Py Data

As a data engineer working with large F1 racing datasets, you're likely no stranger to the challenges of processing and analyzing this data e…

As a data engineer, I've often struggled to ensure data quality, particularly when working with large datasets or complex data pipelines. Rec…

As a data analyst or investor interested in the Nepali financial market, you may have encountered the challenge of accessing and analyzing financ…

The most insidious data problems aren't the ones that break your pipeline; they're the subtle shifts in data quality that go unnoticed, s…

As data volumes grow, even the most robust ETL pipelines can become bottlenecks, with Pandas-heavy transformations consuming excessive memory and…

When you're building production data pipelines, especially those consuming data from external APIs, you inevitably hit snags: network flakine…

As I delved into the world of financial data scraping in Nepal, I was struck by the scarcity of reliable and up-to-date sources. The Nepali finan…

What if you could transform the disparate and often unstructured financial data from Nepali sources into a coherent, normalized dataset, empoweri…

As data engineers, we often face the dilemma of choosing between ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) patterns when …

When you're running complex data pipelines, especially across multiple teams, the biggest headache isn't usually the code itself; it'…

Have you ever wondered how to scale Apache Airflow from a small experimental setup to a robust production environment, handling complex data pipe…