Google BigQuery
Google is the flagship subsidiary and company that led to the creation of Alphabet, which is the third largest tech giant in the world. Google generates most of its revenues from its context-aware advertisement business through Google search, Google Ads and YouTube. As Google is all about data, the company has unmatched expertise and success in the management and monetization of data, and also in research and development of all the methodology and technology that is required to manage data at truly enormous scale. Google is constantly expanding its global footprint. It currently has over 150,000 employees and more than 150 offices and data centers around the world.
Google Cloud Platform is Google’s cloud offering for enterprises of all sizes. It is built on the same strong technical foundation and suite of tools Google uses internally for its core services such as Google Search, Google Ads, Google Mail and YouTube. Google Cloud Platform was introduced in 2008 and is a constantly growing suite of IaaS, PaaS and SaaS solutions, which are suitable for any kind or size of use case.
As data is very much the DNA of Google, all its data, data management, analytics, machine learning and AI solutions have a very high reputation in the developer and data engineering community, as well as with many companies focused on delivering modern, cloud-native, data-driven business solutions and services.
Google Cloud Platform is said to be the third biggest player in the cloud services business, behind Amazon AWS and Microsoft Azure. It does not yet offer such a broad and deep portfolio of cloud services as Amazon AWS and Microsoft Azure, but what it does have is recognized as best-in-class technology, open, very developer-friendly and highly scalable out of the box. Google BigQuery is no exception.
Google BigQuery was first introduced in 2011. It is a serverless data warehouse PaaS solution offering data analysis and machine learning capabilities up to petabyte scale, but also perfectly complements small data analytics use cases. Technically, BigQuery is built on Dremel, a Google-developed distributed column-based data management system, both for batch and interactive querying of very large datasets. The fact that Google uses BigQuery extensively throughout its own solutions and service portfolio can be taken as proof that it works well, and at any scale. A good example is Google Ads, Google’s online advertising platform and main source of revenue ($209 billion in 2021). Google Ads users can easily access all their data through Google BigQuery in real time. Every click made by customers is instantly (with very low latency) available for analysis in Google BigQuery. As such capabilities are essential for digital business and have even become business-critical when used for fully automated real-time campaign management, Google BigQuery and the entire Google Data Platform ecosystem is designed to cope with very large and demanding use cases (e.g., Spotify). This also makes Google BigQuery a contender in the cloud-based enterprise data warehouse market and especially the analytics market. Direct integrations to end-user tools such as Tableau, Power BI, Qlik and many others (even Excel) are available. In addition, most modern data processing, analytics and AI tools support direct access to data from Google BigQuery, and APIs are provided for all major programming languages to build data-driven solutions.
Google pursues the strategy of being an operating platform for data apps and focuses on openness and the ability to be integrated. Customers should be able to build good solutions with Google products, but also with partner products. This is another principle that Google BigQuery follows. For more advanced use cases such as ML/AI or for building reporting and dashboard solutions, the product does offer basic functions, but in more complex tasks it also likes to refer customers to third-party technologies and offers the appropriate connectivity. Accordingly, the portfolio of supported technologies will be further expanded in order to enable, for example, additional reporting tools to have high-performance and full access to Google BigQuery functions.
User & Use Cases
As already indicated, Google BigQuery is what it is: a raw but powerful cloud-based data management and analytics platform. Therefore, the top two tasks listed in the chart are very much in line with its intended use. 89 percent of survey respondents use it for data warehousing/BI, 56 percent deploy it as a data lake and 56 percent use it as a query engine. It should be noted that Google BigQuery itself is not really a data lake solution as its focus is on structured data in columnar storage formats for fast access and querying. However, in the overall Google Smart Analytics ecosystem, there are other components such as DataProc (Hadoop, Spark-based data processing) and Data Flow that do the job.
Interestingly, Google BigQuery usage is evenly spread across small, medium and large companies and it is used company-wide as well as for specific use cases or in one division. The reason for this is quite simple: its use cases vary from straightforward Google Ads scenarios to complex processing of petabytes of data as Spotify does.
In addition to its core capabilities, Google BigQuery has also become increasingly valuable as a back end for machine learning and AI use cases, as implied by the 44 percent of users who already use Google BigQuery for these purposes. This is also the direction in which Google is pushing its platform, making Google Smart Analytics a universal data-driven platform for any analytical use case.
Use cases
n=36
Extend of usage in the company
n=20
Total number of users per company
n=36
Total number of developers per company
n=20
Company size (number of employees)
n=36
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