Are you looking for a comparison of Snowflake vs BigQuery?
The cloud data warehouse has recently become widespread in working with a modern data stack.
Without it, it is impossible to obtain the most valuable information from the total array of information.
The cloud warehouse itself is a special platform that stores information from various sources before it is processed.
By focusing on such data, it is possible to make the right decisions and find answers to relevant business queries. Today, almost every business entity has already armed itself with such a tool as a cloud warehouse or is engaged in its implementation.
There are many key players in this niche, but in our article, we will focus on Snowflake vs BigQuery.
General information about cloud platforms
Snowflake is a SaaS platform that can interact with any of the common cloud providers (AWS, GCP or Azure). It was created explicitly for cloud operations, giving it special components that its competitors do not have. The launch of Snowflake took place in 2014, and since then, the company has been considered a major player in the cloud industry. Its capitalization was more than $90 billion by the fall of 2021.
It is interesting that Snowflake is a program for the cloud, but it is located directly in the cloud. The service may well control the server infrastructure so that customers can extract valuable information from their stored data. With such a service, it is possible to perform a lot of unlimited simultaneous queries.
To contrast Snowflake vs BigQuery: Cloud service BigQuery was launched back in 2010 as part of Google CloudPlatform. This platform was one of the first data cloud warehouse solutions available to a large number of users back then. But at the time, such a program was considered complicated because of the mechanism of query processing itself. However, the service didn’t stand still and evolved and now BigQuery is a completely different solution.
Like Snowflake, there is no need for infrastructure in this service. Users can concentrate on finding the information they need using familiar SQL. But BigQuery is a “native” service of Google, it is not used by other systems.
Features of platform architectures
Snowflake is based on ANSI SQL and is completely devoid of server infrastructure. The system combines classic shared disk and non-shared architectures to give users a better solution. The software provides data access to all nodes of the computer using a central repository for storing information.
Snowflake uses an MPP to process client queries. It turns out that each computer cluster stores a part of the total dataset. For storage, the service puts the information into special micro-partitions where the data is internally optimized and compressed into a columnar format, which then makes it possible to store them in a cloud warehouse
All nuances of information storage are automatically processed in Snowflake: file size, structure, compression, metadata, and statistics. In addition, the system studies other information that is not directly visible but accessible only by SQL query.
Processing of information in such a program is made by the method of “virtual warehouses” or clusters of computers. A warehouse is an MPP that includes a number of nodes. Snowflake manages all the actions to process the whole information fully.
Google BigQuery resembles Snowflake in the absence of server infrastructure and separation from computer resources and in the fact that the service is also based on ANSI SQL. But its architecture is significantly different because BigQuery contains a wide range of services that are managed by Google solutions such as Dremel, Colossus, or Jupiter. The developers put the computing processes on the Dremel multi-user cluster, which is used to query SQL.
In addition, Dremel transforms SQL queries into executable trees, and their leaves are called slots. Each such cell reads data from the warehouse and performs the required calculations. Tree branches or “mixers” process all the queries. One user will need 1000 slots to work comfortably.
Google BigQuery also uses a columnar compression system to store information in Colossus. This system manages data replication, recovery, and management processes so that clients are not dependent on a single point of failure. The service works with Google Jupiter which quickly moves user data from one location to another. The process of allocating hardware resources is the responsibility of Borg.
What about scalability?
Snowflake has an auto-pause and auto-scaling feature that allows the user to start or stop clusters during downtime or workflow. The service will allow the user not to change the size of the nodes but will change the size of the clusters with a single click of the mouse. The program also gives the possibility to expand up to 10 storages with a limit of 20 DML in turn in one table.
BigQuery follows a similar strategy, automatically allocating resources. But this service limits the number of simultaneous users to 100. It turns out that both platforms can increase or decrease the scale automatically depending on user requests. Snowflake will also allow to isolate workflows between different companies on different cells.
Features of platform security and data support
One of the fundamental features of Snowflake is the automatic encryption of data without accessing the system. But the service does not give permissions for columns and other objects. At the same time, BigQuery protects information at the level of columns, arrays, individual tables, and control elements.
Since BigQuery is a Google application, users can use other cloud warehouse services from that system that have BigQuery authentication. Snowflake does not offer these solutions, but it can be hosted by AWS, and then, the integration problems will be solved.
BigQuery will allow customers to use the virtual cloud. In addition, both named services comply with all internationally accepted standards HIPAA, ISO 27001, PCI DSS, and others. Both platforms are compatible with structured and partially structured information. Snowflake began supporting unstructured data in the fall of 2021, making it available as part of a preliminary version.
Administering
Cloud systems BigQuery and Snowflake are endowed with capabilities to manage user profiles, permissions, and security of stored information. All settings are made automatically. As soon as the volume of information grows, the sites can automatically scale up in the background on the client’s query.
Solutions such as a SaaS service that performs all maintenance for users are also offered. BigQuery automatically processes all information, and Snowflake gives administrators the ability to independently scale computing levels and storage. Additionally, BigQuery offers the ability to connect BigQuery to Excel, enabling smooth data integration and analysis directly within the Excel environment  Â
Information security
Both services have proved to be excellent in terms of the security of stored data. Snowflake keeps information secure with Time Travel and Fail-safe. The first option will save the state of client data before activating updates, the standard retention period is one day. This option is applied to databases, tables, and schemas.
The second option is designed to restore the historical data. This period is not configurable, it starts immediately after the end of storage in Time Travel. The user will have to request Snowflake in data recovery, and the option will be able to put back any information lost for various reasons.
BigQuery has made it even simpler. System administrators have the ability to cancel changes. The system can keep a 7-day history of all changes to the tables. But in order to keep tabular data for more than 7 days, the service has an option of instant table capture.
A few words about productivity
The efficiency of Snowflake lies in the short query time, a point where it is superior to BigQuery, despite their obvious similarities. The latter is more productive when the user performs plenty of queries with significant downtime. But if the client is always busy, it is better to use Snowflake.
Snowflake vs BigQuery: The main differences
Despite the many common features between Snowflake vs BigQuery, both services still have a number of differences:
- Scaling. Snowflake requires some output data, whereas BigQuery will do it itself.
- Functionality. One of the major differences between the services is that Snowflake is a separate SaaS solution, while BigQuery only works with Google services. In addition, the first technology will allow you to share information with other accounts of the base.
But BigQuery does not provide the ability to exchange data, but this system will allow you to share query results with a group of users without access. In addition, through a certain function, you can “teach” the system to perform certain tasks, which will improve the quality of queries.
It appears that Google BigQuery is still superior to Snowflake, since it has a machine-learning option. When choosing a cloud platform, you should decide on the problems you may face, and how they can be solved with the help of the mentioned cloud warehouses.
The future of cloud platforms
The main goal of today’s cloud warehouses is to integrate individual cloud services into a cohesive unit, so that analysts can make full use of all the tools they need to create a stable source of authentic information. With the help of services, it is possible to get access to all customer data in one place for a particular business group, but no competitor can take advantage of such data.
Renta is designed to solve this problem. With it, you can move data from central data warehouses into the operating system’s records. The service synchronizes the information in real time from a special source and sends it to the user’s own business application tools. Based on this, you can make decisions that will bring profits.
Wrapping Up: Snowflake vs BigQuery
In closing, by reading this post, you learned two differences between Snowflake and BigQuery. You also discovered the features of each.
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