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Begin by logging into your Harvest account using your credentials. Ensure you have the necessary permissions to access the data you need to export.
Once logged in, locate the 'Reports' section in your Harvest dashboard. This section allows you to generate and view various types of reports, including time, expenses, and invoices.
Choose the specific type of report you wish to export, such as a time report, expense report, or invoice report. Configure the report settings by selecting the appropriate date range, projects, or team members to include.
After generating the report, look for an export option, usually found near the top or bottom of the report page. Select 'Export as CSV' to download the data in a CSV format to your computer. CSV files are compatible with Google Sheets.
Go to Google Sheets and create a new spreadsheet by clicking on the ‘+’ button or selecting ‘Blank’ from the template gallery. This will be the destination for your Harvest data.
In your new Google Sheets document, click on ‘File’ in the top menu, then select ‘Import.’ Choose ‘Upload’ in the import options and drag your downloaded CSV file into the designated area, or click on 'Select a file from your device' to locate it. Follow the prompt to import the data, ensuring you select the appropriate options to replace, append, or create a new sheet based on your preference.
Once the CSV file is imported into Google Sheets, review the data for accuracy. Use Google Sheets' tools to format and organize the data as needed, including adjusting column widths, applying filters, and creating charts or pivot tables for better visualization and analysis.
By following these steps, you can effectively move data from Harvest to Google Sheets without relying on third-party connectors or integrations.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
Harvest is a provider of time tracking and online invoicing services for freelancers and small businesses. Harvest focuses on providing simple to use web-based software for professional services. Customers range from freelancers to creative services businesses, to team within Fortune 500 organizations and non-profits.
Harvest's API provides access to a wide range of data related to time tracking, invoicing, and project management. The following are the categories of data that can be accessed through Harvest's API:
1. Time tracking data: This includes information about the time spent on tasks, projects, and clients.
2. Invoicing data: This includes information about invoices, payments, and expenses.
3. Project management data: This includes information about projects, tasks, and team members.
4. Client data: This includes information about clients, contacts, and projects associated with them.
5. User data: This includes information about users, their roles, and permissions.
6. Reports data: This includes information about various reports generated by Harvest, such as time reports, expense reports, and project reports.
7. Account data: This includes information about the Harvest account, such as account settings, plan details, and billing information.
Overall, Harvest's API provides a comprehensive set of data that can be used to automate various business processes and gain insights into the performance of projects and teams.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
