Since its November 2011 launch of the BigQuery architecture as an enterprise-wide data warehouse, Google has been bringing innovations in the field of data analytics to make it more easy and accessible for business enterprises. The success of Google BigQuery architecture since its inception has been marked by its ability to execute fast and ad-hoc queries on multiple datasets with petabyte-sized data sizes.
All about Google BigQuery
As a cloud platform-based big data analytics service, Google BigQuery was designed to perform analytics of read-only data from billions of data source rows using an SQL-like syntax. As a service, it runs on the Google Cloud Storage platform and can be invoked through REST-based API framework.
Additionally, with no servers required to host the web service or any database requirements, BigQuery enables the facility of data warehousing as a service, enabling customers to only pay for the data being processed.
Applying Machine Learning to Data Analytics
As a branch of artificial intelligence (AI), machine learning has emerged in recent years as an effective tool to derive business insights from big data. Prime examples of machine learning are the self-driving Google car and online product recommendations for customers on Amazon and Netflix.
However, many business enterprises using Google BigQuery web service are not able to leverage the benefits of machine learning in developing a deeper understanding of their generated data. This is because even though most data analysts or scientists are proficient in SQL, they are not adequately skilled in data sciences to be able to build complex data models based on predictive analytics.
Introducing Google BigQuery ML
With the introduction of Google BigQuery Machine Learning (ML), Google has announced the capability of BigQuery to design and deploy ML-based models on both structured and semi-structured datasets. BigQuery ML contains a set of SQL-based extensions that can be used to utilize ML functionalities. This allows data scientists and analysts to perform predictive analytics on the data source using machine learning.
So, why is BigQuery ML such an exciting prospect for the future of data analytics? Here are some of its capabilities:
Easier application of machine learning
BigQuery ML provides the capability of applying ML automatically and seamlessly to Big Data. It provides a single environment for performing data analytics, storing data, and executing predictive analytics.
Use of the gradient descent method
The use of the gradient descent optimization method in machine learning can be used to easily redesign the existing BigQuery SQL engine for BigQuery ML. BigQuery ML uses the batch variant of gradient descent instead of the stochastic version, which allows it to effectively scan large dataset volumes instead of drawing smaller and random data samples.
BigQuery clustering capability
BigQuery ML allows the creation of clustered tables which can accelerate SQL query performance, improve their efficiency, and are also cheaper to implement. This is particularly useful when performing quick analytics on large datasets such as those of ad impressions, IoT devices, and point-of-sale (PoS) transactions. Clustered table are also more cost-effective as BigQuery ML queries and charges only for the scanned data, instead of the entire table.
Integration with Google Sheets
Using BigQuery ML, you can now get valuable data insights using the Google Sheets data connector. It allows you to access and refresh data retrieved from the Google Sheets application. You can also leverage on capabilities of Google Sheets such as Explore to generate charts and pivot tables.
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