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."
First, you need to register for an account on CoinMarketCap and obtain an API key. This key will allow you to make requests to CoinMarketCap's API. Once registered, navigate to the API section of your account to generate and copy your API key.
Use a programming language like Python to make API requests. Install the `requests` library if needed. Write a script to send a GET request to the CoinMarketCap API endpoint (e.g., `/v1/cryptocurrency/listings/latest`). Include your API key in the request headers to authenticate. Parse and store the JSON response data.
Example in Python:
```python
import requests
headers = {'X-CMC_PRO_API_KEY': 'your_api_key'}
response = requests.get('https://pro-api.coinmarketcap.com/v1/cryptocurrency/listings/latest', headers=headers)
data = response.json()
```
Convert the fetched data into a format suitable for Weaviate. Weaviate expects data to be structured into classes and properties. Define a schema that represents the data structure. Map CoinMarketCap data fields to Weaviate classes and properties.
Example structure:
```json
{
"class": "Cryptocurrency",
"properties": [
{"name": "name", "dataType": ["string"]},
{"name": "symbol", "dataType": ["string"]},
{"name": "price", "dataType": ["number"]}
]
}
```
Install and set up Weaviate. This can be done locally using Docker or on a cloud service. Ensure Weaviate is running and accessible. You may need to configure network settings or ports if running locally.
Example Docker command:
```bash
docker run -d -p 8080:8080 semitechnologies/weaviate:latest
```
Use Weaviate’s RESTful API to define the schema. Send a POST request to `/v1/schema` with the schema JSON you prepared. This sets up the necessary classes and properties in Weaviate to store your data.
Example in Python:
```python
import json
schema = {
"classes": [{
"class": "Cryptocurrency",
"properties": [
{"name": "name", "dataType": ["string"]},
{"name": "symbol", "dataType": ["string"]},
{"name": "price", "dataType": ["number"]}
]
}]
}
response = requests.post('http://localhost:8080/v1/schema', json=schema)
```
Iterate over the CoinMarketCap data and transform each entry to match the Weaviate schema. Use Weaviate’s RESTful API to upload data by sending POST requests to `/v1/objects`.
Example in Python:
```python
for crypto in data['data']:
obj = {
"class": "Cryptocurrency",
"properties": {
"name": crypto['name'],
"symbol": crypto['symbol'],
"price": crypto['quote']['USD']['price']
}
}
response = requests.post('http://localhost:8080/v1/objects', json=obj)
```
After loading the data, query Weaviate to ensure the data has been correctly imported. Use Weaviate’s GraphQL interface to fetch and verify data matches your expectations. This step ensures that the data is both complete and accurate.
Example query:
```python
query = """
{
Get {
Cryptocurrency {
name
symbol
price
}
}
}
"""
response = requests.post('http://localhost:8080/v1/graphql', json={'query': query})
print(response.json())
```
This guide provides a direct and practical approach to moving data from CoinMarketCap to Weaviate using native capabilities and basic programming techniques.
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.
"CoinMarketCap is the world's most-referenced price-tracking website for cryptoassets in the quick growing cryptocurrency space. CoinMarketCap has been the premier price-tracking website for cryptocurrencies. Cryptocurrency market capitalization is a simple, straightforward way of searching out how big a digital currency is and it can assist you make smarter. It is an online resource for cryptocurrency market capitalization, volume and liquidity data. Coinmarketcap is the authority when it comes to tracking cryptocurrency prices in real time. "
CoinMarketCap's API provides access to a wide range of data related to cryptocurrencies and their markets. The following are the categories of data that can be accessed through the API:
1. Cryptocurrency data: This includes information about individual cryptocurrencies such as their name, symbol, market cap, circulating supply, total supply, and maximum supply.
2. Market data: This includes data related to the cryptocurrency markets such as the current price, trading volume, and market capitalization of individual cryptocurrencies.
3. Exchange data: This includes data related to cryptocurrency exchanges such as the trading pairs available, trading volume, and price information.
4. Historical data: This includes historical price and volume data for individual cryptocurrencies and the overall cryptocurrency market.
5. News data: This includes news articles related to cryptocurrencies and the blockchain industry.
6. Social data: This includes data related to social media activity such as the number of mentions and sentiment analysis for individual cryptocurrencies.
7. Blockchain data: This includes data related to the blockchain such as the number of transactions, block height, and mining difficulty.
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:





