When it comes to data warehouse projects, if the wrong technology is chosen, the project is often doomed to failure. However, when data warehouse project managers take the time to weigh the pros and cons of various data warehouse providers, they often achieve terrific results. In the long run, taking the time to find the right technology is often a wise investment, as a successful data warehouse project has the power to transform any business through keen data-driven insights.
Some of the leaders in data warehouse technology are Snowflake, Google BigQuery, and Amazon Redshift. Of course, we've already compared Redshift to Snowflake and Redshift to BigQuery, but in the battle of data warehouses, which comes out the victor: Snowflake or BigQuery?
Remember, with Integrate.io, you get a truly unbiased review, as our data pipelines support both Snowflake and BigQuery. Our only goal is for our clients to choose the right data warehouse for their needs. Read on to learn more about Snowflake and BigQuery and to discover which of these data warehouse giants will provide the best data warehouse solution for your company.
Table of Contents
The Main Differences Between Snowflake and BigQuery
The main differences between Snowflake and BigQuery are:
- Pricing: Snowflake uses a time-based pricing model for computing resources, in which users are charged for execution time. BigQuery uses a query-based pricing model for computing resources, in which users are charged for the amount of data that is returned for their queries. BigQuery storage is slightly cheaper per terabyte than Snowflake storage.
- Performance: According to independent third-party benchmarks, Snowflake performance is noticeably better than BigQuery performance. However, this conclusion is not universal—there are certain situations in which BigQuery outperforms Snowflake.
- Ease of use: Both Snowflake and BigQuery score highly on the usability scale, although Snowflake may be slightly easier to use. In particular, BigQuery's serverless nature makes it easy to get up and running quickly.
- Scalability: Snowflake and BigQuery both have advanced scalability features. However, BigQuery comes out slightly ahead by handling everything under the hood, removing the need for users to perform any manual scaling or performance tuning.
- Security: Both Snowflake and BigQuery include robust security features that protect the confidentiality and integrity of your sensitive data. In addition, both solutions are compliant with industry-specific regulations such as HIPAA and PCI DSS.
Data Warehouses, ETL, and OLAP: A Quick Refresher
A data warehouse is a centralized data repository that collects and stores information from various sources, both internal and external to your organization. Data warehouses serve as BI and analytics “factories.” Raw data is dumped inside the data warehouse, where it is also processed in order to answer your most pressing business queries and help you with forecasting and budgeting decisions.
By intaking data from across the organization—from sales and marketing to customer service and HR—data warehouses make it significantly easier to run your analytics processing workloads. Both Snowflake and BigQuery are examples of enterprise-class data warehouses that can power the BI and analytics needs of the largest organizations.
One popular use case for data warehouses is trend analysis. For example, support you’re interested in learning which customers are most valuable and which most likely to churn. You can connect your data warehouse to a customer relationship management (CRM) platform like Salesforce, ingest the Salesforce data, and then run the appropriate queries.
Data warehouses use the ETL (extract, transform, load) process to ingest data:
- Extract: Data is first extracted from a source database or file, which may be internal or external to the organization.
- Transform: Data is cleaned, prepared, and transformed in order to fit the schema and constraints of the target data warehouse.
- Load: Data is loaded into the target data warehouse.
ETL is closely related to ELT, a process in which data loading is performed before the transformation. However, data warehouses have traditionally required the use of ETL (and not ELT). This is because input data must be organized in a relational (row-column) format before it can be ingested into a data warehouse.
Related Reading: ETL vs ELT - Top Differences
Both Snowflake and BigQuery are designed to work with both ETL and ELT. Snowflake supports data transformation during or after loading, which makes it compatible with ELT as well. Meanwhile, many data integration experts recommend that you use ELT with BigQuery instead of ETL since it's more efficient to first load the data into BigQuery and then perform any necessary transformations on it.
Data warehouses are one example of OLAP (online analytical processing) systems. OLAP systems are focused on big-picture business intelligence and analytics activities, crawling through massive amounts of data to find meaningful trends. This means that OLAP systems need to support a high volume of transactions from a relatively small number of people. Querying and reporting, rather than making changes to the data, are the most important functions of an OLAP system.
There are some important differences between Snowflake and BigQuery regarding their relationship with OLAP. According to the Snowflake website, OLAP is a “foundational part” of the Snowflake database schema. On the other hand, BigQuery excels at ad hoc queries that obviate the need for traditional OLAP tools.
Now that we’ve gone over the finer points of data warehouse terminology, let’s compare two of the most popular cloud data warehouse solutions: Snowflake and BigQuery.
What is Snowflake?
Snowflake is a data warehousing solution that is offered as a SaaS (software as a service) platform and that has been purpose-built for the cloud. Data warehouses in Snowflake can be hosted on either of two public cloud services: Amazon Web Services or Microsoft Azure.
According to its creators, Snowflake is “faster, easier to use, and far more flexible than traditional data warehouse offerings.” Rather than building on existing solutions such as Hadoop, Snowflake uses a new SQL database engine with a cloud-optimized architecture.
Snowflake is also noteworthy because it enables total separation between a data warehouse’s to compute and storage requirements. This allows you to scale both requirements up and down independently, giving you greater flexibility while cutting costs.
For more information on Integrate.io's native Snowflake connector, visit our Integration page.
What is Google BigQuery?
Google BigQuery is Google’s own data warehousing solution. First launched in 2010, BigQuery was one of the first data warehouse solutions to be generally available, after C-Store and MonetDB.
BigQuery is an important part of Google’s entire cloud computing ecosystem, which is known as Google Cloud Platform. The main competitors of BigQuery are other cloud data warehouse giants such as Snowflake, Amazon Redshift, and Microsoft Azure Synapse Analytics (formerly Azure SQL Data Warehouse).
Dremel is a powerful query engine developed by Google that is used to execute queries in BigQuery. In Google’s own words, Dremel is “a query service that allows you to run SQL-like queries against very, very large data sets and get accurate results in mere seconds.” BigQuery and Dremel are supported by other Google cloud technologies such as Borg and Colossus that help allocate resources and provide data for Dremel jobs.
For more information on Integrate.io's native Google BigQuery connector, visit our Integration page.
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Snowflake vs. BigQuery Pricing
Cost is the most important concern for many companies when choosing a data warehouse solution, so let’s begin this Snowflake and BigQuery comparison by discussing the pricing of these two options.
How much does Snowflake cost? The answer depends on how much you use it. As previously mentioned, Snowflake separates compute and storage, so there are separate costs for both.
First, Snowflake pricing for storage is straightforward enough: $23 per terabyte per month if paid upfront or $40 per terabyte per month if on-demand (average compressed amount).
Snowflake pricing for computing, however, is a little more complex. There are seven different service tiers for Snowflake data warehouses. The cheapest (i.e. “Standard”) compute tier costs $2 per hour, or one credit per hour (based on AWS hosting for the US-East region). This translates to a price of $0.00056 per second.
For those still confused, the company offers a comprehensive Snowflake pricing guide for prospective customers to peruse.
Meanwhile, the question “How much does BigQuery cost?” might be even harder to answer.
Like Snowflake, BigQuery separates pricing for computing and storage. Google charges a flat rate of $20 per terabyte per month for active, uncompressed storage, or $10 per terabyte per month for long-term storage. In addition, the first 10 gigabytes of storage every month are free. This makes BigQuery storage prices noticeably cheaper than those of Snowflake.
It’s with BigQuery compute prices, however, that things get more complicated. Google charges on-demand queries at a price of $5 per terabyte. Users can also purchase 500 slots at a monthly flat rate of $10,000, or an annual flat rate of $8,500. In addition, the first terabyte of queries every month is free.
Since BigQuery charges per the amount of data returned, and not per hours used, it can be more difficult to estimate the cost of a BigQuery data warehouse.
Snowflake vs. BigQuery Pricing: The Bottom Line
BigQuery is the winner versus Snowflake in terms of storage prices: $20 (uncompressed) vs. $23 (compressed) for 1 terabyte of data per month. But with data compression & compute costs taken into account, BigQuery could end up being the more expensive solution.
Snowflake’s time-based pricing model likely makes it the better option for users who need constant runtime, or who will be executing hundreds or thousands of data-heavy queries every day. On the other hand, BigQuery’s query-based pricing model will be more cost-effective for users who do a lot of data mining, or who see spikes in their processing activity throughout the day.
Snowflake vs. BigQuery Performance
The next obvious point of comparison between Snowflake and BigQuery is performance—so how do both solutions stack up?
In a series of 2019 benchmark tests, technology blog GigaOm found that Snowflake consistently outperformed BigQuery on a number of metrics. (The tests also included two other cloud data warehouse options, Amazon Redshift and Azure SQL Data Warehouse.)
The tests used the industry-standard TPC-DS dataset, which is used to model “general-purpose decision support systems” based on the fictional data of an e-commerce retailer. GigaOm ran a total of 103 tests over the dataset, which had a total size of 30 terabytes.
Snowflake required a total of 5,793 seconds to complete all 103 TPC-DS queries. BigQuery, however, required more than sixfold the amount of time, at 37,283 seconds.
Of course, it’s too reductionist to say that Snowflake is faster than BigQuery for all use cases. For example, GigaOm found that BigQuery outperformed Snowflake on Query 44 of the benchmark tests, which involves finding the best-performing and worst-performing items as measured by net profit.
In addition, both Snowflake and BigQuery are under active development, with new features and performance enhancements arriving on a regular basis. Current and upcoming changes to Snowflake and BigQuery may shift the calculus on which data warehouse solution truly exhibits superior performance.
Snowflake vs. BigQuery Features
Pricing and performance are perhaps the two most important issues when choosing a data warehouse solution, but they’re not the only ones. In this section, we’ll discuss some other important factors to consider when deciding between Snowflake and BigQuery.
Snowflake vs. BigQuery: Ease of Use
Both Snowflake and BigQuery fall on the “user-friendly” side of the spectrum when it comes to the question of ease of use.
On the business software review website G2, Snowflake has received average ease of use rating of 9.2 (compared to an average of 8.7 for all data warehouse solutions). Meanwhile, BigQuery earns still-respectable ease of use rating of 8.2.
For example, the fact that BigQuery is a serverless, fully managed data warehouse does much to help its usability score. Once their data is uploaded into the Google Cloud Platform, users can get started with BigQuery without a lengthy setup or configuration process.
While both Snowflake and BigQuery are easy to use, Integrate.io makes it even easier. With simple pre-built workflows and integrations, Integrate.io abstracts away the complexities of managing a data warehouse and lets you zero in on the results of your queries.
Snowflake vs. BigQuery: Scalability
Snowflake allows users to scale their compute and storage resources up and down independently. It includes automatic performance tuning and workload monitoring in order to improve query times while the platform is running.
BigQuery, meanwhile, handles the question of scalability entirely under the hood. As a serverless offering, BigQuery automatically provisions additional compute resources on an as-needed basis in order to handle large data workloads. This makes it easy to process even petabytes of data in a matter of just a few minutes.
Snowflake vs. BigQuery: Security
Organizations that process confidential or sensitive information—such as healthcare, financial, and retail data—need to be especially mindful when it comes to data warehouse security. The good news is that both Snowflake and BigQuery offer users enterprise-class security features to protect their data.
- Multi-factor authentication
- Support for OAuth and user SSO (single sign-on)
- IP whitelisting and blacklisting
- Access control
- Automatic data encryption
BigQuery, as well as other Google Cloud Platform solutions, provides automatic encryption for data both in transit and at rest. Google’s Cloud Identity and Access Management (IAM) feature enable administrators to fine-tune users’ access to cloud resources. BigQuery is also capable of meeting HIPAA and PCI DSS compliance standards.