Why use Google BigQuery?

Why use Google BigQuery?

Google BigQuery is a cloud service for data analytics, allowing users to process massive amounts of data in real time and use the collected data to create predictive machine learning (ML) models. Biq Query charges on a “pay-per-use” model, which means we only incur costs for the resources we actually consume. This is a very big plus because the infrastructure and resources used in cloud technologies can be very expensive. One of BigQuery’s main strengths is its ability to process data in real time. This means that data can be analyzed on the fly, which is extremely important in today’s rapidly changing business environment.

BigQuery is extremely scalable. The solution can process data of any size, from small data sets to petabytes of information, without having to worry about infrastructure. BigQuery integrates with other Google Cloud tools and services, such as Google Cloud Storage, Google Cloud Dataprep, Google Cloud Machine Learning Engine, Google Sheets, Google Looker Studio and Google Analytics. The integration of Google BiqQuery with other Google solutions enables the creation of a comprehensive analytics ecosystem within an organization. Google BiqQuery thus provides an end-to-end analytics solution within a single, purpose-built platform.

A major advantage, giving flexibility in data management, is that BigQuery supports the SQL query language. As a result, even people without specialized programming knowledge can easily use this tool by creating SQL queries directly to individual data sets. Google cares about data security. BigQuery offers advanced access management mechanisms, encryption of data in motion and at rest, and monitoring of user activity.

 

Google BigQuery applications

In the analytics community, BigQuery is used to analyze large data sets, speeding up research processes and enabling the discovery of new patterns or relationships. The tool also works well in the process of analyzing data from IoT devices.

Thanks to its built-in geospatial processing functions, BigQuery can also be used to analyze location-related data, which finds applications in navigation, logistics or spatial planning, among others.

 

Machine Learning in Biq Query

BigQuery ML allows you to create and run machine learning (ML) models using GoogleSQL queries. It also provides access to LLM and Cloud AI APIs to perform artificial intelligence (AI) tasks such as text generation or machine translation.

Typically, performing machine learning (ML) or artificial intelligence (AI) processes on large data sets requires advanced programming and knowledge of ML frameworks. These requirements limit the development of solutions to a small group of people in each company and exclude data analysts who understand the data but have limited ML knowledge and programming experience. However, with BigQuery ML, those familiar with SQL query language can use existing tools and their own SQL skills to create and evaluate models and generate results from LLM and Cloud AI APIs.

BigQuery ML enables the use of machine learning and artificial intelligence by allowing data analysts, the primary users of the data warehouse, to create and run models using existing business analytics tools and spreadsheets. Predictive analytics is critical because it can guide business decision-making across an organization. In Biq Query, there is no need to program an ML or AI solution using the programming languages used in ML model development: Python or Java. Learning ML models and accessing AI resources is done using SQL – a language familiar to data analysts.

Google Biq Query currently offers its users the following built-in ML models:

  • Linear regression is used for forecasting, for example, this model can predict the sales of an item on a given day.
  • Logistic regression, on the other hand, is used to classify two or more possible values. For example, the model can classify inputs as low, medium or high.
  • K-means clustering is an unsupervised learning technique used to segment data. For example, the model can identify different customer segments.
  • Matrix factorization is used to create product recommendation systems based on customer behavior history, transactions and product ratings.
  • Principal component analysis (PCA) is the process of reducing the dimensionality of data by calculating principal components and projecting data into these components. This tool is particularly useful for preserving relevant information while reducing the number of dimensions.
  • Time series model is used for forecasting from sequential data. In this case, we can create advanced time series models that automatically take into account anomalies, seasonality

 

Built-in ML models in BiqQuery

Built-in ML models in BiqQuery

Source: Google Cloud materials

 

Data security in Biq Query

BigQuery Studio allows you to get reliable information from trusted data. Data professionals have the ability to monitor the source of data, profile data and implement data quality restrictions to ensure data quality, accuracy and reliability.

In addition, BigQuery enables administrators to implement a uniform security policy for the datasets used, reducing the need to copy, move or share data outside of BigQuery. Thus, there is no need to manage additional external connections. Using simple SQL queries in BigQuery, data analysts can take advantage of the Machine Learning predictive models discussed above without having to share data with third-party services.

The Future of Data Analytics with Google BigQuery

Google BigQuery is constantly evolving with new features and improvements. Looking ahead, expect even more integration with other cloud tools, the development of real-time data processing capabilities, and the adaptation of the tool to a growing number of industries and applications.

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