Just like Snowflake Data warehouse, Google BigQuery is a Cloud-based Data Warehouse that provides services to store substantial amounts of data. It works on a server-less design, requiring no provisioning or hardware management from your side.
This allows you to focus on your data and analytics rather than managing your assets. Google BigQuery automatically replicates your data across numerous locations to ensure high availability, scalability, and reliance for your data.
Moreover, unlike its peers, this Google-based tool requires little to no maintenance. You can access its services from all locations where the Google Cloud is available. Google BigQuery also provides seamless integration with other Google-based services like Google Analytics, Google Drive, etc.
Key Features and Advantages of Google BigQuery
The following features and advantages of Google BigQuery make it popular in the business world:
Data Availability
Google BigQuery provides automatic Data Replication. Moreover, it maintains a 7-day log that has information on any change made to your data in the previous week. These features safeguard your information against system crashes.
Low Maintenance
Google BigQuery automates most of your tasks related to Data Warehousing. Querying, Storage Management, Resource allocation, etc. are all inherently managed by Google. This minimizes any human interference required to run your Data Warehouse system.
Smart Pricing Model
Google BigQuery contains a separation between processing and storage functions. Its on-demand services charge only for the storage space used thus enabling it to offer attractive pricing models for both small and large-scale businesses.
Real-Time Insights Using Machine Learning
Google BigQuery’s Machine Learning features offer real-time insights into your data. Its high-speed API builds a solid foundation for faster Data Analytics. This allows you to analyze your business data as soon as it is imported.
High Scalability
Google BigQuery implements parallel processing to provide you with high scalability and consistent performance. It works great for real-time Data Analytics and scales your data to suit your needs.
Use Cases of Google BigQuery
The Google BigQuery Data Warehouse service offers you a great data storage solution. This service works best in the following cases:
1) Working with Unchanged Data
Google BigQuery automates the query execution process. However, when planning to update or delete data, you’ll need to truncate the entire table and recreate the table with new data.
This implies that you should go for Google BigQuery only if your data will remain majorly unchanged, even at scale.
This service charges you based on the amount of data processed. So, unchanged data will result in reduced costs.
2) Working with Cached Data
Google BigQuery is a worthwhile solution if you are using cache for faster Data Access. It has a built-in cache, that allows you to run the same query multiple times if the tables are not modified.
Google BigQuery will utilize the results stored in the cache and will not try to execute the query again. Also, it does not charge money for cached queries, so your monthly bills will be relatively low.
3) Working with Data in Different Formats
Google BigQuery supports data in Structured, Semi-Structured, and even Unstructured forms. So, you can use this Data Warehouse to import data of all formats regardless of its structure.
Furthermore, it provides free ETL tools if you require to transfer data from another Google-based service.
4) Multiple Pricing Plans
Most Data Warehouses offer only a Flat-Rate model that involves fixed monthly payments. However, Google BigQuery requires you to pay based on your usage associated with Storage and Query services. Your storage is billed based on the amount of data you have in the Data Warehouse.
Query costs on the other hand depend on the amount of data processed with each query execution. Even if you have a fixed monthly budget, Google BigQuery can be of use.
You can have the required resources for a monthly fee using its Flat-Rate model.
Conclusion
This blog post introduced you to Google BigQuery and explained its major advantages. Furthermore, it discussed various use cases in which this Data Warehouse is best suited to manage your valuable data.
Google BigQuery provides easy integration options for other Google-based services. However, when it comes to connecting with tools that are outside Google’s ecosystem, a 3rd party ETL Pipeline is your best option.
Hevo Data offers a No-code Data Pipeline that can seamlessly integrate over 100+ Data Sources with your Google BigQuery Data Warehouse in real-time. It will automate your data transfer process and provide you with a hassle-free experience.
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