The extract, transform, and load (ETL) process has been the de facto way to move and transform data within data warehouses since the onset. As such, it makes perfect sense that IT organizations moving their data to Google Cloud Platform would seek out the cloud equivalent, Google Cloud ETL. Not only would Google Cloud ETL be a similarly hardened and highly-governed process, but, more importantly, the work of Google Cloud ETL would ensure that business users have clean and secure data at their disposal. 

While characteristically true of Google Cloud ETL, a growing shift in thinking has occurred in the past several years, not just around Google Cloud ETL, but of the ETL market as a whole. ETL is through-and-through a developer’s tool; it demands coding skills and otherwise technical knowledge. Yet with the explosion in business users demanding access to data, in many cases, ETL has become a bottleneck more than a boon to the organization. This isn’t necessarily a sign that Google Cloud ETL, or any ETL, is no good, but rather that it is often stretched beyond its purpose. Namely, that ETL in Google Cloud shouldn’t be expected to do the work intended for a data wrangling platform, or more specifically, Google Cloud Dataprep by Trifacta.