In 2019, I wrote an article all about this big industry data warehouse called BigQuery. Naturally a lot has changed in three years so this article is meant to recap some key points and update others. Let’s take a look at BigQuery in 2023!
So, What Is BigQuery?
The simplest definition comes from Google itself: “BigQuery is Google’s serverless cloud storage platform designed for large data sets.”
Now let’s unpack this to provide some actual clarity.
If you don’t like technical explanations, the TL;DR is just under the end of the next paragraph.
A server is a fancy word for a computer with a dedicated job. The server running your website or any of the services you use is not much different than the computer you’re on right now. The difference is this computer has a job: to serve your website. As the cloud has evolved, we have begun doing neat tricks with our servers, such as auto scaling. This means the server is not just one computer but can scale up to be many computers to handle load—but since these are physical machines, just like the one you are on right now, it takes time for those to boot up each time we need a new machine for a large load. The term “serverless” is a bit silly because if a server is a computer it suggests we are not using computers, but instead what it means is we aren’t using any one specific computer. A whole sea of computers is waiting to serve any request that comes to this platform; therefore, we don’t have to manage our one computer or the load that the software is handling.
If all that was too much tech talk, remember this: “serverless” means the platform will handle scaling for us without needing IT to handle the infrastructure.
Since BigQuery is serverless, it can handle a lot of data very fast and at a low cost. The platform is there to help you get all of your data in one place for faster insights, which leads to faster actioning of data.
Other helpful BigQuery benefits include:
- Built-in integrations that make building a data lake in BigQuery simple, fast, and cost-effective.
- Centralizing your data to allow for auto-integrations with Google Cloud’s machine-learning tools for advanced data science reports.
- A one-click integration with Data Studio means visualizing processed tables is simple and fast.
- ETL solutions like DataFlow and DataProc that take the overhead out of data transformation.
- NEW: BigQuery can now sit on top of platforms like AWS’s S3 to get all BigQuery value without data needing to be in Google Cloud.
Now that you have a high-level understanding of BigQuery, let’s take a deeper dive into some of these uses.
Integrations with Google Sheets and Google Analytics 4 (GA4)
One day you might have your data engineering team working with data and they find insights that are valuable to share with the team, but the team doesn’t have the SQL skills to explore the data like the engineers do. This is where BigQuery’s integration with Sheets sets it apart. With one button click you can load any dataset or query straight into Google Sheets for a NON-SQL like analysis. If your team wants to get hands-on with data, but doesn’t have the SQL skills to write complex queries, they can simply export any table to Sheets and get to exploring.
#New Feature
When writing the 2019 version of this post, this is where I highlighted the Universal Analytics integration with BigQuery—but now we have a new Analytics product. GA4 was actually built to pair with BigQuery, unlike its predecessor. With platforms like Heap disrupting the market with open event-based analytics, Google answered the market with GA4. This maximized the value of the open-event model while keeping the structured and processed data we love, and all that was built to be stored raw in BigQuery. Part of setting up GA4 leads to a very simple way to stream raw data straight into BigQuery. OWN YOUR OWN DATA!
Data Lakes Made Simple
What is a data lake?
A data lake is a place for you to store all of your structured/unstructured data before processing. Step one is usually getting all of your data in one place so we can decide how we want to combine it, enrich it, process it, etc. A data lake is the place where we dump everything until processing. Once we process it for a purpose and store it in processed purposeful form, it becomes a data warehouse.
It’s time to get all of your marketing data, analytics data, and operational data in one place. BigQuery is a product designed for taking on a huge amount of data at very low cost. That makes it perfect for storing large amounts of raw data, regardless of the source. If you don’t have an ETL too, no sweat. BigQuery has built-in ETL for all GMP products and many other data sources. If you are already using an ETL tool, BigQuery is universally supported as a data destination. They also have libraries for picking up data from AWS or Azure if you want to experiment with another cloud.
Data Science and Machine Learning
Storing your data lake in BigQuery gives you an auto-integration to Google Cloud Console’s suite of artificial intelligence and machine-learning tools. This allows you to carry out advanced data analysis, such as customer lifetime value. You can use these products with your data to start predicting data points such as future revenue or product pairing. Google Cloud Console lets you access your BigQuery data in Compute Engine and other server solutions where you can run processes in the cloud without having to pay for expensive machines. Having your data stored within the same system that does your cloud computing keeps you from consuming all of your network traffic while transferring large datasets around—making big data fast and cheap.
#New Feature
BigQuery now supports modeling directly on the platform. This means they handle the entire modeling pipeline for you right in the BigQuery console. Building datasets, training models, storing the model, applying the model to new data, and storing results can now all be set up and automated from within the BigQuery system itself. This is a very cool feature and can massively lower time to value in modeling pipelines, as well as level of effort.
Low-Effort, ‘Sexy’ Dashboarding with Data Studio and Now Looker!
BigQuery makes visualizing your data a high priority. So much so, in fact, that they put a button right on your tables that takes you straight to Data Studio where you can start aggregating and visualizing your data points in seconds. There is no need to set up database configurations or deal with VPNs. Google took out all of the steps between storing the data and visualizing the data to get you faster visualization. Data Studio is not only accessible from any BigQuery table, but also allows you to customize your reports to your brand, making them both “sexy” and valuable.
[Read More: 5 Questions to Ask Before Buying that ‘Sexy’ New Marketing Dashboard]
#New Feature
Since the original writing of this article, Google has purchased Looker and integrated the platform into their cloud. Looker is a more robust visualization tool than Data Studio that compliments the platform nicely. For quick, smaller scale dashboards, I would still recommend Data Studio, but if you’re building larger dashboards meant for analysis and data exploration then Looker is the way to go. Looker has a deep library of functionality that makes exploring data intuitive and fast. Looker integrates closely with BigQuery so getting your data in BigQuery gives you many visualization options.
DataFlow and DataProc Make Processing Tables Simple
If your data needs to be processed before it can be stored, you need an ETL solution to process your data in an automated way. ETL (extract, transform, load) is a system that will read, process, and load your data into any source. Google Cloud Console’s DataFlow and DataProc are two ETL solutions that connect natively into BigQuery. DataFlows Apache Beam and DataProcs Hadoop help distribute processing for streaming and stored data so you can set up your data pipes and never touch them again. No matter how complex the data, Google’s ETL solutions will help you process your data into BigQuery to make storing data simple.
BigQuery in Multi-Cloud
#New Feature
If you are excited about any of these features but don’t know how to get your data into BigQuery, you have a few options. In BigQuery’s built-in data transfer system you can move data from all major clouds to BigQuery. However, sometimes your teams don’t want that to happen so BigQuery has released a feature where you can query, model, visualize, and actionize data through BigQuery even if it’s not stored in BigQuery. You can now sit BigQuery on top of platforms like AWS’s S3 and use its key features without having to move your data.
Learn More about BigQuery
If you have more questions about BigQuery and how it can be utilized to help your organization, reach out to the Google Marketing Platform Certified Partners at InfoTrust today.
