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Begin by setting up your development environment. Ensure you have a Python environment ready since it will help you interact with the Polygon Stock API and Typesense. Install essential libraries such as `requests` for HTTP requests and `typesense` client for interacting with Typesense using `pip install requests typesense`.
Register for an API key on the Polygon Stock API platform if you haven't already. Familiarize yourself with the API documentation to understand the endpoints available for fetching stock data. Use Python's `requests` library to send HTTP GET requests to the desired endpoint, such as `/v2/aggs/ticker/{ticker}/range/{multiplier}/{timespan}/{from}/{to}` to fetch stock data.
Once you've fetched the data, parse the JSON response. Use Python's built-in `json` module to convert the response into a Python dictionary. Extract relevant fields that you wish to store in Typesense, such as stock symbol, date, open, close, high, low, and volume.
Install and run a Typesense server if you haven't done so. You can download the Typesense binary for your operating system and run it locally. Ensure the server is up and running at `localhost:8108` with the default API key or set up your own key.
Define the schema for your Typesense collection. The schema should include the fields extracted from the Polygon Stock API data. Use the Typesense client to create a new collection in Typesense by specifying the collection name and schema, which includes field names, types, and any indexing settings.
Transform your parsed data into the format expected by Typesense. Each stock data entry should be a dictionary matching the collection schema. Use the Typesense client to index the data into the created collection. You can use the `import_documents` method to index multiple documents in a batch for efficiency.
After indexing, verify the data by querying the Typesense collection. Use the `search` method of the Typesense client to perform a search query and ensure the data is correctly stored. Check for any errors in indexing and repeat the data parsing and indexing steps if necessary.
By following these steps, you can efficiently move data from the Polygon Stock API 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.
Polygon Stock API is a financial data provider that offers real-time and historical stock market data for developers and investors. The API provides access to a wide range of financial data, including stock prices, volume, market capitalization, and more. It also offers advanced features such as technical indicators, news feeds, and sentiment analysis. The API is designed to be easy to use and integrate into existing applications, making it a valuable tool for financial professionals and developers looking to build financial applications. With Polygon Stock API, users can access accurate and reliable financial data to make informed investment decisions.
Polygon Stock API provides access to a wide range of financial data related to the stock market. The API offers real-time and historical data for various financial instruments, including stocks, options, and cryptocurrencies. Here are the categories of data that the Polygon Stock API provides:
1. Stock Data: The API provides real-time and historical data for stocks listed on various exchanges, including NYSE, NASDAQ, and BATS.
2. Options Data: The API offers real-time and historical data for options contracts, including strike price, expiration date, and implied volatility.
3. Cryptocurrency Data: The API provides real-time and historical data for various cryptocurrencies, including Bitcoin, Ethereum, and Litecoin.
4. News Data: The API offers access to news articles related to the stock market, including company news, market trends, and economic indicators.
5. Financial Data: The API provides access to various financial data, including earnings reports, financial statements, and analyst ratings.
6. Market Data: The API offers real-time and historical market data, including market indices, volume, and price movements.
7. Fundamental Data: The API provides access to fundamental data, including company profiles, financial ratios, and dividend information.
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:
