Summarize this article with:


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."
Start by exporting the data you need from Retently. Log into your Retently account, navigate to the data or reports section, and look for an option to export data. Choose a suitable format like CSV or JSON for easy handling. Download the exported file to your local system.
Prepare your local environment for data processing. Ensure you have Python installed, as it will be used to transform and load the data. Install necessary Python packages, like `pandas` for data manipulation and `pymongo` for MongoDB interaction, using pip:
```bash
pip install pandas pymongo
```
Write a Python script to read the exported data file and transform it to fit your MongoDB schema requirements. For example, you can use pandas to load a CSV file and then reformat or clean the data as needed:
```python
import pandas as pd
# Load the data
data = pd.read_csv('retently_data.csv')
# Perform any necessary transformations
# e.g., renaming columns, converting data types
data['new_column'] = data['old_column'].apply(some_transformation_function)
```
Set up a MongoDB database where the data will be stored. Ensure MongoDB is installed and running on your local machine or a server. Create a database and collection if they do not already exist:
```bash
mongo
use my_database
db.createCollection('my_collection')
```
Use the `pymongo` library in your Python script to connect to your MongoDB instance. Set the connection string according to your MongoDB setup (local or remote):
```python
from pymongo import MongoClient
client = MongoClient('mongodb://localhost:27017/')
db = client['my_database']
collection = db['my_collection']
```
Convert the transformed data into a format suitable for MongoDB (e.g., a list of dictionaries if using pandas) and insert it into the desired collection:
```python
# Convert DataFrame to dictionary
data_dict = data.to_dict('records')
# Insert data into MongoDB
collection.insert_many(data_dict)
```
Check that the data has been successfully transferred by querying your MongoDB collection. This can be done using either the MongoDB CLI or a Python script:
```python
# Verify insertion
for document in collection.find():
print(document)
```
Alternatively, use the MongoDB shell:
```bash
mongo
use my_database
db.my_collection.find().pretty()
```
By following these steps, you can effectively move data from Retently to a MongoDB destination without relying on any 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.
Retently is a tool for measuring and increasing customer satisfaction and loyalty through Net Promoter Score surveys and collecting feedback and The tool is packed with various robust features to help you segment your audience, create custom polls, and collect multichannel polls. With Retently, businesses can collect customer feedback and analyze the results with advanced analytics and reports for corrective action. Retently's cloud-based platform is designed to help businesses track their Net Promoter Score, collect valuable customer reviews, and build customer loyalty by converting detractors into repeat customers.
Retently's API provides access to various types of data related to customer feedback and satisfaction. The categories of data that can be accessed through Retently's API include:
1. Customer feedback data: This includes data related to customer feedback, such as NPS scores, customer comments, and ratings.
2. Customer satisfaction data: This includes data related to customer satisfaction, such as customer satisfaction scores, customer loyalty, and customer retention rates.
3. Customer behavior data: This includes data related to customer behavior, such as customer purchase history, customer demographics, and customer preferences.
4. Campaign data: This includes data related to Retently's campaigns, such as campaign performance metrics, campaign engagement rates, and campaign conversion rates.
5. User data: This includes data related to Retently's users, such as user activity, user preferences, and user engagement.
Overall, Retently's API provides access to a wide range of data related to customer feedback and satisfaction, which can be used to improve customer experience and drive business growth.
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:





