Big Data
Large, varied information sets that are expanding at an exponential rate are referred to as big data. It is used to describe high-volume, high-velocity, and/or high-variety information assets that call for creative, cost-effective methods of information processing to improve insight, decision-making, and process automation.
Google BigQuery is a highly scalable, serverless data warehouse with an integrated query engine. Terabytes of data can be processed by the query engine in a matter of seconds, and petabytes in just a few minutes.This type of performance can be achieved without managing any infrastructure or creating or rebuilding indexes.
Users of BigQuery can handle data using quick SQL-like queries for in-the-moment analysis.
BigQuery features:
- BigQuery makes it possible to analyse data on several cloud platforms. BigQuery offers an innovative method of evaluating data that is present in multiple clouds. This is in contrast to past methods, where migrating data from the source usually came at a considerable cost. BigQuery instead of having to move the data to another zone for processing, can perform the computation on it right there.
- Simple SQL queries are used to build and run Machine Learning models in BigQuery using BigQuery ML. Machine learning on massive datasets required ML-specific knowledge and programming abilities prior to the release of BigQuery ML. BigQuery Ml made it unnecessary for that to happen by enabling SQL professionals to create ML models using their existing expertise.
- A tool for in-memory analysis is BigQuery BI engine. With high concurrency and reaction times of less than a second, it is utilised to examine the data kept in BigQuery. It should come as no surprise that it includes a SQL Interface given that it is a member of the BigQuery family.
- BigQuery Data Transfer is a service that regularly automates the transfer of data into BigQuery. The analytics team can handle this schedule in a straightforward manner without the need of any code. Data backfills can also be added to fill in any gaps or interruptions that may occur during intake.
- A data-warehouse like BigQuery benefits from the location and mapping data that BigQuery Geographic Information Systems (GIS) offers.
BigQuery provides centralized management of data and compute resources while Identity and Access Management (IAM) helps you secure those resources with the access model that's used throughout Google Cloud.