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Begin by exporting the data from Wrike. Navigate to the Wrike workspace and select the data you wish to export (e.g., tasks, projects). Use the export feature to download the data in CSV format, as Wrike natively supports CSV exports. This will provide a structured format that can be easily manipulated and imported into SQL Server.
Open the exported CSV file in a spreadsheet program like Microsoft Excel or Google Sheets. Review the data to ensure all necessary fields are included and formatted correctly. Make any required edits, such as renaming columns to match your SQL Server table schema, and ensure there are no invalid characters or empty rows. Save the final version of the CSV file.
Access your SQL Server Management Studio (SSMS) and define a table schema in your database that matches the structure of your CSV file. Create a new table with appropriate data types for each column. Ensure column names in the SQL Server table correspond to the headers in your CSV file. Use SQL scripts to create and configure the table if necessary.
In SSMS, use the Import Data Wizard to import the CSV file. Right-click on the database where you want to import the data, and select "Tasks" > "Import Data." Choose "Flat File Source" as the data source and browse to select your CSV file. Follow the wizard steps to configure the data import process, mapping columns from the CSV to the SQL table.
During the import process, configure options such as delimiter settings (commonly comma for CSV), text qualifier (like double quotes if used), and specify whether the first row contains headers. Ensure that the mappings between CSV columns and SQL Server table columns are correct. Adjust any advanced settings such as error handling and performance options as needed.
Complete the wizard setup and execute the import process. Review the import summary and confirm that the process completes without errors. If any errors occur, refer to the error logs for troubleshooting and correct any issues in the CSV file or import configuration. Re-run the import process if necessary until successful.
After the import is complete, run SQL queries on the SQL Server to verify that the data has been imported correctly. Check for data integrity, such as ensuring no rows are missing and data types are correctly applied. If needed, perform additional transformations using SQL scripts to clean or adjust the data. Confirm the data is now usable within your SQL Server environment.
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:
