Summarize this article with:
Trusted by data-driven companies
Building your pipeline or Using Airbyte
Airbyte is the only open source solution empowering data teams to meet all their growing custom business demands in the new AI era.
- Inconsistent and inaccurate data
- Laborious and expensive
- Brittle and inflexible
- Reliable and accurate
- Extensible and scalable for all your needs
- Deployed and governed your way
Start syncing with Airbyte in 3 easy steps within 10 minutes
Take a virtual tour
Demo video of Airbyte Cloud
Demo video of AI Connector Builder
Setup Complexities simplified!
Simple & Easy to use Interface
Airbyte is built to get out of your way. Our clean, modern interface walks you through setup, so you can go from zero to sync in minutes—without deep technical expertise.
Guided Tour: Assisting you in building connections
Whether you’re setting up your first connection or managing complex syncs, Airbyte’s UI and documentation help you move with confidence. No guesswork. Just clarity.
Airbyte AI Assistant that will act as your sidekick in building your data pipelines in Minutes
Airbyte’s built-in assistant helps you choose sources, set destinations, and configure syncs quickly. It’s like having a data engineer on call—without the overhead.
What sets Airbyte Apart
Modern GenAI Workflows
Move Large Volumes, Fast
An Extensible Open-Source Standard
Full Control & Security
Fully Featured & Integrated
Enterprise Support with SLAs
What our users say
Andre Exner
"For TUI Musement, Airbyte cut development time in half and enabled dynamic customer experiences."
Chase Zieman
“Airbyte helped us accelerate our progress by years, compared to our competitors. We don’t need to worry about connectors and focus on creating value for our users instead of building infrastructure. That’s priceless. The time and energy saved allows us to disrupt and grow faster.”
Rupak Patel
"With Airbyte, we could just push a few buttons, allow API access, and bring all the data into Google BigQuery. By blending all the different marketing data sources, we can gain valuable insights."
Begin by logging into your SurveySparrow account. Navigate to the survey whose data you wish to export. Use the built-in export functionality to download the survey data in a CSV format, which is typically available in the survey results or analytics section. Save this CSV file on your local machine for further processing.
Open the downloaded CSV file using a spreadsheet application like Microsoft Excel or Google Sheets. Review the data for consistency and clarity, ensuring there are no corrupted fields or missing headers. Make any necessary adjustments to the data, such as standardizing date formats or correcting any irregularities.
Ensure your PostgreSQL server is up and running. If not already installed, download the PostgreSQL installer from the official website and follow the instructions to set it up on your system. Create a new database within PostgreSQL to store the survey data.
Use the PostgreSQL command-line interface (psql) or a GUI tool like pgAdmin to define the schema of the table that will store your survey data. Craft a CREATE TABLE statement that matches the structure of your CSV file. Ensure that data types in PostgreSQL match the nature of the data in the CSV file (e.g., INTEGER for numbers, VARCHAR for text).
Utilize the COPY command in PostgreSQL to import data from the CSV file into the database. You can execute this command via the psql interface:
```sql
COPY your_table_name FROM '/path/to/yourfile.csv' DELIMITER ',' CSV HEADER;
```
Replace `/path/to/yourfile.csv` with the actual file path and `your_table_name` with the name of the table you created.
After the data import, run SELECT queries to verify that the data has been correctly imported into the PostgreSQL database. Check for correct number of entries, and ensure that all fields have been populated as expected. This step is crucial to ensure data integrity.
If you anticipate needing to perform this data transfer regularly, consider scripting the process using a language like Python or Bash. You can write a script that automates exporting the data, preparing it, and loading it into PostgreSQL. Use cron jobs (Linux) or Task Scheduler (Windows) to run the script at regular intervals, ensuring your PostgreSQL database remains up-to-date with the latest survey data.
By following these steps, you can efficiently transfer data from SurveySparrow to a PostgreSQL database without relying on third-party tools.
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.
SurveySparrow is an online survey tool which permits users to create and distribute customer surveys through multiple channels, along with evaluate responses and it is also an experience management platform on a mission to assists businesses refine experiences end to end Conversational Experience Management Platform that helps you get a 40% better response rate. SurveySparrow supports you measure employee motivation by using surveys specially made for them. One can easily measure how engaged they are and their job satisfaction.
SurveySparrow's API provides access to a wide range of data related to surveys and responses. The following are the categories of data that can be accessed through SurveySparrow's API:
1. Survey data: This includes information about the surveys created on the platform, such as survey title, description, and status.
2. Response data: This includes information about the responses received for each survey, such as response ID, respondent email, and response timestamp.
3. Question data: This includes information about the questions asked in each survey, such as question type, question text, and answer options.
4. User data: This includes information about the users who have access to the surveys, such as user ID, email, and role.
5. Analytics data: This includes information about the survey performance, such as response rate, completion rate, and average time taken to complete the survey.
6. Integration data: This includes information about the integrations used with SurveySparrow, such as the API key and endpoint URL.
Overall, SurveySparrow's API provides comprehensive access to all the data related to surveys and responses, enabling users to analyze and utilize the data for various purposes.
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:
