In this article, I will go into depth on the functions of Google Analytics and BigQuery. I will then highlight the similarities and differences between these two tools. Finally, I will discuss the advantages of using the two tools simultaneously and the process of doing so.
Overview
What is Google Analytics?
Businesses monitor their websites and perform analytics on site metrics to make inferences that can help them perform more effectively. Google Analytics is a tool provided by Google that allows for its users to track and generate customized reports on the results. Businesses can use these reports to tailor their site to better meet their visitor’s requirements or use it as a gauge on how effective their strategy is at reaching their target demographic.
What is BigQuery?
BigQuery is another tool provided by Google. BigQuery stores and allows querying of massive data sets, very much like a relational database manager. However, Google provides the infrastructure to store and process these large datasets. Within this tool, data is able to be manipulated and investigated.
How are they used together?
Google has created these tools to complement one another. Rather than relying solely on Google Analytics’ API’s to access, process, and report data, raw data is able to be collected from Google Analytics and exported to BigQuery. When data is exported to BigQuery from Google Analytics, it is stored in a query-able data containing attributes such as: visitor information, pages viewed, time spent on each page, etc.
Google Analytics
Google Analytics allows users to track and generate customized reports from site visitor metrics. This data is accessed by the use of APIs and data collection is conducted in an open measure protocol. Therefore, any third party system that is connected to the internet is able to be collected. Custom variables (such as unstructured data) are also able to be collected. After collection, data is cleansed and transformed to match existing data parameters. If the data set being analyzed is very large, reports generated from Google Analytics are based on sampled data. Google Analytics is crucial in evaluating the effectiveness of a site.
If there are clear problems with a business’s site or an opportunity for improvement, Google Analytics will provide the data and management tools in an organized view.
BigQuery
BigQuery is a server-less data warehouse tool designed and managed by Google. While traditional data warehouses require administrators and monitors to function, because BigQuery is managed by Google, users do not need to worry about infrastructure. BigQuery stores data in SQL query-able tables and allows for powerful and comprehensive analytics. Data can be imported into BigQuery at lightning speeds from multiple sources and is able to be processed in real-time.
BigQuery was introduced in 2011 as a solution to querying massive amounts of data. Traditional database management systems can take potentially hours to query information. However, BigQuery utilizes Google’s infrastructure so queries can be completed at a much faster rate. This information is than able to be used with various data visualization tools to provide insights for decision-makers.
Google Analytics vs. BigQuery
As with any tools, Analytics and BigQuery have pros and cons associated. Every business requirement is dependent entirely on the situation, and Google has tools that are catered for every scenario. When considering either Google Analytics or BigQuery, it is important to examine the pros and cons of each.
Google Analytics
Pros:
- Activity: Excellent at tracking and storing website activity. A business would be able to see who accessed what page in a site when, and for how long.
- Features a built-in goal tracker. If a business’s objective is to increase traffic to a certain page, the goal tracking feature would allow them to monitor their progress and see what needs to be done to reach it.
- Compatible with tools such as AdWords. When a visitor is brought to your sight via AdWords, Google Analytics is able to track this and report which link they came from.
- Filters: Has variable filters to exterminate redundant or useless information. For example, there is a filter to remove internal traffic so it does not skew your data.
Cons:
- Sampling: Rather than providing reports on the entire data set collected, Analytics only reports on a measured sample of the data. Therefore all possible data is not being investigated and possible inferences may be missed. However, a solution to this issue would be to export the data to another tool like BigQuery.
BigQuery
Pros:
- Compatibility: BigQuery is able to be used with other data sets and visualization tools. This includes Google Analytics.
- Storage: Terabytes of data are able to be stored on BigQuery. Also, there are favorable long-term storage pricings. For every 90 days that data is stored within BigQuery, storage costs are reduced by $0.02 a GB a month.
- Un-sampled: Uses all possible data in its queries. Therefore, you are not making assumptions on test data, but the whole dataset.
- Speed: Can process terabytes of data in seconds. Constraints of traditional hardware do not affect this cloud-based data warehouse.
Cons:
- SQL: Requires that its users know SQL before it can be used.
- Tables: Users need to have in-depth knowledge on the structures of the tables within BigQuery in order to make useful queries.
From Google Analytics to BigQuery
Why use them together?
After reviewing the strengths and weaknesses of each tool, there are advantages that stand out in using both of these tools simultaneously. Google Analytics is great at collecting data and filtering out data that is not useful. However, it lacks in full-analytic capabilities because it uses sampled data to generate reports. Luckily, it is compatible with an external warehouse and analytics tools like BigQuery. BigQuery is able to process massive amounts of data quickly and is compatible with advanced visualization tools to get the most out of the data collected.
How to load Google Analytics data into BigQuery
A Google Analytics 360 account is required for any users that are considering using both of these tools. Both tools will charge customers to store data, however, premium members belonging to Google Analytics 360 will have a $500 credit a month that can be routed to these costs.
To link these two tools, a user first needs to create a Google-APIs-Console project and enable BigQuery. Then, they must prepare their data for exportation. Once it is prepared, the user must sign into Google Analytics and access the admin options. From these settings, they can link to BigQuery.
Closing thoughts
Both Google Analytics and BigQuery are powerful tools. However, I believe that they are most effective if they are used together. They are easily integrated and their strength play on each other’s weaknesses.
Bibliography
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Manate, Daniela. “Google Analytics vs BigQuery: What is the better tool for my data? Advantages and downsides of each solution.” Cognetik Blog, Cognitek, 11 Apr. 2017, blog.cognetik.com. Accessed 24 Sept. 2017.
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