How to Perform BigQuery Dataprep

Technology   |   Bertrand Cariou   |   Aug 6, 2022 TIME TO READ: 3 MINS

What is BigQuery Dataprep?

“BigQuery dataprep” is a question asked of analysts that have transitioned to Google Cloud Platform and its serverless data warehouse, BigQuery, but for whom data quality remains a barrier to analytic success. In other words, how do you clean up data stored in BigQuery? Of the many strengths of Google BigQuery—incredible performance and scalability, to name a few—BigQuery dataprep is not one. To complete BigQuery dataprep, analysts leverage another Google Cloud service, Cloud Dataprep.

Cloud Dataprep is an intelligent data service specifically designed to explore, clean, and prepare structured and unstructured data for analysis. As a native Google Platform service, Cloud Dataprep offers the same serverless benefits of BigQuery, removing the need for up-front software installation or ongoing operational overhead. Cloud Dataprep also integrates seamlessly with BigQuery, thereby allowing for BigQuery dataprep. Analysts can perform BigQuery dataprep with data stored in Google BigQuery, on Google Cloud Storage,  or with data stored on their desktop that can then be uploaded to BigQuery.

BigQuery Dataprep and Cloud Dataprep

Alongside investments in cloud platforms like Google, companies have bigger stakes in cutting-edge machine learning and AI initiatives that demand large volumes of complex data to be modeled. That means data stored in BigQuery, which requires BigQuery dataprep for all BigQuery data types, is often increasingly difficult to prepare for feature engineering. By pairing BigQuery with Cloud Dataprep, organizations are able to spend more time focusing on training, testing, and validating their ML and AI models. Cloud Dataprep offers a guided approach to tackle challenging Google BigQuery dataprep, automatically suggesting the next ideal data transformation with each interaction. Its visual nature also makes it easier for analysts and data scientists to discover anomalies or errors before even starting with BigQuery dataprep, which reduces wasted cycles and informs smarter BigQuery dataprep.

To demonstrate its use in complex BigQuery dataprep problems, one analyst used Cloud Dataprep to clean up his Family Tree DNA and 23andMe raw data and eventually gain a better understanding of his genotyping raw data. For example, with BigQuery dataprep and the broader use of Google Cloud Platform, the analyst was able to gain a better understanding of his risk for prostate cancer. While in this case proof of risk was not conclusive, the exercise demonstrates how the power of data coupled with savvy methods such as BigQuery dataprep can dramatically affect risk analysis in the medical community.

Alteryx Powers Cloud Dataprep

As we’ve reviewed, BigQuery dataprep is more or less synonymous with Cloud Dataprep. But there’s a bit more to this relationship between Alteryx and Google BigQuery. As is made clear by the name, Cloud Dataprep is an embedded version of the Designer Cloud platform that matches the same intelligent and interactive Designer Cloud experience one can find in any of the Alteryx Designer Cloud 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. It also means that you don’t have to be a Google Cloud Platform customer to start experiencing Designer Cloud. To try the Designer Cloud experience out for yourself, sign up for the free 30 day trial of our cloud version here or by talking to one of our sales reps.