Embracing the ELT Revolution

A Leap from Traditional ETL Processes

Photo by Christina Morillo

As an engineering leader, I’ve spent over a decade creating, implementing, and supporting complex and innovative infrastructures. My journey in the data sphere, which began with traditional ETL (Extract, Transform, Load) models, has taken a fascinating turn in recent years toward the ELT (Extract, Load, Transform) paradigm. I’ve noticed the tremendous advantages ELT brings to modern data platforms, and I’d like to share my insights with you.

The Old Guard: ETL

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The traditional ETL process was a great starting point, allowing us to extract data from multiple sources, transform it according to business rules, and then load it into a data warehouse. During my tenure in Data Engineering at Dow Jones, I led a number of teams responsible for building platforms that processed billions of records and terabytes of data daily. ETL served us well, offering a structured and predictable data processing methodology.

However, the ETL model often fell short of real-time data processing needs or required an ever-growing amount of capacity when updating existing or adding new data pipelines. Data…