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."
Log into your SurveyMonkey account and navigate to the survey from which you want to export data. Click on the "Analyze Results" tab. From there, select "Export" and choose a format like CSV or Excel. Download the exported file to your local system.
Open the exported file and ensure that the data is well-structured for Weaviate's requirements. This typically involves ensuring that each column represents a property and each row represents an object. Adjust column headers as necessary to match the schema you plan to use in Weaviate.
Access your Weaviate instance and define a schema that matches the structure of your SurveyMonkey data. This involves creating classes and properties in Weaviate. Use the Weaviate Console or API to add the schema definitions, ensuring data types align with your SurveyMonkey export.
Prepare your local development environment for data ingestion. This includes installing necessary tools like Python and the Weaviate Python client. Ensure you have network access to your Weaviate instance.
Develop a script (using Python, for example) to read the formatted data from your CSV/Excel file and convert it into JSON objects that match your Weaviate schema. Ensure the script handles data types correctly and can process all rows in your export file.
Use the Weaviate Python client or a similar method to authenticate and connect to your Weaviate instance. Execute your script to send the JSON objects to Weaviate via its REST API. Implement error handling to manage any issues during the data upload process.
Once data is loaded, verify the import by querying Weaviate to ensure that all records are correctly represented. Check for any discrepancies in the data and make adjustments as needed. Use the Weaviate Console or API to perform these checks and ensure data integrity.
By following these steps, you can transition your SurveyMonkey data to Weaviate efficiently without relying on external connectors.
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.
Survey Monkey uses the power of the web to enable individuals and companies to reach unprecedented numbers of respondents to gain insights into almost anything. An experience management company, Momentive Inc. (formerly SurveyMonkey Inc.) uses a cloud-based software to provide service solutions for businesses and individuals needing brand or market insights, information regarding consumers’ product experiences, employee and customer experiences—information of any kind for which surveys can provide useful information to improve products, events, experiences.
SurveyMonkey'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 SurveyMonkey's API:
1. Survey data: This includes information about the survey itself, such as the survey title, description, and questions.
2. Response data: This includes information about the responses to the survey, such as the respondent's answers to each question.
3. User data: This includes information about the users who created the survey, such as their name, email address, and account type.
4. Team data: This includes information about the teams that the user belongs to, such as the team name and members.
5. Template data: This includes information about the survey templates available on SurveyMonkey, such as the template name and description.
6. Collector data: This includes information about the collectors used to distribute the survey, such as the collector type and status.
7. Analytic data: This includes information about the survey results, such as the response rate, completion time, and average score.
Overall, SurveyMonkey's API provides access to a comprehensive set of data related to surveys and responses, which can be used to gain insights and make data-driven decisions.
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:
