How to load data from CoinGecko Coins to AWS Datalake

Learn how to use Airbyte to synchronize your CoinGecko Coins data into AWS Datalake within minutes.

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Bespoke pipelines are:
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Furthermore, you will need to build and maintain Y x Z pipelines with Y sources and Z destinations to cover all your needs.
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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a CoinGecko Coins connector in Airbyte

Connect to CoinGecko Coins or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up AWS Datalake for your extracted CoinGecko Coins data

Select AWS Datalake where you want to import data from your CoinGecko Coins source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the CoinGecko Coins to AWS Datalake in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.

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How to Sync CoinGecko Coins to AWS Datalake Manually

How to Sync CoinGecko Coins to AWS Datalake Manually - Method 2:

FAQs

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.

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps: 
1. Set up CoinGecko Coins to AWS Datalake as a source connector (using Auth, or usually an API key)
2. Choose a destination (more than 50 available destination databases, data warehouses or lakes) to sync data too and set it up as a destination connector
3. Define which data you want to transfer from CoinGecko Coins to AWS Datalake and how frequently
You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud. 

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.

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.

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