Google is the flagship subsidiary and company that led to the creation of Alphabet, which is the third largest tech giant globally, with a market cap of $1.4 trillion and $40 billion net income in 2020. 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 140,000 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 offer is recognized as best-in-class technology, open, very developer-friendly and highly scalable out of the box. Google BigQuery is no exception here.
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 ($146 billion in 2020). 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.
User & Use Cases
This year we had 36 responses from Google BigQuery users. At the time of the survey, all of them were using the cloud version.
Extend of usage in the company
Total number of users per company
Total number of developers per company
Company size (number of employees)
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