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, ensure you have a stable environment to work with. This includes having access to a machine with Python installed, as we’ll use Python scripts to pull data from CoinMarketCap. Also, ensure TiDB is installed and running on your server. Verify connectivity between your Python environment and the TiDB server.
To access CoinMarketCap data, you need an API key. Sign up at the CoinMarketCap website and navigate to the API section. Register for an API key, which will be used to authenticate your requests to their server.
Use Python to fetch data from CoinMarketCap. You can use the `requests` library to make HTTP requests to their API. For example:
```python
import requests
api_url = 'https://pro-api.coinmarketcap.com/v1/cryptocurrency/listings/latest'
headers = {
'Accepts': 'application/json',
'X-CMC_PRO_API_KEY': 'your_api_key_here',
}
response = requests.get(api_url, headers=headers)
data = response.json()
```
This script fetches the latest cryptocurrency listings. Parse the JSON data to extract the information you need.
Once you have the raw data, process it to fit the schema of your TiDB database. This might involve selecting specific fields, transforming data formats, or cleaning the data. Create a function to transform this data into a list of tuples or a similar structure that matches your TiDB table schema.
Use a MySQL client library in Python, such as `mysql-connector-python`, to connect to TiDB. First, install the library if you haven’t:
```bash
pip install mysql-connector-python
```
Then, establish a connection:
```python
import mysql.connector
conn = mysql.connector.connect(
host='your_tidb_host',
user='your_tidb_user',
password='your_tidb_password',
database='your_database'
)
cursor = conn.cursor()
```
With the processed data and an active connection, write SQL queries to insert the data into your TiDB tables. Use SQL `INSERT` statements, and consider using `executemany()` for batch inserts to improve performance:
```python
insert_query = """
INSERT INTO your_table (column1, column2, column3)
VALUES (%s, %s, %s)
"""
cursor.executemany(insert_query, processed_data)
conn.commit()
```
After the data insertion, verify that the data is correctly inserted and consistent. Run `SELECT` queries on your TiDB database to ensure the data reflects the fetched CoinMarketCap data. Also, check for any anomalies or mismatches and address them by reprocessing or cleaning the data as necessary.
By following these steps, you can move data from CoinMarketCap to TiDB using direct API calls and manual data handling in Python, 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.
"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:





