Google BigQuery went general in 2011 and has since been positioned as a unique analytics data warehousing solution. Its serverless architecture enables it to run at scale and speed and perform exceptionally quick SQL analytics across big datasets. Since its launch, numerous features and upgrades have been added to increase efficiency, security, and accuracy and make it easier for users to discover insights.
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.
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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
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 –
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Drop a query if you have any questions regarding Google BigQuery and I will get back to you quickly.
1. Why is BigQuery faster than traditional databases?
ANS: – BigQuery stores data in columnar format, which makes it possible to achieve a very high compression ratio & scan throughput. Additionally, tree architecture is used by BigQuery queries for aggregating results across thousands of machines in a few seconds.
2. What is portioning in BigQuery?
ANS: – Using partitioned tables, Google BigQuery provides a serverless method for managing enormous datasets. To make it simpler to organize and query data, some tables have been segmented. Partitions can decrease the amount of data a query needs to read, enhancing query efficiency and lowering expenses.
3. Can flat files such as Excel .xlsx or .csv files be used to add data to BigQuery? Is it possible to upload data from Google Drive into BigQuery?
ANS: – Certainly! You can import data from flat files and Google Drive directly into BigQuery as a table. Simply go to the desired dataset where you want to include the table and click on the “CREATE TABLE” button on the interface’s right-hand side. Once you have selected the “CREATE TABLE” button, you will have access to a range of upload options, including flat file uploads, Google Drive uploads, Google Cloud Storage (GCS) uploads, and Google Cloud Bigtable uploads. Adding this data will be nested under the selected Dataset as a table and can be easily queried.
WRITTEN BY Sahil Kumar
Sahil Kumar works as a Subject Matter Expert - Data and AI/ML at CloudThat. He is a certified Google Cloud Professional Data Engineer. He has a great enthusiasm for cloud computing and a strong desire to learn new technologies continuously.