BigQuery vs Snowflake: A Comparison of Data Warehouse Giants
In this article we are going to compare the two topmost data warehouses: BigQuery and Snowflake.
By Anji Velagana, Freelance Content Writer
It's essential to understand data warehousing depending on your requirements and business. Many organizations struggle in selecting the data warehouse that suits them. Hence, people are opting for the BigQuery/Snowflake course to understand data warehousing. Here, we are going to compare the two topmost data warehouses: BigQuery and Snowflake.
Let's move right into knowing them.
What is BigQuery?
BigQuery is a serverless and fully managed data warehouse that can enable a scalable analysis of the data. It is referred to as "Platform-as-a-Service (PaaS)." By using ANSI SQL, BigQuery supports querying. An enterprise data warehouse is used to solve problems using Google infrastructure processing power by enabling super-fast SQL.
Significance of BigQuery
It is very hard to manage the data spread across the applications as the business grows. Also, it isn't easy to analyze the data within meaningful insight systems. The precious engineering sources are often deployed in setting up a centralized data store. With BigQuery, the developers can focus on essential activities like analyzing business-critical data. The REST API of BigQuery enables businesses in building mobile front-ends and App Engine-based dashboards.
What is Snowflake?
It is a cloud-based data warehousing company founded in 2012. Snowflake offers analytics services, cloud-based data storage services. In general, Snowflake is termed as a "Data warehouse as a service." The data cloud of Snowflake is powered by the advanced data platform with Software-as-a-Service (SaaS). It enables data processing, storage, and analytics solutions that are faster, easier, and more flexible than traditional offerings.
Significance of Snowflake
Snowflake has an architecture of multi-cluster shared data. The architecture separates their compute and storage layer. This can help them in scaling up automatically as per the demand without impacting their performance. Micro-partitioning is featured in Snowflake architecture that is able to manage both structured and semi-structured data. Hence, they are able to manage Parque, JSON and so on. Within the Snowflake. One of the vital points is that Snowflake can be delivered as a service. Besides, it is extremely easy to use with zero management. After the data migration into Snowflake, everything is taken care of, and there's no requirement to prune, index, etc., allowing the users to focus more on the value in the data.
Which is better, BigQuery or Snowflake?
If you want to know which is better among BigQuery and Snowflake, let’s know their characteristics.
For computing resources, Snowflake uses a time-based pricing model, in which the users charge for execution time. Whereas, BigQuery uses a model of query-based pricing for computing the resources, in which the users charge for data. BigQuery storage is less expensive than Snowflake storage.
The architecture of Snowflake is a shared-nothing database and a hybrid traditional shared-disk architecture. Also, Snowflake uses the central data repository from all the compute nodes for persisted data. Similarly to shared-nothing architectures, Snowflake processes all the queries by using Massively Parallel Processing (MPP) to compute the clusters.
Both BigQuery and Snowflake perform well under different load levels. One must run the benchmarks by using his own data. Also, they can be used to handle the workloads of many companies with good performance. In terms of raw speed, on an average of 10.74 seconds, the Snowflake edged out BigQuery. It is a known factor that BigQuery clocked at 14.32 seconds per query. As per the benchmarks of independent third-party, the performance of Snowflake is better than the performance of BigQuery.
Ease of Use
Both BigQuery and Snowflake are user-friendly in nature when it comes to the case ease of use. Snowflake has received a 9.2 rating on the G2 business software review website.
Often, Snowflake allows the users to scale their storage and compute resources independently. It includes workload monitoring and automatic performance tuning to improve query times. Whereas, Bigquey handles scalability questions under the hood entirely. BigQuery can automatically provide the compute resources. It makes it very easy to process the data in just a few minutes.
Both BigQuery and Snowflake use AES encryption on the data to support customer-managed keys. Besides, they depend on the roles to provide access to the resources. Often, Snowflake allows federated user access through SAML 2.0 compliant vendors and Microsoft Active Directory (ADFS). BigQuery also allows federated user access by using Active Directory. Snowflake provides granular permissions for views, schemas, procedures, tables, and other objects.
Maintenance and Management
Both BigQuery and Snowflake require low maintenance because automated management is going on in the background. This can imply in Snowflake that queries are optimized and tuned in the background sometimes. Since the platform of Bigquery is serverless, the customers are hardly aware of the considerations.
I hope you found the detailed insights of BigQuery and Snowflake. Now, you are aware of both and choose to pick as per your needs. Still, you have any queries regarding BigQuery and Snowflake, feel free to comment in the below section.
Bio: Anji Velagana is a B.Tech graduate turned Freelance Content Writer, pursuing a Masters in Journalism & Mass Communication with passion.
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