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Visit the CoinGecko API documentation page at https://www.coingecko.com/en/api. Familiarize yourself with the endpoints available for retrieving cryptocurrency data. Identify the specific endpoints you need, such as `/coins/markets` for current data on various cryptocurrencies.
Use a programming language like Python to send HTTP requests to the CoinGecko API. For example, using Python's `requests` library, you can fetch data by sending a GET request to the desired endpoint. Parse the JSON response to extract the data you need, such as coin IDs, prices, market caps, etc.
Prepare the data in a format compatible with Starburst Galaxy. Ensure the data is structured in a way that Starburst Galaxy can ingest, such as CSV or JSON. Ensure all fields match the expected schema in Starburst Galaxy, including correct data types and field names.
Log into your Starburst Galaxy account and set up the necessary environment for data ingestion. Create a new catalog or schema if required, and ensure you have the necessary permissions to load data into your desired destination.
Use the Starburst Galaxy web interface or command-line tools to upload your formatted data file. If using the web interface, navigate to the data loading section and follow the prompts to upload your CSV or JSON file. Ensure you specify the correct destination schema and table.
Once the data is uploaded, run queries in Starburst Galaxy to verify the data has been ingested correctly. Check for consistency in data fields and ensure no data corruption occurred during the transfer. Address any discrepancies by revisiting the formatting or re-uploading the data.
To keep your data up-to-date, automate the data retrieval, formatting, and upload process by writing a script that performs these actions at regular intervals. Use cron jobs (for Unix-like systems) or Task Scheduler (for Windows) to run your script at scheduled times.
By following these steps, you can manually move data from CoinGecko to Starburst Galaxy 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.
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