Data engineering requires expertise in programming and data management, and now IT leaders need to include large language models in their data strategy.

As enterprises generate and process vast amounts of data, the need for scalable, cost-efficient, and high-performance data ...

And before that data is ready for analysis, it needs to be combined, cleaned, and normalized—a process otherwise known as extract, transform, load (ETL)—which can be laborious and error-prone.

Azure Databricks is a cloud-based analytics platform optimized for big data and artificial intelligence (AI) workloads.

For ETL processes, I find PySpark particularly powerful ... A: I would emphasize the importance of building a strong ...

A: My journey in data engineering began with a fascination for ... Databricks, and traditional ETL tools, I've learned that the key is to choose the right tool for the specific problem while ...