Let’s find out more about the Big Data management tool proposed by Google
We have already talked about Google Data Studio, a data visualization tool with which it is possible to build Business Intelligence close to key users, even in a “self-service” format, with the possibility of data management through a customized dashboard.
Like any visualization tool, analytical power is also highly dependent on data preparation.
Google BigQuery is the Data Warehouse as a service proposed by the Google Cloud Platform: through the Platform you can directly access the tables and functionalities of the tool totally Cloud. It is perfectly complementary to Google Data Studio because of its considerable capacity for data management and data preparation.
When we talk about dealing with infinite amounts of data, surely the first name we can think of is Google: just think of the business with which it was originally born, the world’s highest performing search engine capable of handling millions of results for any keyword.
BigQuery was born in this very area, out of an internal development need of Google: because of its excellent features, it has been proposed as a Cloud tool for companies in continuous development and expansion since last decade.
The benefits of Google BigQuery
The first major advantage is complete scalability, both from a performance and cost perspective: BigQuery is a fully pay-per-use service. The cost items are a direct consequence of the mass of data ingestion, amount of data historicized, and volume affected by queries.
With BigQuery, therefore, there is no choice of scaling, license limit or down-time to increase resources: scalability is automatically managed and guaranteed by Google, with total transparency of who is using the tool.
BigQuery: Google makes the infrastructure aspect simple
Any infrastructural aspect is totally transparent to the user of BigQuery: the interface is from the first minute ready to use, where you can find the resource list, the SQL query editor, and more advanced features such as scheduled queries.
Through Google users, with whom the Google Cloud Platform is accessed, it is possible to manage full profiling on data access at all levels: table, single column and/or even single row.
BigQuery in Google’s Data Strategy
Looking at any data architecture proposed by Google, it becomes very clear how Google BigQuery is the central element of the Mountan View company’s data strategy: streams of data of any nature, including IoT, converge toward it. From BigQuery one can then start to perform analysis, transformations or get to integrate predictive logic via AI Plaftform, where Machine Learning models can be developed.
Google BigQuery can also be leveraged within a hybrid platform, whether it is multi-cloud or even part cloud and part on-premises. In fact, this powerful tool has connectors for direct interfacing with all major Analytics and Data Visualization tools, including Open Source.
Everything in “Google BQ,” as the service is sometimes colloquially called, is designed to make it as easy as possible to transport data from any standard relational DBMS, such as SQL Server or MySQL, but also from SAP systems.
To complete the overview of Google’s Data Strategy, we finally mention the following BigQuery ML, a service that, with similar assumptions, offers Machine Learning capabilities for the creation and use of models. In this context, the use of this resource is primarily predictive. For example, it is possible through BigQuery ML, to create predictive models for future sales, customer segmentation, or, in general, future probability projections.
How to get started with Google BigQuery
As with most of the Google Cloud Platform tools, there is a free usable slice in BigQuery where you can start developing a pilot project to touch on the benefits of the world’s leading Data Warehouse-as-a-service.
Contact us to find out how Regesta LAB leverages this powerful tool for data management projects in manufacturing and to bring the revolution to your company!
