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Begin by logging into your Typeform account. Navigate to the form whose data you wish to export. Typeform allows you to manually export data in CSV format. Click on the “Results” tab, select “Responses” and then click on the “Export” button to download your data as a CSV file to your local machine.
Open the exported CSV file to ensure that the data is clean and formatted correctly. Check for any inconsistencies, such as missing headers or irregular data types, and correct them. This step is crucial to prevent errors during the upload to BigQuery.
If you haven’t already, create a Google Cloud Platform account. Once logged in, create a new project in the Google Cloud Console, which will be used to manage your BigQuery datasets. Ensure you have billing enabled for your GCP account as BigQuery requires it for data storage and querying.
In the Google Cloud Console, navigate to BigQuery. From the BigQuery interface, click on “Create Dataset” to create a new dataset where your Typeform data will be stored. Specify the dataset ID and choose the appropriate data location and expiration settings according to your needs.
Before uploading your CSV data, you need to define the schema of the BigQuery table. Use the information from your CSV headers to establish the table schema, specifying field names and data types (e.g., STRING, INTEGER, FLOAT, BOOLEAN, etc.). This can also be done directly during the data upload process in the BigQuery interface.
In BigQuery, click on your dataset and choose “Create Table.” As the source, select “Upload” and then browse to the CSV file on your local machine. Configure the destination settings by specifying the table name and using the schema defined in the previous step. Choose CSV as the file format and configure any additional options, such as field delimiter or header row options. Click “Create Table” to upload your data.
After the upload is complete, it's important to verify that the data has been correctly imported. Use the BigQuery interface to run basic SQL queries to check the data in the table. Look for any discrepancies or errors in the data types and values. Make any necessary adjustments by re-uploading the corrected data if needed.
By following these steps, you can effectively move data from Typeform to BigQuery 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:
