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First, log into your Typeform account and navigate to the form containing the data you need to export. Use the "Results" section to access the data. Click on the "Export" option and choose a compatible format such as CSV or Excel. Ensure you download the file to your local machine.
Open the exported file in a spreadsheet application like Microsoft Excel or Google Sheets. Review the data to ensure it's complete and organized. Cleanse the data by removing any unnecessary columns, correcting data types, and handling missing values. Save the cleaned data in CSV format for easy transformation.
On your local machine, ensure you have the necessary tools to interact with Starburst Galaxy. You will need a command-line interface (CLI) tool such as `curl` or `wget` and a SQL client capable of connecting to Starburst Galaxy. Install these tools if they are not already available.
Log into your Starburst Galaxy account. Navigate to the workspace where you want to import the data. Make sure you have the necessary permissions to create tables and insert data. Retrieve connection details such as the host address, port, and authentication credentials.
Use the SQL client to connect to Starburst Galaxy. Create a new table schema that matches the structure of your Typeform data. For example, if your CSV file has columns like "Name" and "Email," the SQL command might look like:
```sql
CREATE TABLE typeform_data (
name VARCHAR,
email VARCHAR
);
```
Adjust data types according to your needs.
Convert your CSV data into a format suitable for SQL insertion, such as a series of SQL `INSERT` statements. You can do this manually or by using a script that reads the CSV file and generates the SQL statements. Execute these statements in your SQL client connected to Starburst Galaxy to populate the table.
After loading the data, perform a series of queries to validate that the data in Starburst Galaxy matches the original data from Typeform. Check for consistency in the number of records and ensure that data fields are correctly populated. Adjust any discrepancies as needed to ensure the integrity of the data transfer.
By following these steps, you can manually move data from Typeform to Starburst Galaxy 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.
Typeform makes collecting and sharing information comfortable and conversational. It's a web-based platform you can use to create anything from surveys to apps, without needing to write a single line of code.
Typeform's API provides access to a wide range of data related to surveys and forms. The following are the categories of data that can be accessed through Typeform's API:
1. Form data: This includes all the questions and responses from a form or survey.
2. Response data: This includes all the responses submitted by users for a particular form or survey.
3. User data: This includes information about the users who have responded to a form or survey, such as their name, email address, and other contact details.
4. Analytics data: This includes data related to the performance of a form or survey, such as the number of responses, completion rates, and other metrics.
5. Theme data: This includes information about the visual appearance of a form or survey, such as the colors, fonts, and other design elements.
6. Webhook data: This includes data related to the integration of a form or survey with other applications, such as the data that is sent to a third-party application when a form is submitted.
Overall, Typeform's API provides access to a comprehensive set of data that can be used to analyze and optimize the performance of forms and surveys.
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:
