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Begin by logging into your Retently account. Navigate to the data or report section where the information you want to export is located. Use the export functionality provided by Retently to download the data in a CSV or Excel format. Save this file to a location on your computer where it can be easily accessed.
Open the exported file in Excel or a similar spreadsheet application. Review the data to ensure it is clean and properly formatted. Remove any unnecessary columns or rows, and ensure there are no blank or malformed entries. Save the cleaned file in CSV format, which is suitable for import into MS SQL Server.
Access your MS SQL Server database using a tool like SQL Server Management Studio (SSMS). Create a new table where you will import the data. Define the table schema to match the structure of the CSV file, ensuring that data types in SQL Server align with the data types from the CSV. Execute the SQL `CREATE TABLE` statement to create the table.
Utilize the `BULK INSERT` command in SQL Server to import data from the CSV file into the newly created table. Open a new query window in SSMS and write a SQL command similar to the following:
```sql
BULK INSERT YourDatabase.YourTable
FROM 'C:\path\to\your\file.csv'
WITH
(
FIELDTERMINATOR = ',',
ROWTERMINATOR = '\n',
FIRSTROW = 2
);
```
Replace `YourDatabase`, `YourTable`, and the file path with your actual database name, table name, and file location.
After running the `BULK INSERT` command, check the data in the table to ensure it has been imported correctly. You can do this by executing a simple `SELECT` query to fetch a few rows from the table. Verify that the data types and values match expectations.
If there are any errors during the import process, review error messages for specific details. Common issues might include data type mismatches or incorrect file paths. Use SQL Server's error logs and messages to troubleshoot and resolve these issues. Modify data types or correct the CSV file if necessary, and then attempt the import again.
If you need to perform this data transfer regularly, consider writing a script or a stored procedure to automate the export, cleaning, and import process. You can schedule this script to run at regular intervals using SQL Server Agent or a similar scheduling tool available in your environment.
By following these steps, you can effectively move data from Retently to MS SQL Server manually, ensuring that you maintain control over the entire process without relying on third-party tools 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.
Retently is a tool for measuring and increasing customer satisfaction and loyalty through Net Promoter Score surveys and collecting feedback and The tool is packed with various robust features to help you segment your audience, create custom polls, and collect multichannel polls. With Retently, businesses can collect customer feedback and analyze the results with advanced analytics and reports for corrective action. Retently's cloud-based platform is designed to help businesses track their Net Promoter Score, collect valuable customer reviews, and build customer loyalty by converting detractors into repeat customers.
Retently's API provides access to various types of data related to customer feedback and satisfaction. The categories of data that can be accessed through Retently's API include:
1. Customer feedback data: This includes data related to customer feedback, such as NPS scores, customer comments, and ratings.
2. Customer satisfaction data: This includes data related to customer satisfaction, such as customer satisfaction scores, customer loyalty, and customer retention rates.
3. Customer behavior data: This includes data related to customer behavior, such as customer purchase history, customer demographics, and customer preferences.
4. Campaign data: This includes data related to Retently's campaigns, such as campaign performance metrics, campaign engagement rates, and campaign conversion rates.
5. User data: This includes data related to Retently's users, such as user activity, user preferences, and user engagement.
Overall, Retently's API provides access to a wide range of data related to customer feedback and satisfaction, which can be used to improve customer experience and drive business growth.
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:





