In addition to Google Analytics reports, BigQuery enables querying, processing, loading, exporting, and data visualization of big data.
The platform uses columnar storage, which belarus phone number library enables much faster data browsing, as well as a tree architecture model, which makes querying and aggregating results significantly easier and more efficient. Additionally, BigQuery is serverless and designed to be highly scalable, thanks to its rapid deployment cycle and on-demand pricing.
Data in BigQuery is automatically encrypted at rest or in transit.
BigQuery is based on Dremel technology. Dremel is a tool that has been used at Google for about 10 years.
Dremel: Dynamically allocates slots to queries as needed and distributes them among multiple users querying simultaneously. A single user can have thousands of slots to run their queries. It takes more than just a lot of hardware to make your queries run fast. BigQuery requests are powered by the Dremel query engine.
Colossus: BigQuery relies on Colossus, Google’s latest generation distributed file system. Each Google data center has its own Colossus cluster, and each Colossus cluster has enough disks to serve thousands of dedicated disks to every BigQuery user simultaneously. Colossus also handles replication, recovery (when disks fail), and distributed management.
Jupiter Network: Internal data center network that allows.
The platform’s expanded data capabilities – it’s built to handle big query analytics at petabyte scale – also means it can collect more data from different sources and organize it faster.
