Depending on your business goals and requirements, it is crucial to understand what type of data warehousing you require. However, selecting the best one can occasionally be challenging. The ideal data warehouse for many firms is difficult to choose. To help you choose the best data warehouse for you, we will compare the two most widely used data warehouses, Snowflake vs BigQuery, in this blog post. then let’s begin.
Cloud Data Warehouses Comparison: Snowflake vs BigQuery
Describing Snowflake
According to Wikipedia, Snowflake Inc. was established in 2012 and is an American cloud-based data-warehousing startup. Snowflake provides “data warehouse-as-a-service,” or cloud-based data storage and analytics. A powerful data platform offered as Software-as-a-Service powers Snowflake’s Data Cloud (SaaS).
In comparison to conventional services, Snowflake’s data storage, processing, and analytic solutions are far quicker, simpler to use, and much more versatile. Snowflake’s Data Cloud uses a unique multi-cluster architecture to provide a secure and highly available data platform that can be scaled up or down in response to changing customer needs.
With the ability to access data from anywhere and anytime, Snowflake’s Data Cloud can be quickly deployed and dynamically scaled, making it one of the most powerful cloud-based analytics solutions on the market. Today, Snowflakes Data Cloud offers customers the ability to analyze data faster and more accurately than ever before.
Why use Snowflake?
Snowflake’s multi-cluster and shared data architecture allow for separating the storage and computation layers. They can scale up and down autonomously in response to demand without performance being impacted. Micro-partitioning is a component of their architecture. This suggests that they are competent in handling structured and semi-structured data.
As a result, Snowflake has native support for managing JSON, Parque, etc., and it can do so on an unlimited scale. Snowflake is a cloud data warehousing system that utilizes micro-partitioning as a core component of its architecture. Snowflake’s ability to manage structured and semi-structured data on an unlimited scale gives it a great advantage.
The fact that Snowflake is offered as a service is crucial. As a result, it requires practically no management and is incredibly simple to use. Customers can concentrate on the value contained in their data since once their data has been migrated into Snowflake, everything else is taken care of and does not require indexing, pruning, etc.
Describe BigQuery
BigQuery, according to Wikipedia, is a fully-managed, serverless data warehouse that provides scalable analysis across petabytes of data. It is a Platform as a Service (PaaS) that facilitates ANSI SQL querying. BigQuery, an enterprise data warehouse, addresses this issue by providing lightning-quick SQL queries leveraging Google’s infrastructure.
BigQuery: Why Use It?
Managing the data dispersed across the myriad applications used by teams as the firm expands becomes challenging. This further makes it challenging to use these systems’ data for useful insight-gathering analysis. Frequently, valuable engineering resources are used to create a centralised data store that houses all of this data and makes BI possible.
BigQuery simplifies this process by providing a reliable, fast, and cost-effective data warehouse that enables users to store and analyse massive datasets quickly, simply, and at scale. With BigQuery, data integration and analysis are seamless; it provides a single, easy-to-use platform for accessing and analyzing data from disparate sources.
Developers may now return to concentrating on crucial tasks like creating queries to evaluate business-critical data thanks to BigQuery. Additionally, BigQuery’s REST API makes it simple for companies to create mobile front ends and dashboards that are powered by App Engine. Businesses may then fully release the value of this data and enable all stakeholders in the organisation to gain insights from it.
Which is superior, Snowflake or BigQuery?
So let’s begin this article on Snowflake vs. BigQuery.
#1. Snowflake vs. BigQuery: Cost Comparison
The computational resources used by Snowflake are priced according to execution time, and users are charged accordingly. BigQuery charges users for the quantity of data that is returned for their queries using a query-based pricing model for computing resources. Snowflake storage costs a little more per terabyte than BigQuery storage.
#2. Architecture: Snowflake vs. BigQuery
The architecture of Snowflake is a cross between the conventional shared disk and shared-nothing database designs. Snowflake, like shared-disk systems, uses a central data repository to persist data that is accessible from all compute nodes in the platform. However, Snowflake performs queries utilizing MPP (massively parallel processing) compute clusters, which are akin to shared-nothing systems in that each node in the cluster keeps a subset of the full data set locally.
You don’t need to consider architecture when using BigQuery, a serverless data warehouse, because the platform handles all resources and automates scalability and availability, freeing managers from having to choose the ideal CPU or storage levels.
#3. Performance Comparison between Snowflake and BigQuery
Both Snowflake and BigQuery operate well under varied load levels because of their capacity to autoscale. Although you should do tests with your data, you’ll probably discover that both platforms function admirably when handling the workloads of the majority of businesses.
With queries taking an average of 10.74 seconds in a head-to-head comparison, Snowflake edged out BigQuery in raw speed (geometric mean). BigQuery, on the other hand, averaged 14.32 seconds per query. In other words, during these testing, Snowflake was quicker.
Independent third-party benchmarks reveal that Snowflake performs noticeably better than BigQuery. BigQuery occasionally performs better than Snowflake, therefore this conclusion is not absolute.
#4. BigQuery vs. Snowflake: Ease of Use
When it comes to the issue of usability, Snowflake and BigQuery both fall on the “user-friendly” end of the spectrum.
The average ease of use rating for Snowflake on the business software review website G2 is 9.2. (compared to an average of 8.7 for all data warehouse solutions). However, BigQuery receives a still-respectable 8.2 for ease of use.
#5: Scalability of Snowflake vs. BigQuery
Users can individually scale their processing and storage resources using Snowflake. To reduce query times while the platform is running, it has automatic performance tweaking and workload monitoring.
While BigQuery completely takes care of scalability behind the scenes. BigQuery is a serverless service that automatically creates more compute resources as required to tackle huge data workloads. Because of this, processing petabytes of data in a matter of minutes is simple.
Conclusion
We hope you enjoyed and found the aforementioned comparison to be accurate. You can now choose a side based on your needs since you are aware of both sides of the argument. Another intriguing comparison between Snowflake and Amazon Redshift is available (Snowflake vs Redshift).
In conclusion, both Snowflake and Amazon Redshift offer unique features that can benefit any business depending on their size, scalability needs, budget, and data warehouse type. With so many similarities and differences between Snowflake and Amazon Redshift, it can be a difficult decision to make when selecting the right data warehouse for your business.
