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Considerations for Google Cloud ETL on Google Cloud Platform

What's New   |   Bertrand Cariou   |   Oct 22, 2022

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.

Cloud Dataprep vs. Google Cloud ETL

Cloud Dataprep is an intelligent service allows anyone to explore, clean, and prepare structured and unstructured data for analysis, reporting, and machine learning. Whereas Google Cloud ETL is a process designed for developers to transform data for its eventual business users, Cloud Dataprep puts the onus on those business users to transform data themselves. Unlike the mapping-based tool that one might picture when thinking about Google Cloud ETL, Cloud Dataprep is visual, interactive, and intelligent. Every click or swipe prompts the service to suggest and predict the next ideal data transformation, which greatly accelerates the overall data preparation process. The service is also equipped to handle any size or type of data thrown at it, be that structured or unstructured data.

The result of choosing Cloud Dataprep instead of solely relying on Google Cloud ETL is data preparation at scale. IT organizations won’t be struggling to keep up with Google Cloud ETL demands, but provisioning the use of a business-friendly service across the organization. Cloud Dataprep has been generally available as of September 2018, but since its private beta launch in early 2017, early customers have already seen tremendous results. Read more about a few testimonials here.

However, it’s worth reiterating that it isn’t always a choice between Cloud Dataprep and Google Cloud ETL; in many cases, our customers use both. For one thing, abandoning Google Cloud ETL isn’t an overnight process, but other organizations are simply reexamining where and when Google Cloud ETL fits in this new landscape.


The Alteryx Experience

The final, quite obvious difference to point out between Google Cloud ETL and Cloud Dataprep is that Alteryx (formerly Trifacta) is the secret ingredient behind the latter. Cloud Dataprep is an embedded version of the Alteryx Designer Cloud platform that matches the same intelligent and interactive Designer Cloud experience one can find in any of our products. In that sense, if an organization is balancing cloud and on-prem platforms, they can maintain consistency with data preparation solutions across these platforms. To try the Designer Cloud experience out for yourself, sign up for a free 30 day trial of our cloud version here or by talking to one of our sales reps.


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