What you need to know about Google BigQuery for Analytics

We did the research and pulled together some useful articles on Google BigQuery so you don’t have to! Read this post to find out some great information on this cloud-based data warehouse so you can decide if Google BigQuery is right for your analytics.

What is Google BigQuery?

Google BigQuery is a cloud based enterprise data warehouse. BigQuery is fully managed, meaning it is hosted and provisioned on Google Cloud servers. It is designed for analyzing billions of rows of data with a SQL-like syntax. BigQuery provides a simple client interface for users to run queries.

For more on Google BigQuery, review BigQuery’s documentation for details on interacting, running and managing jobs, datasets and more.

Comparing Google BigQuery to other warehouses for analysis

BigQuery is simple to use and scales on demand with querying use. In particular, for the mid-market enterprise, one of the biggest advantages of BigQuery for analytics is the scalability and support from Google. BigQuery does not expect you to manage your resources, enabling businesses to overcome the challenges of maintaining a big data infrastructure.

BigQuery is priced per-query, so the price comparison against other managed warehouses depends on your warehouse idleness. If you have a “spiky” workload BigQuery would be cheaper, but if you have a “steady” workload BigQuery is likely more expensive. You can store more data on BigQuery, assuming you aren’t reading it frequently whereas storing the same amount of data in Amazon Redshift will be more expensive.

If you are comparing Google BigQuery against another warehousing option, there are many specific comparisons already written on using BigQuery vs other solutions, some of our favorites are:

The Main Benefits of Using Google BigQuery for Analytics

The best known benefit of using BigQuery is the speed of running queries on massive datasets. BigQuery distributes computing resources, which decreases the time to scan through the data, and its decentralized design allows it to perform queries on the petabyte scale.

You can load Google Analytics data in BigQuery which would provide a detailed compilation of hits from your Google Analytics data source. Using BigQuery you can analyze those website hits on a very granular level, such as a sequence of interactions on a particular session.

By using Google BigQuery as your data warehouse in conjunction with a business intelligence tool, you can load thousands of data points from BigQuery into your analytics tool, using BigQuery’s computing resources and have real-time analysis available.

Here are some more articles about Google BigQuery and its unique features:

The Limitations of Using Google BigQuery for Analytics

As mentioned above, Google BigQuery is priced per query, so if you query your data a lot, it can be very expensive.

The disadvantage of having a fully managed warehouse, means resource controls are out of reach and you may run into quota issues and cannot resolve on your own. Additionally, if you have data sovereignty issues you don’t have many options for geo-locality of the data as BigQuery is only available in a few regions.

Here are some more articles around considerations for Google BigQuery:

Building a Data Stack with Google BigQuery

Google BigQuery works with a variety of partners from data management to analytics to help you load or extract data from your warehouse for meaningful insights.

Google BigQuery’s data integration partners provide solutions for data integration such as ETL / ELT, data modeling, data cleansing, or data migration.

Google BigQuery’s data analytics and visualization partners provide solutions for reporting, sharing, embedding, analyzing and visualizing your data.

Consulting partners provide trusted and validated experts who can provide training, expertise or consulting work on Google BigQuery.

For more resources on building a data stack for analytics with Google BigQuery:

Running Data Analytics on Google BigQuery

Considerations for using a Google BigQuery warehouse include scalability, cost and maintenance, but also should also focus on the ease of extracting your data into useful business insights. You can run queries on BigQuery directly within their tool, but if you want to share those insights or enable non-technical members of your organization you will benefit from a more user-friendly tool for analytics.

Google BigQuery easily integrates with analytics tools such as Chartio through their partner network.

Learn more about visualizing Google BigQuery with Chartio: