Analytics have become such an important aspect in a company’s online success, it’s imperative to know how your company is performing in the digital world, which includes measuring your online success from the total number of users on your website, to how long your users spent on your website and which pages they were most interested in, as well as which channel they used to get to your site, which in the long run is all instrumental in the ongoing success of your company.
This is where BigQuery comes in! BigQuery is Google’s cloud-based data warehouse that can be used to perform analytics. To execute queries in BigQuery, Structured Query Language (SQL) is a popular language amongst those in the IT world and it is used to execute queries, therefore making it easy to use by most data analysts. One of the core and exciting features of BigQuery is that it is serverless and fully managed by Google Cloud enabling users to focus less of their attention on provisioning resources and more on their use case. BigQuery can be used to perform analytics on real-time, dynamic data and possesses built-in Machine Learning (ML) (a field of artificial intelligence (AI) that keeps a computer’s built-in algorithms current) capabilities in its BigQuery ML offering.
BigQuery ML is a great tool, especially for those users that would like to perform ML on their existing SQL databases in their specific environments, it not only allows for users to build and execute ML models using SQL queries, BigQuery ML also supports a number of ML algorithms, including, Linear and Logistic Regression, K-means Clustering, Neural Networks and many more too!
Let’s look at BigQuery in more detail shall we:
Linear Regression is an algorithm in ML that is used to predict numerical values, otherwise known as continuous target features. For example, linear regression can be used to predict the rental price for apartments based on certain descriptive features such as, the number of bedrooms, which floor it’s on, WIFI bandwidth availability etc.
A Logistic Regression model can be used to perform classification and predict categorical target features. For example, a logistic regression model can be used to determine whether a generator is faulty or in good-condition based on its runtime, current loss and other descriptive features.
K-means Clustering is an ML algorithm that allows users to perform data segmentation which can be very handy when used to build recommendation systems.
One of the most useful features of BigQuery ML is that it allows users to import existing, trained Tensorflow models into BigQuery, where prediction can then take place. ML usually requires a significant amount of knowledge about different ML libraries and packages such as Tensorflow and Keras. (Keras is a deep learning library that performs ML using artificial neural networks through a Python interface that the user can interact with.) BigQuery ML, however, does not require the user to have a significant amount of experience using ML libraries and tools, but rather allows users to build ML models quickly without having to manage their infrastructure, and performs some data preparation on behalf of the user. Amazing, we know!!
Some of the advantages of using BigQuery ML include:
- It enables users to easily build and run ML models without having to adapt their current database environment.
- It does not require it’s users to have knowledge in specialized programming languages since it uses SQL.
- It allows for the quick creation and implementation of ML models without having to export your data from a data warehouse since BigQuery ML can be applied to a dataset in its current environment, therefore being less time-consuming and requiring fewer tools.
Some of the applications of BigQuery ML include:
- Fraud detection
- Customer Segmentation
- Product Recommendation Engines and Forecasting
It is evident how widely applicable BigQuery ML is and how it can be used to ensure that your business runs effectively, allowing you to make well-informed business decisions and save a significant amount of costs for your company in the long run!
Allow us to accompany you on your journey with BigQuery, contact us today on email@example.com