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Begin by setting up your local development environment. Install Python, as it will be used for fetching data from CoinGecko. Additionally, ensure you have a functional installation of Typesense. You can run Typesense using Docker for ease of setup. This will serve as your local search server.
Use the CoinGecko API to fetch cryptocurrency data. You can do this by sending HTTP GET requests to the CoinGecko API endpoint. For example, use Python's `requests` library to fetch data:
```python
import requests
response = requests.get("https://api.coingecko.com/api/v3/coins/list")
data = response.json()
```
This will give you a list of coins with their respective details.
Once you have the raw data, process and clean it to fit your needs. Ensure that the data is in a structure that Typesense accepts (like JSON). Remove any unnecessary fields and format the data to include only relevant information such as `id`, `symbol`, and `name`.
Before importing data into Typesense, define a schema for the collection that matches the structure of your cleaned data. This schema specifies the fields and their types:
```python
collection_schema = {
"name": "coins",
"fields": [
{"name": "id", "type": "string"},
{"name": "symbol", "type": "string"},
{"name": "name", "type": "string"}
]
}
```
Use the Typesense client to create a collection with the defined schema. Install the Typesense Client for Python and initialize it:
```python
from typesense import Client
client = Client({
'nodes': [{
'host': 'localhost',
'port': '8108',
'protocol': 'http'
}],
'api_key': 'YOUR_API_KEY',
'connection_timeout_seconds': 2
})
client.collections.create(collection_schema)
```
With the collection created, proceed to add the processed data into Typesense. This involves sending the cleaned data in batches to the Typesense collection:
```python
coins_data = [
# Add your processed data here
]
client.collections['coins'].documents.import_(coins_data, {'action': 'upsert'})
```
Finally, verify that the data import was successful. You can do this by querying the Typesense server to retrieve data:
```python
search_parameters = {
'q': 'bitcoin',
'query_by': 'name'
}
results = client.collections['coins'].documents.search(search_parameters)
print(results)
```
This will ensure that your data is correctly indexed and retrievable from Typesense.
By following these steps, you can manually transfer data from CoinGecko to Typesense 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.
CoinGecko is the world's largest independent cryptocurrency data aggregator with over 13,000+ different cryptoassets tracked across more than 600+ exchanges. Coin Price refers to the current global volume-weighted average price of a cryptoasset traded on an active cryptoasset exchange as tracked through CoinGeck. The CoinGecko data market APIs are a set of robust APIs that developers can use to not only enhance their existing apps and services but also to build advanced .
CoinGecko Coins API provides access to a wide range of cryptocurrency data. The API offers real-time and historical data on over 7,000 cryptocurrencies, including Bitcoin, Ethereum, and Litecoin. The data is available in JSON format and can be accessed through HTTP requests. The following are the categories of data that CoinGecko Coins API provides access to:
1. Market Data: This includes real-time and historical price data, trading volume, market capitalization, and market dominance.
2. Exchange Data: This includes data on cryptocurrency exchanges, such as trading pairs, trading volume, and exchange rankings.
3. Blockchain Data: This includes data on the blockchain, such as block height, hash rate, and difficulty.
4. Developer Data: This includes data on developer activity, such as code repositories, commits, and contributors.
5. Social Data: This includes data on social media activity, such as Twitter followers, Reddit subscribers, and Telegram members.
6. Derivatives Data: This includes data on cryptocurrency derivatives, such as futures and options.
7. Defi Data: This includes data on decentralized finance (DeFi) protocols, such as total value locked (TVL) and token prices.
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: