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Begin by familiarizing yourself with how Delighted exports its data. Delighted allows you to export data in CSV or Excel formats. Access your Delighted account and navigate to the data export section. Determine which format suits your needs best for further processing.
Use the Delighted interface to export your required data set. Choose the CSV format for simplicity as it is widely supported. Ensure that you include all necessary fields in your export for a comprehensive data transfer.
Set up a local directory on your computer where you will store the exported Delighted data. Create separate folders if you plan to handle multiple exports or datasets. Install any necessary tools, such as a text editor or spreadsheet application, to view and edit CSV files.
Open the exported CSV file in a spreadsheet application, such as Microsoft Excel or Google Sheets. Inspect the data for any inconsistencies, missing values, or formatting issues. Clean the data by correcting any errors, and ensure it matches the schema of your MSSQL destination. Save the cleaned file.
Connect to your MSSQL database using a tool like SQL Server Management Studio (SSMS). Verify that you have the necessary permissions to insert data into the target database and that the database is ready to receive the new data.
In your MSSQL database, create a new table that matches the structure of your cleaned CSV data. Use SQL commands to define the table schema, ensuring that data types and field lengths match those in your CSV file. This may involve specifying columns, data types, and primary keys.
Utilize the SQL Server Import and Export Wizard to import your CSV data into the newly created table. Launch the wizard from SSMS, select the CSV file as the data source, and specify the target table in your MSSQL database. Follow the prompts to map columns correctly, and complete the import process. Once completed, verify that the data has been accurately transferred by querying the table.
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.
Delighted assists businesses connect with their customers learning, improving, and delighting.It is well known for delivering some of the most innovative functionality for customer experience management. Delighted is completely the self-serve experience management platform of choice for the worldwide top brands. It helps to collect and analyze survey feedback through Delighted. Get set up in minutes, no technical knowledge needed. Delight helps to build long-lasting relationships and deliver great service experience.
Delighted's API provides access to various types of data related to customer feedback and satisfaction. The categories of data that can be accessed through Delighted's API are:
1. Survey Responses: This includes all the responses received from customers through Delighted's surveys. It includes both quantitative and qualitative data.
2. Metrics: This includes various metrics related to customer satisfaction, such as Net Promoter Score (NPS), Customer Satisfaction Score (CSAT), and Customer Effort Score (CES).
3. Trends: This includes trends related to customer feedback and satisfaction over time. It helps businesses to identify patterns and make data-driven decisions.
4. Segmentation: This includes data related to customer segments, such as demographics, location, and behavior. It helps businesses to understand their customers better and tailor their offerings accordingly.
5. Integrations: Delighted's API also provides access to data from various integrations, such as Salesforce, HubSpot, and Slack. It helps businesses to streamline their workflows and improve their customer experience. Overall, Delighted's API provides a comprehensive set of data that businesses can use to measure and improve their customer satisfaction.
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:
