Discover all the possible integrations for Google BigQuery

Data Warehouse Google

Connect Google BigQuery with your favorite tool in a few clicks. Choose between over 370 integrations to get the most out from your Google BigQuery data.

Google BigQuery is an enterprise warehouse that helps to store and query huge datasets. This is done through the use of lightning-fast SQL queries and Google’s infrastructure. The service helps to solve the problem of spending time and enormous budgets on data storage and query. Ingesting of data into BigQuery managed storage is easy and can be done in different ways depending on data origin. For instance, data sources from GCP such as Cloud Logging and Google Analytics can be done through direct exports to BigQuery. Second, data can be ingested into BigQuery from Google SaaS apps, Amazon S3, and several data warehouses through BigQuery Data Transfer Service.

Google BigQuery is a serverless and cloud architecture helps to decouple storage and computer and allows them to scale on their own on-demand. This makes it less expensive for customers, as they don’t have to keep data resources running every time. With this architecture, users can bring data of any size into the warehouse and analyze them using SQL without being concerned about database operations and system engineering.

Google BigQuery is cost-effective as it operates a pay-as-you-go model for storage and querying. This means the prices depend on the amount of data you stored in the service and processed by each query you run. However, big companies can request a flat-rate price in order to get dedicated resources specific to business needs.

Google BigQuery is built with connectors to effective EFL tools which enrich data with the aid of the built-in Data Transmission Service (DTS) that helps to move data in between data storage types. It also has BigQuery ML that allows users to design, run and test machine learning models with the aid of standard SQL queries available on the platform.

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Google BigQuery is a managed service that runs automatically, giving you the opportunity to focus on other aspects of your business. Because it delivers updates automatically, you don’t have to manage any infrastructure. Google takes care of all maintenance, patching, and upgrades. This makes it effective for businesses.

With Google BigQuery, your data is secured. It comes with built-in DR and data protection. This means that Google BigQuery helps to protect and keep the integrity of users’ data so that it is accurate and reliable. This is done through automatic data replication for in-built disaster recovery (DR) and availability that ensures uptime in case there is downtime or breaches.

When businesses integrate Google Big Query with LeadsBridge, companies can collect and nurture leads from this data warehouse in a streamlined and effective manner.

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Google BigQuery features:

Dremel

Google BigQuery requests are carried out through the Dremel query engine. The Dremel feature shares slots to queries as needed. It maintains fairness between multiple users who are using the query at once. For example, a single user can be allotted up to thousands of slots to run queries.
Colossus

Google BigQuery is dependent on Colossus, which is a distributed file system. Colossus is responsible for replication, recovery, and distributed management. Every Google data center is built with its own colossus cluster. Every cluster contains disks to provide every user with thousands of dedicated disks per time.
Jupiter Network

It is the internal data centre network that allows Google BigQuery to separate storage and compute.