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Start by obtaining an API key from Polygon.io. Use this key to authenticate your requests to the Polygon Stock API. Ensure you have reviewed the API documentation to understand the endpoints and the data format you will be working with.
Log in to your AWS Management Console and navigate to the S3 service. Create a new S3 bucket where you will store the data retrieved from the Polygon API. Ensure the bucket has the appropriate permissions and is in the correct region.
Write a Python script to fetch data from the Polygon Stock API. Use Python's `requests` library to send HTTP requests and retrieve the data. Parse the API response to handle JSON or CSV formats as returned by the API.
Process the data you retrieved in the previous step to match the desired schema for your S3 storage. This could involve cleaning, transforming, or reorganizing the data. Use Python’s data manipulation libraries like pandas for efficient data transformation.
Use the `boto3` library to upload the processed data to your S3 bucket. Ensure that the data is saved in a format that is compatible with AWS Glue, such as CSV, JSON, or Parquet. Write the script to handle errors in case of upload failures.
In the AWS Management Console, navigate to AWS Glue and set up a Glue Crawler. Configure the crawler to point to your S3 bucket. The crawler will catalog the data and create a table schema in the AWS Glue Data Catalog, making it available for further processing.
Create an AWS Glue ETL job to process the data. Use the AWS Glue Studio or the AWS Glue Console to define the job. You can write a PySpark script if additional data processing is required. Schedule the job to run at your desired frequency, ensuring that data is regularly updated in your data catalog or transformed as needed.
By following these steps, you can effectively move data from the Polygon Stock API to AWS S3 and process it with AWS Glue, utilizing AWS's built-in tools and services.
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:
