

.webp)
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 visiting the ChartMogul API documentation at [ChartMogul API Docs](https://dev.chartmogul.com/). Familiarize yourself with the available endpoints and data structures, as you'll be using these to extract data directly.
Log into your ChartMogul account. Navigate to the API section to create your API key and secret. These credentials will be used to authenticate your API requests. Make sure to store them securely for later use.
On your local machine, ensure you have a programming environment set up. You can use Python, Node.js, or another language that supports HTTP requests. Install any necessary libraries; for example, `requests` for Python or `axios` for Node.js.
Create a script to send HTTP GET requests to the desired ChartMogul API endpoints. Use your API credentials for Basic Authentication. For example, in Python with the `requests` library, structure your request as follows:
```python
import requests
from requests.auth import HTTPBasicAuth
url = "https://api.chartmogul.com/v1/customers"
response = requests.get(url, auth=HTTPBasicAuth('YOUR_API_KEY', 'YOUR_API_SECRET'))
if response.status_code == 200:
data = response.json()
else:
print("Error fetching data:", response.status_code)
```
Depending on the structure of the data returned by the API, parse the JSON response to extract the relevant fields you want to save into your CSV file. This might involve iterating through lists or accessing nested dictionaries.
Utilize a CSV library to write the parsed data into a CSV file. In Python, you can use the `csv` module:
```python
import csv
with open('chartmogul_data.csv', mode='w', newline='') as file:
writer = csv.writer(file)
# Write header
writer.writerow(['Field1', 'Field2', 'Field3'])
# Write data rows
for item in data['entries']:
writer.writerow([item['field1'], item['field2'], item['field3']])
```
After exporting the data to CSV, open the file to ensure all data is correctly formatted and complete. Store the CSV file in a secure location, especially if it contains sensitive information. Regularly update your script to accommodate any changes in ChartMogul’s API or your data requirements.
By following these steps, you can effectively extract data from ChartMogul and save it to a local CSV file 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.
ChartMogul is an analytics platform to assist you run your subscription business. You get a complete overview of your global subscriber base; MRR, ARPU, ASP, churn and LTV are presented in a beautiful and easy to use dashboard. ChartMogul is a real time reporting and analytics solution for subscription businesses who use Stripe, PayPal, Chargify, Braintree, or Recurly. ChartMogul is an analytics platform to assist you run your subscription business. ChartMogul is a subscription analytics tool that provides real-time reporting on the most critical metrics.
Chartmogul's API provides access to a wide range of data related to subscription businesses. The following are the categories of data that can be accessed through Chartmogul's API:
1. Customer data: This includes information about customers such as their name, email address, and billing information.
2. Subscription data: This includes information about the subscription plans that customers have signed up for, including the plan name, price, and billing frequency.
3. Revenue data: This includes information about the revenue generated by the subscription business, including monthly recurring revenue (MRR), annual recurring revenue (ARR), and total revenue.
4. Churn data: This includes information about customer churn, including the number of customers who have cancelled their subscriptions and the reasons for cancellation.
5. Usage data: This includes information about how customers are using the subscription service, including the number of logins, the amount of data used, and the features that are being used.
6. Financial data: This includes information about the financial performance of the subscription business, including expenses, profits, and cash flow.
Overall, Chartmogul's API provides a comprehensive set of data that can be used to analyze and optimize subscription businesses.
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: