BigQuery is a columnar database designed for fast and efficient processing of large data sets.

Quick execution of complex SQL queries.

The main goal of BigQuery is to enable users to quickly execute complex SQL queries on large volumes of data without managing hardware or software infrastructure.

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Process large data sets in a simple way.

Seperation of memory and processing

In BigQuery, memory usage and processing power are treated separately, allowing flexible management of the platform to achieve the expected performance.

Data modeling and SQL language

In BigQuery, we operate with the standard SQL query language, which makes it very easy for anyone with previous experience with data modeling and querying to get started.

Real-time processing

BigQuery enables real-time data analysis, allowing you to monitor and analyze data on the fly.


BigQuery is scalable and flexible, which means you can easily adapt your computing resources to the amount of data you are processing. You can easily handle huge data sets.



BigQuery offers security features such as line-level access management, access controls, and the ability to use Google Cloud IAM (Identity and Access Management) authentication.


Processing of multiple data types

BigQuery supports a variety of data types, including structured, semi-structured and unstructured, allowing flexibility in the area of data storage and analysis.

Processing of multiple data types


Payment for actual use

BigQuery's pricing model is based on actual resource usage, allowing for flexible and efficient cost management.

Quick SQL queries

BigQuery offers very fast SQL queries, allowing users to process data efficiently without having to wait long for query results.

Integration with Google Cloud Platform

BigQuery easily integrates with other Google Cloud Platform services, allowing you to take advantage of the full ecosystem of tools available in the Google cloud.

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Google BigQuery is a fully managed, serverless data warehouse solution provided by Google Cloud Platform for storing and analyzing huge data sets using SQL queries.

Google BigQuery was designed with scalability and performance in mind, allowing users to quickly analyze terabytes of data without managing infrastructure.

Google BigQuery supports structured, semi-structured and unstructured data formats, making it versatile for storing various data types, including JSON, Avro and Parquet.

To get started with Google BigQuery, all you need to do is create a project in Google Cloud Platform, enable the BigQuery API, load the data into BigQuery tables and get started. At Vizyble, we can help get the whole process up and running.

Among Vizyble's clients, marketing agencies, technology companies and organizations using the Google Workspace ecosystem use BigQuery most often. Google's unified office environment combined with Google Cloud Platform provides a lot of opportunities for analyzing internal data. Marketing agencies appreciate the use of BigQuery together with the free Looker Studio.

Although Google BigQuery handles large-scale analytical workloads very well, it is not optimized for real-time analytics due to its batch processing nature.

Google BigQuery offers multiple layers of security, including encryption at rest and during transmission, precise access control, and integration with identity and access management (IAM) policies.

Yes, Google BigQuery seamlessly integrates with other Google Cloud services such as Cloud Storage, Dataflow and Dataprep to create a comprehensive data analytics ecosystem.

Yes, Google BigQuery ML allows users to create and deploy machine learning models directly in BigQuery using SQL queries for predictive analytics.

Google BigQuery can handle data sets of virtually any size, with no limits on the amount of data that can be queried in a single SQL statement.

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