A Pythonic Approach to API Data Management: Fetching, Processing, and Displaying Data for Business Intelligence
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V4I2P104Keywords:
API, Python, Business Intelligence, Data Management, Data Processing, Visualization, Modular ProgrammingAbstract
Business intelligence (BI) systems are critical for converting raw data into usable intelligence. As APIs exist everywhere, they are an abundant source of external and internal data for organizations. Building a solid pipeline that helps implement data integrity, scalability, and performance is required to handle these heterogeneous data sets. Having a rich ecosystem of libraries and tools, Python provides a strong platform for solving these problems. This article discusses the Pythonic way of handling API data in the context of business intelligence (BI), from fetching to processing to rendering. We focus on Modular, Scalable, and Reusable data pipeline solutions by Utilizing proven, best-of-breed Python libraries and BI tools. We also address common issues: authentication, error handling, rate-limiting real-time visualization, and interactive dashboards.
This paper illustrates the applicability and efficiency of Python in handling API data for BI by demonstrating a case study of e-commerce sales data using the proposed method. These results showcase a considerable reduction in data processing times, improved user engagement via visualizations, and effortless integration with various APIs. Suppose you wish to utilize the power of Python to make data-driven decisions, including in the business intelligence domain in general
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