Did you know that most of the data warehouse projects fail due to wrong planning and platform selection? That said, many businesses skip the step of selecting the right cloud data warehouse and proceed directly with the other tasks. Speaking of cloud data warehouse platform providers, both Snowflake and Google BigQuery are among the most sought-after options and offer top-notch features to facilitate organizations.
Our blog compares both warehouse solution providers in detail as we dig into the details of these data warehouse giants to help you make the right selection.
Understanding Snowflake and BigQuery
The thought of setting up a data warehouse earlier implied emptying your pockets on overly expensive hardware solutions to run in your data centers. However, the advent of cloud data warehouse solutions has halted these scary means and has provided inexpensive and finer solutions like Snowflake and BigQuery. Before we jump into the comparison, let us first give a brief overview of Snowflake and BigQuery for people new to these names.
If you are already acquainted with these data warehousing solution providers, you may skip this part and directly move towards the comparison part.
What is Snowflake?
Snowflake is a fully managed cloud data warehouse that is offered as a SaaS and DaaS to users worldwide.What separates Snowflake from its competitors is its architecture, which lets the users scale and pay for the computations and storage separately.You can deploy Snowflake to any of the following cloud providers:
- Microsoft Azure
- Amazon Web Services (AWS)
- Google Cloud Storage (GCS)
Businesses and organizations that don't want to get into the nitty-gritty of handling their in-house servers and hiring multiple people for the system's installation, configuration, and management can get a solution like Snowflake.With Snowflake, you don't have to deal with any back-end work, as you can deploy Snowflake instances on any of their preferred cloud providers.
What is BigQuery?
Google BigQuery, like Snowflake, is also a fully managed cloud data warehouse solution that is popular for its speed and responsiveness. As the name suggests, BigQuery is presented by Google and uses its Dremel technology, and is presented as a read-only data solution. BigQuery's tree-like architecture is the secret behind its ultra-fast scanning and querying. BigQuery is highly scalable due to the fast deployment cycle, and to put the cherry on top, it is serverless and offers on-demand pricing. Its architecture works on analyzing the used resources. It assures the usage of all available allocated resources so that the organizations can deploy them without needing to scale out. BigQuery is also a big-data solution thanks to its ability to collect high volumes of data and analyze and organize it fastly. Businesses and organizations seeking robust analytical and intelligent solutions can opt for BigQuery, as its algorithm, architecture, and flexible pricing makes it quite handy.
Snowflake vs. BigQuery: Comparison
Now that we have learned about Snowflake and BigQuery, we can jump into their comparison. We will compare both data warehouse solutions in three different departments, i.e., features, performance, and pricing, and lastly will conclude a winner that excels better in these departments.
Snowflake vs. BigQuery: Features
We all fancy solutions that are not just reliable and affordable but are also packed with the best and latest features. We will compare BigQuery and Snowflake in terms of their features' offering in this section and declare a winner in the features department at the end of this section.
Machine learning sheds light on the algorithms and the data usage to copy the methods by which a process is learned and improvised with time, thanks to its complex technology. While the technological world is welcoming artificial intelligence with open arms, it is impossible to forget the importance of machine learning in growing data science solutions. BigQuery pays its homage to machine learning as it lets the users train and deploy the machine learning models using the existing models and improvising them. You can make most of this feature as you no longer are required to export your data or use a tool to carry data exportation tasks. Contrarily Snowflake solely depends on the external tools for machine learning. Even though using these external tools, you can carry out the tasks in a proficient manner; this solution is certainly not as coherent and handy as the one that BigQuery provides. Furthermore, if you combine BigQuery with Looker, you can get the best machine learning results.
Security is one factor that, if compromised, can annihilate any business or organization regardless of its size. Any business or firm dealing with confidential data should only opt for the cloud data warehouse solution that provides the most robust security. Thankfully, both our competitors BigQuery, and Snowflake are strong contenders in the security domain. Snowflake and BigQuery both use Advanced Encryption Standard on the data and support customer-managed keys. That said, both are dependent on the roles to offer access to their resources. Snowflake provides the SOC 1 Type II, SOC 2 Type II, PCI DSS, and HIPAA compliance, and offers strong security features to safeguard your precious data from intruders. Other security features include access control, multi-factor authentication, etc.
Don't want specific IP addresses to access your data? Snowflake lets you choose a list of IP addresses that you can whitelist, and any user with a different IP address from the list won't be able to enter the system. You can also blacklist IP addresses and use its automatic data encryption feature to guard your data further. On the other hand, BigQuery also focuses on security and follows modern methods to ensure the best security protocols. As BigQuery is a cloud solution offered by Google, it encrypts all your data automatically regardless of it being at rest or in transit. What more would one want?Like Snowflake, BigQuery also meets the PCI DSS and HIPAA compliance standards. Moreover, BigQuery allows the admins to manage the user's access to the cloud resources.
Ease of Use
Most organizations are reluctant to pay high prices while spending on cloud warehouse solution providers and to save a few bucks, opt for inexpensive solutions. Even though they save themselves in the beginning by paying low costs, that strikes back as the cheap solutions often fail or require hefty amounts for their maintenance. The cheap solutions' maintenance is hard on the pockets, but they are also unreliable and insecure. Always go for a well-reputed warehouse solution provider and that does not require heavy maintenance over time. Unlike other solutions, Snowflake and BigQuery do not require massive administration costs and are pretty easily maintained. BigQuery facilitates its users by transferring the unused data to long-term storage automatically, saving high costs. If any element within BigQuery has not been used for over three months, it will automatically move it to long-term storage. Since both Snowflake and BigQuery are automated systems, they don't require much supervision. Both don't need human intervention in query optimization and instance adjustment. They also allow the admins to manage the user roles and permissions to ensure secure access. As data scales up with the passing time and the queries get more complex, both Snowflex and BigQuery automatically scale them to meet the requirements.
Since Snowflake separates the compute and storage resources, users can independently scale them as per their requirements. It also considers automated performance tuning and workload monitoring to enhance the query times when the platform is running. On the other hand, BigQuery tackles scalability differently. As it is serverless, it automatically facilitates extra compute resources or as per the on-time requirements to deal with big data. This ability makes it easier for BigQuery to process millions of gigabytes of data in a couple of minutes. Winner: BigQueryCombining our results in the domain of the features, we see BigQuery as the clear winner. Let’s see what we get in the performance and pricing domains.
Snowflake vs. BigQuery: Performance
The auto-scaling ability of both Snowflake and BigQuery allows them to sustain incredible amounts of load and deliver excellent performance. Both deliver almost similar performances for many tasks and require very little maintenance.If your business or organization deals with massive volumes of data and has high idle times, then BigQuery is a better option.On the flip slide, if your usage is relatively steady dealing with the data and queries, then Snowflake would be a more economical option, as it will let you resolve more queries into your compute times.Last year, Fivetran worked on a benchmark report that compared both our contenders, Snowflake and BigQuery. They ran 99 TPC-DS queries of different complexities and ran each query only once to abstain from caching the previous results.Fivetran generated a 1TB TPC data set having 24 tables in a snowflake schema, and they also decided to avoid fine-tuning the data warehouses and delivered the following results.
- Snowflake gave an average query time of 8.21 seconds.
- BigQuery gave an average query time of 11.18 seconds.
The results concluded that Snowflake is faster than BigQuery in terms of performance.Winner: Snowflake
Snowflake vs. BigQuery: Pricing
The last and probably the most important factor of our Snowflake and BigQuery comparison is their pricing plans and affordability. As mentioned in the upper sections, they both provide separate storage and compute, but we didn't discuss the computing costs.Interestingly, both Snowflake and BigQuery have different ways to calculate computing costs. While Snowflake calculates the prices based on time usage, BigQuery focuses on the data amount spent in scanning the queries.Let's discover more about their pricing plans:
Snowflake offers you a monthly amount of $23 per terabyte if you opt for upfront payment; else, you can also choose their $40 per terabyte (monthly average) if you choose their on-demand plan.Snowflake has separate pricing plans for the compute. It has divided its service into seven different tiers for data warehouses. You can avail of it for as low as an amount of $0.00056 per second.Visit Snowflake's official website to check out its pricing plans in detail.
With BigQuery, you have the following two payment options with storage:
- A flat rate of $20 per terabyte (monthly) for uncompressed and active storage.
- Pay $10 per terabyte (monthly) for long-term storage.
Note: Google offers the first 10 GBs of monthly storage for free. If we look at BigQuery's compute pricing plans, it charges you the on-demand queries for $5 per terabyte. It also gives you the option to buy 500 slots at $10,000 (monthly flat rate) or $8500 (annual flat rate). Note: Google offers the first 1TB of monthly storage for free. Visit BigQuery’s official website to check out its pricing plans in detail. Users seeking on-demand and pre-purchasing pricing plans as per their data needs and spending on a per-second basis should opt for Snowflake. While users looking for a charge per usage basis should go for BigQuery. BigQuery's web console also provides an estimated number of scanned data before the run to help you get an idea of the total cost. Winner: BigQuery
Final Decision: Snowflake vs BigQuery?
We compared both Snowflake vs BigQuery on various factors. While we have concluded a winner from our findings and personal opinions, we leave the final decision to you to pick up the better option.As per our comparison, BigQuery won in the features and pricing department, while Snowflake won in the performance department. While both are neck-to-neck competitors in all domains, our results conclude BigQuery as the better data warehouse solution.