Cloud Data Warehousing Solution using Google BigQuery

Overview

Google BigQuery, introduced in 2011, has evolved into a distinctive analytics data warehousing solution. Its serverless architecture enables scalable and rapid SQL analytics across large datasets. Over the years, continuous enhancements have enhanced efficiency, security, and user experience.

Data Warehousing with BigQuery: With its advanced features, BigQuery streamlines storing and analyzing massive datasets, making it a vital asset for today’s data-driven organizations.”

How does a Data Warehouse drive the business decision?

A data warehouse aggregates data from various sources and runs analytics to bring value to business operations by offering insights. For the previous two decades, data warehouses have kept the most critical business data in the corporation. As businesses become more data-driven, data warehouses are increasingly important in their digital transformation path.

Data warehousing has evolved beyond its traditional role in operational reporting, as modern enterprises require more sophisticated and comprehensive functionalities from their data warehouses. Today, enterprises need to:

• Have a 360-degree view of their businesses – Data holds immense value, enabling enterprises to understand their business operations fully. As the cost of data storage and processing continues to decrease, organizations increasingly seek to process, store and analyze all relevant internal and external datasets to their company to achieve a 360-degree view of their business.
• Reduce time to insights – To stay competitive in today’s fast-paced market, businesses need to be able to launch rapidly without enduring lengthy waits for the installation and configuration of hardware or software.
• Secure their data and govern its use – To ensure that data is utilized effectively, it is imperative to safeguard it and make it accessible to relevant internal and external parties.
• Make insights available to business users to enable data-driven decision-making across the enterprise – To foster a data-driven culture, enterprises must democratize access to data, making it easily accessible to all organization members.

Traditional data warehouses were neither developed for evolving data processing patterns nor intended to accommodate the tremendous expansion of data.

BigQuery – Cloud Data Warehouse

Google BigQuery was designed as a “cloud-native “ data warehouse. It was created to meet the demands of data-driven enterprises in a world that prioritize the cloud.

BigQuery is the cost-effective, serverless, and highly scalable cloud data warehouse offered by GCP. It uses Google’s infrastructure and computing power to provide incredibly quick queries at a petabyte scale. Customers don’t have to worry about maintaining any infrastructure, so they can concentrate on using familiar SQL to find useful insights without the help of a database administrator. Because clients only pay for the processing and storage they utilize, it is also cost-effective.

Architecture of BigQuery

The serverless architecture of BigQuery separates storage from computation, allowing each to scale independently as needed. Customers benefit from this structure’s enormous flexibility and cost control because they don’t always have to maintain their pricey computational resources in operation. This is considerably different compared to conventional node-based cloud data warehousing solutions or on-premises massively parallel processing (MPP) systems. Additionally, this strategy enables clients of any size to upload their data into the data warehouse and begin performing Standard SQL analyses without worrying about database administration and system engineering.

Source: Google Cloud

How to get started with BigQuery?

BigQuery can be used immediately by loading data and executing SQL statements. There is no need to establish drives, specify replication, arrange compression and encryption, or perform any other setup or configuration work required to build a traditional data warehouse. There is also no need to size VMs, storage, or hardware resources.

There are several ways to access BigQuery

• GCP console
• bq command line tool
• BigQuery REST API
• Client libraries such as Java, .NET, or Python

Conclusion

In this article, we discussed BigQuery basics and its architecture. We also covered different methods to access BigQuery.

Follow the below links to learn more about BigQuery –

• Get started with the BigQuery sandbox
• Try the lab with BigQuery Sandbox to query GitHub data using BigQuery

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