When it comes to storing large amounts of information on the cloud, everyone is faced with the choice of BigTable or BiQuery. Both services may seem exactly the same at first, so now let’s take a look at the functionality of each of them.
BigTable is a NoSQL database. It is perfect for storing large amounts of information and fast data transfer. It is also a service with strict requirements for the storage of IoT, AdTech or FinTech materials. BigTable is great for heavy read and write operations.
We can characterize BigTable as follows:
- Customizable Bigtable throughput by removing and adding nodes;
- Used as a storage engine for large-scale, low latency applications;
- It is also used for processing throughput-intensive data;
- Offers high availability with a service level agreement.
You can also use a service such as Cloud BigTable – storage in the form of a table with a billion rows, which allows you to store terabytes or even petabytes of information. It is a versatile data source that easily integrates with data tools such as Hadoop, Dataflow, and Dataproc.
Unlike the first service, BigQuery is a cooperative storage of relational data and is more suitable for analysis. BigQuery is SQL powered and is suitable for analyzing Cloud BigTable data.
BigQuery differs in the following factors in comparison with the first service:
- Petabyte-scale storage for storing and visualizing data;
- Designed for stock storage of information from other sources;
- Provides analytical information in real time;
- Supports SQL;
- ANSI compliant;
- Perfect for data analytics;
- Enables scanning of large tables with information;
- Suitable for making requests;
- Includes online analytical OLAP processing.
What do they have in common?
Both services are designed to store your information on a large scale, and are also excellent variants for serving customers. With service updates that do not affect your workflow in any way, you will not notice the change while the services improve.
Both services can also offer unlimited scalability, auto-burn and even restore backups. For high throughput, both services separate processing and storage functions.
If you are interested in information about one of the services or would like to know more details, please contact Cloudfresh. We will help you build integration with one of the services based on Google Cloud fast and with easy adaptation.