Google BigQuery
A serverless, auto-scaling data warehouse that's also the core of QuickBI's own data warehouse solution.
Overview
Google BigQuery is a serverless, fully managed data warehouse from Google Cloud that separates storage from compute. It lets you run fast SQL queries over massive datasets in standard SQL, without worrying about servers, maintenance, or scaling: BigQuery scales automatically with the workload.
Google introduced BigQuery in May 2010, and it became generally available in 2011–2012. The technology is built on Google's internal Dremel technology, which the company had already used for years to run fast queries over trillions of rows. Since its release, BigQuery has become one of the most widely used cloud data warehouses in the world.
For QuickBI (now Kaivo), BigQuery isn't just one supported technology among many: it's the technical core of QuickBI's own data warehouse solution. When QuickBI builds a data warehouse for a customer, it's built on top of BigQuery, giving the customer the scalability of Google's infrastructure without having to manage it themselves.
Pricing
BigQuery pricing is usage-based: you mainly pay for the amount of data you process and the storage you use. Capacity-based pricing options are also available, which suit companies with steady, predictable usage.
BigQuery components
BigQuery Console : a browser-based interface for running queries, managing data, and visualizing it, directly in the Google Cloud Console.
BigQuery CLI and APIs : a command-line tool and APIs for using and automating BigQuery as part of other systems.
BigQuery ML : lets you build and run machine learning models directly with SQL queries, without separate data science tooling or moving models elsewhere.
BI Engine : a built-in, in-memory accelerator that speeds up queries from BI tools such as Tableau, Power BI, or Data Studio.
BigQuery Omni : lets you analyze data stored in other clouds, such as AWS and Azure, without having to move it.
Why choose BigQuery?
Serverless and auto-scaling
BigQuery scales automatically with demand, from small queries to petabyte-scale analysis, so you never have to size or maintain infrastructure yourself.
Separation of storage and compute
Because storage and compute are priced and scaled separately, you can use resources more efficiently and avoid paying for capacity you don't need.
Standard SQL
BigQuery uses standard SQL, which most analysts already know, making it easier to migrate from other databases.
BigQuery ML
Build machine learning models directly in SQL, without separate data science work or moving data to other tools.
Integrations with popular BI tools
BigQuery works seamlessly with tools such as Tableau, Power BI, Data Studio, and Qlik Sense, so you can use your data with whichever reporting tool you prefer.
Up-to-date data
BigQuery supports streaming data ingestion, so reports can run on continuously updated data instead of relying on traditional batch refreshes.
Comprehensive security and governance
Includes IAM-based access control, encryption at rest and in transit, audit logs, and row- and column-level security.
Global infrastructure
Data can be stored and processed across multiple regions, supporting both performance and data protection requirements in different countries.
QuickBI builds and maintains your BigQuery data warehouse
BigQuery is one of the most widely used and powerful cloud data warehouses in the world, but unlocking its full potential requires a properly built and maintained data warehouse. Kaivo, built by QuickBI, builds and manages your BigQuery data warehouse for you, so you get the scalability of Google's infrastructure without having to know SQL or optimize queries yourselves.
If your goal is a scalable, reliable, and cost-effective data warehouse, get in touch with QuickBI.
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