Accelerating Data Analytics Using Google BigQuery
Posted By : Anirudh Bhardwaj | 20-May-2020
An increasing number of businesses across the globe are migrating their data to the cloud. In doing so, they avail numerous benefits in the form of increased security, scalability, accessibility, transparency, and interoperability. Data is certainly the most valuable asset for an enterprise. Making the right use of it can significantly improve its inbound/outbound operations and customer service efforts. It brings the discussion to Big Data Analytics, a systematic approach to data analysis for extracting useful information from complex datasets. In general, big data analytics solutions are quite complex and require several interdependencies. In addition, you have to deal with the complexities of data storage, server-based data processing, and maintaining the flow of information.
The advent of cloud computing has greatly streamlined big data analytics by providing businesses access to a variety of serverless features. Google BigQuery is an apt example of a serverless solution that provides a cloud-based analytics platform to accelerate and simplify data analysis. Let’s explore how Google BigQuery streamlines big data analytics and accelerates data processing.
What Is Google BigQuery?
BigQuery is a cloud-based data analytics platform from Google that improves, streamlines, and accelerates big data analytics over a serverless infrastructure. It is a fully-managed cloud service that uses an SQL-like syntax and can be invoked through REST APIs. BigQuery provides a serverless data warehouse with machine learning features to facilitate a fast, scalable, and cost-effective data analysis with the elasticity of the Google cloud. It comes with ANSI SQL query support and is capable of handling enormous amounts of data in terabytes and petabytes.
Key Features of BigQuery
BigQuery is an enterprise-grade cloud service that is extremely useful processing and analyzing massive chunks of data to extract useful information. It acts as a serverless data warehouse with built-in machine learning capabilities to achieve higher performance and process efficiency. Given below are the main features of Google BigQuery that make it an ideal solution for big data analytics.
As already mentioned above, BigQuery comes with built-in machine learning features. BigQuery ML is a serverless web service that enables data scientists to build and deploying machine learning models on structured or semi-structured data. It offers a set of SQL-based extensions to implement machine learning functionalities. As a result, analysts and data scientists can perform predictive analytics on various data sources.
BigQuery BI Engine
As the name suggests, BigQuery BI Engine is a business intelligence-based data analysis service that enables business managers to analyze complex datasets. Businesses can seamlessly integrate BigQuery BI Engine with Data Studio to accurately explore and analyze data for Looker, Google Sheets, and Google’s BI partners.
BigQuery GIS is a geospatial information system that combines the serverless architecture of the Google Cloud with geospatial analysis techniques. In doing so, it provides data scientists the ability to augment their analytical workflows with location intelligence and geospatial analysis. BigQuery GIS lets you analyze location data in an interactive geospatial format including lines, arbitrary points, polygons, and multi-polygons for an augmented view.
The built-in ML features in BigQuery let you create clustered tables to enhance the performance of SQL queries and improve process efficiency. The clustering capabilities in BigQuery come handy while analyzing complex data patterns and large datasets mainly associated with IoT devices and point-of-sale (PoS) systems.
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Google BigQuery is a serverless data warehousing service that takes care of all your data processing and analysis related tasks. It gives you the elastic features of the Google cloud and lets you seamlessly integrate with a variety of Google cloud services to maximize your enterprise benefits. It also provides an intuitive interface for real-time data analysis of enormously large datasets. Above all, it eliminates the complexities of managing servers and provides a cost-effective solution with a pay-as-you-go pricing model.
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