Fully managed, serverless data warehouse and analysis environment as a Platform-as-a-Service (PaaS). BigQuery is optimized for the analysis of data in the petabyte range with SQL-like syntax.
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Google is the flagship subsidiary of Alphabet Inc., one of the world’s largest technology conglomerates. While advertising remains its primary source of revenue, mainly through Google Search, Google Ads and YouTube, Google’s diverse product portfolio includes cloud services, hardware and numerous online platforms. The company is synonymous with data, boasting unmatched expertise and success in managing and monetizing data and in the research and development of methodologies and technology required to manage data at a truly enormous scale. Google is constantly expanding its global footprint. It currently has over 180,000 employees, 116 offices, 33 data centers and 40 cloud regions around the world.
The Google Cloud Platform (GCP) serves as Google’s enterprise cloud solution, offering a broad range of services from infrastructure to machine learning. GCP, launched in 2008, builds upon the same robust infrastructure and tools that support Google’s core products. While GCP ranks third behind Amazon AWS and Microsoft Azure in overall market share, its focus on data management, analytics, machine learning and AI solutions has a growing reputation in the developer and data engineering community.
Central to GCP’s data offerings is Google BigQuery, introduced in 2011. BigQuery is a platform as a service (PaaS), fully managed, serverless data warehouse and analytics environment. It is optimized for analyzing petabyte-scale data using SQL-like syntax. Additionally, the system trains, runs and manages machine learning (ML) models and data science workflows via BigQuery ML. BigQuery leverages a unique Google-developed query engine named Dremel. Dremel is a distributed column-based data management system for batch and interactive querying of very large datasets.
BigQuery is an important technology used extensively throughout Google for mission-critical applications and services. The fact that the company relies heavily on the technology is an endorsement of its overall functionality and reliability.
Direct integrations to end-user tools such as Tableau, Power BI, Qlik, Domo, SAS, ThoughtSpot and others (including 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’s Cloud partner ecosystem covers every region of the world and numbers in the thousands.
Google is continually improving and expanding its cloud services offerings around data and AI. The company is heavily focused on AI, integrating its foundational LLM model Gemini into BigQuery and Looker with BigQuery Unified Platform. Combining data and AI on the same platform, Google has extended it AI deliverables with Google Model Garden, delivering 130 curated models integrated with Vertex AI Model Builder to fast-track users who need to leverage AI.
BARC’s viewpoint on the product’s strengths and challenges.
The information contained in this section is based on user feedback and actual experience with Google BigQuery.
The information and figures are largely drawn from BARC’s The BI & Analytics Survey, The Planning Survey, The Financial Consolidation Survey and The Data Management Survey. You can find out more about these surveys by clicking on the relevant links.
All key figures for Google BigQuery at a glance.
Simplicity and integration.
Cost.
Consider other tools like AWS and Azure.
Good but sometimes expensive.
It is fast.
Developers are complaining about the SQL dialect.
It works best if you have very large sets of data.
It worked well for our customer's use case, but I would not recommend it to small or mid-size companies.
