Py Data

Systematically benchmarking common ETL operations across Pandas and Polars, especially with dynamic, semi-structured API data, is crucial for makin…

Master Dagster's asset model, robust error handling, and data quality checks to build resilient, observable, and easily backfillable ELT pipe…

Mastering ELT enables data teams to build highly flexible, resilient pipelines that efficiently ingest raw external data and apply adaptive trans…

Implement an adaptive data quality framework using Great Expectations to validate and monitor dynamic, unpredictable external data feeds, leverag…

When you're building data products, especially those consuming external APIs, you inevitably hit a wall: the data isn't clean, it's n…

Have you ever found your painstakingly built data lake turning into a performance quagmire, with analytical queries grinding to a halt when they …

Have you ever woken up to a production alert, only to discover a critical dashboard or a core ML model is spewing garbage, all because an upstrea…

Have you ever felt that gnawing frustration watching your Spark jobs crawl, with the 'Shuffle Write' and 'Shuffle Read' metrics o…

Remember that frantic 3 AM pager duty call? The one where a critical dashboard went dark, all because an upstream team silently changed a column …

Have you ever felt like you're playing whack-a-mole with your data pipeline, constantly patching for late-arriving records or unexpected sche…

Picture this: you've meticulously built a data pipeline, perhaps even using Pydantic for robust schema validation on incoming API data, just…

Designing a hybrid data warehouse architecture that combines the auditable history and integration flexibility of Data Vault 2.0 with the analyti…

Have you ever spent hours manually sifting through Nepal Rastra Bank's (NRB) Quarterly Economic Bulletins, painstakingly copying numbers from…

As a data engineer working with multiple data sources, I've often struggled with data discovery, governance, and management due to the comple…