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To start, log into your Wrike account and navigate to the project or report containing the data you wish to export. Use Wrike's built-in export feature to download the data. Typically, you can export data in CSV format by selecting the "Export" option from the menu in the top-right corner of your project or report view.
Once you initiate the export, Wrike will generate a CSV file. Download this file to your local machine. Ensure that you save it in a location that's easy to access for the next step, such as your desktop or a dedicated folder.
Open Google Sheets by logging into your Google account and navigating to Google Drive. Click on the '+ New' button and select 'Google Sheets' to create a new spreadsheet where you will import your Wrike data.
In the new Google Sheet, click on 'File' in the top menu, then select 'Import'. Choose 'Upload' and drag your CSV file into the window or click 'Select a file from your device' to locate and select the CSV file downloaded from Wrike.
After selecting your CSV file, Google Sheets will prompt you with an import settings window. Choose 'Replace spreadsheet' if the sheet is empty or 'Insert new sheet(s)' if you want to add the data to an existing sheet. Make sure the 'Detect automatically' option is selected for the separator type, then click 'Import data'.
Once the data is imported, review the spreadsheet to ensure the data has been correctly transferred from Wrike to Google Sheets. Check for any formatting issues or data misalignments and make necessary adjustments, such as adjusting column widths or recalculating formulas.
After verifying the data, save your Google Sheet by clicking on 'File' and then 'Save'. If you need to share the data with others, click the 'Share' button in the top-right corner, add the email addresses of your collaborators, and set the appropriate access permissions.
By following these steps, you can successfully transfer data from Wrike to Google Sheets manually, ensuring you maintain control over the process without relying on third-party solutions.
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.
Wrike is an American project management application service provider which is based in San Jose, California. It is a cloud based association and project management tool that assists users to manage projects from start to finish, providing full visibility. Wrike is entirely a cloud-based project management platform for teams of 20+ which is suitable for both large program and SMBs. Wrike ransaks to discard complexity from work so people and teams can enforce at their best.
Wrike's API provides access to a wide range of data related to project management and collaboration. The following are the categories of data that can be accessed through Wrike's API:
1. Tasks: Information related to tasks such as task name, description, due date, status, and assignee.
2. Projects: Data related to projects such as project name, description, start and end dates, and project status.
3. Users: Information about users such as user name, email address, and user role.
4. Time tracking: Data related to time tracking such as time spent on tasks, time entries, and billable hours.
5. Comments: Information related to comments such as comment text, author, and date.
6. Attachments: Data related to attachments such as attachment name, type, and size.
7. Custom fields: Information related to custom fields such as field name, type, and value.
8. Folders: Data related to folders such as folder name, description, and folder structure.
9. Reports: Information related to reports such as report name, description, and report data.
Overall, Wrike's API provides access to a comprehensive set of data that can be used to enhance project management and collaboration.
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:
