Understanding Automated Cloud Data Warehouse with BigQuery and Looker

Strategy   |   Bertrand Cariou   |   Oct 22, 2020

This blog illustrates how the combination of Cloud Dataprep, Looker, and BigQuery fulfills the three necessary elements for a scalable, self-service data warehouse a.k.a. self-service analytics. 

What is self-service analytics?

Self-service analytics empower the everyday business user to create their own end-to-end analytics solution—that is, accessing data, preparing and cleansing it for use, and generating reports and dashboards. Rewind just one decade back and it would have been unthinkable for that kind of work to be done by business users. All data operations were routed through IT because they were the only professionals trained in working with large and complex data. Today’s self-service analytics tools give users all the power to work as a data scientist or data engineer would, but are simple and scalable enough for anyone to adopt and deliver results quickly. 

True self-service analytics can’t exist without a cloud-based solution. Adopting a self-service vision requires organizations to break free from any hardware, storage and processing dependencies, which limit scalability and demand IT department’s assistance in installing or maintaining solutions. At most, self-service analytics may require light configuration of data services and initial training, though organizations should expect to see results soon after. 

What are the elements for self-service analytics on Google Cloud?

To establish a self-service analytics practice, you need to rely on three fundamental elements: data preparation, data storage & processing, and data visualization.

Data Preparation

Data preparation is a fundamental step toward accurate and trustworthy analytics. Cloud Dataprep allows you to clean, standardize, combine, and create various calculations and metrics from Google Cloud Storage, BigQuery, Google Sheets, or other data sources needed for your analytics. Most of the time, data sources arrive in different formats that don’t coincide; data preparation allows users to clean and normalize disparate data in order to produce trusted analytic insight. 

Data Storage

Data storage is the foundation for constructing analytics. It is a space for your data to live and grow (and stay secure). Within the Google Cloud Platform, the easiest way to store and retrieve your data for analytics is BigQuery, a highly scalable, and cost-effective multi-cloud data warehouse. 

Data Visualization

The visualization layer presents your data in a visually appealing form, such as graphs and tables. It brings insights to life and helps drive your decision-making. Looker uses BigQuery data to allow you to create your reports and dashboards. 

With these fundamental elements in place, you have a scalable and comprehensive self-service analytics solution.

Understand Google Cloud self-service analytics concepts

Here are the basic concepts and their relations for an end-to-end self-service analytics solution built with GCP and Looker. These concepts are the foundation you need to understand and iterate on your self-service analytics solution.

Here is a visual representation of these concepts and their relations to create a self-service analytics solution within the Google Cloud.

You too can benefit from self-service analytics on Google Cloud 

Still a bit too theoretical? No problem! Watch the end-to-end analytics video to see it in action.

The demo more clearly illustrates how you can establish a scalable, self-service analytics solution that leverages Cloud Dataprep to clean, combine, and create metrics; BigQuery to store and retrieve data; and Looker to visually report on top of your data. By exploring each GCP service deeper and iterating on these principles, you will be able to solve any requirements for your analytics in a self-service manner.