How to load data from SurveySparrow to ElasticSearch
Learn how to use Airbyte to synchronize your SurveySparrow data into ElasticSearch within minutes.


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

Raman Singh
Predictable, straightforward pricing model that simplified budgeting and significantly reduced overall spend

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."
How to Sync to Manually
Begin by logging into your SurveySparrow account. Navigate to the survey from which you want to export data. Use the 'Export' option to download the survey data in a CSV or JSON format. Ensure that all necessary fields are included in the export.
Once you have the data file, clean and format it to match Elasticsearch's requirements. Ensure that each survey response is structured in a JSON format, with keys corresponding to Elasticsearch field names. Check for any data inconsistencies or missing fields.
If you haven't already, download and install Elasticsearch on your local machine or server. Follow the official Elasticsearch installation guide for your operating system. Ensure that Elasticsearch is running by accessing `http://localhost:9200` in your web browser.
Use the Elasticsearch API to create an index where the survey data will be stored. Open a terminal or command prompt, and use the following command to create an index (replace `survey_index` with your desired index name):
```bash
curl -X PUT "localhost:9200/survey_index" -H 'Content-Type: application/json' -d'
{
"mappings": {
"properties": {
"field1": { "type": "text" },
"field2": { "type": "date" },
...
}
}
}'
```
Define the appropriate mappings for each field to ensure proper data indexing.
Write a script in a programming language like Python to read the prepared JSON file and send the data to Elasticsearch. Use the `requests` library to handle HTTP requests. Here is a basic example in Python:
```python
import json
import requests
# Load your data from the JSON file
with open('survey_data.json', 'r') as file:
data = json.load(file)
# Define the Elasticsearch endpoint
es_endpoint = "http://localhost:9200/survey_index/_doc"
# Iterate over each survey response and send it to Elasticsearch
for response in data:
response = json.dumps(response)
headers = {'Content-Type': 'application/json'}
requests.post(es_endpoint, headers=headers, data=response)
```
After running your script, verify that the data has been uploaded to Elasticsearch. Use the following command to check the number of documents in the index:
```bash
curl -X GET "localhost:9200/survey_index/_count"
```
Confirm that the count matches the number of records you intended to upload.
Now that your data is successfully uploaded to Elasticsearch, use Elasticsearch's powerful query language to search and analyze your survey data. You can perform operations like aggregations, full-text search, and more using the Elasticsearch Query DSL. Access the data using a tool like Kibana for advanced visualization if needed.
By following these steps, you can efficiently transfer data from SurveySparrow to Elasticsearch without relying on any third-party connectors or integrations.