transfer data from Timely to Snowflake without relying on third-party connectors or integrations.