In our previous blog post, we unearthed a sneak peek of the significant differences between Snowflake & BigQuery depending on existing architecture & use cases. It helped decision makers arrive at a prime data warehouse destination suited for their business model in no time.
In this blog post, let’s take a deeper look into the technical features of Snowflake & BigQuery that is paramount for data specialists.
Snowflake is a cloud-engineered data warehouse platform that combines the best attributes of traditional shared disk & shared-nothing database architectures. With this architecture, Snowflake orchestrates the persisted data in a central repository and processes queries using massively parallel processing (MPP) compute clusters. The unique architecture of Snowflake delivers a multi-cluster approach, auto-scalability, lightning performance, and an extremely fast cloud data platform for modern businesses.
Snowflake is a cloud-agnostic data platform that can be hosted on Amazon Web Services (AWS), Microsoft Azure or Google Cloud Platform (GCP). No matter whether you have multiple cloud-hosted applications in your organization, you can orchestrate everything in a Snowflake Center of Excellence (COE). Snowflake supports a wide range of data workloads such as data engineering, data science & ML, applications, cyber security, and collaboration that helps organizations to meet modern data needs. Also, Snowflake’s cloud services layer manages
On top of these features, Snowflake partners with leading data integration, BI, ML, and native programming interfaces and tools to extend its ecosystem as a 360-degree cloud data platform for its business.
Google BigQuery was launched in 2010 and is engineered as a serverless data warehouse native to Google cloud platform.
YES! You heard it right!
The serverless architecture of BigQuery for sure drains over the operational burden of your data teams to an extent. But, your data administrators absolutely have zero power over compute elasticity of the data warehouse. BigQuery automatically scales it’s compute “slots” as per the demands and you’ll be charged for it based on the amount of data scanned per query. Flat-rate subscribers are equipped with configurable compute slots, but with the limitation of 100 concurrent users. You’ll understand all about BigQuery’s pricing model in the next section of the blog, keep reading!
Like Snowflake, BigQuery decouples storage & compute resources but in a unique way. BigQuery has a massive data warehouse architecture that combines the infrastructure technologies like Dremel, Colossus, Jupiter, & Borg. Each of these technologies supports specific data workloads as follows:
BigQuery has also partnered with various BI, ML, and advanced analytics partners. Comparatively, Snowflake has an edge over the partner ecosystem over BigQuery.
Snowflake offers four licensing editions and leverages a time-based pricing model for compute resources. Snowflake bills storage as terabytes per month and compute resources on a per-second basis.
Let’s take a deep look into Snowflake pricing guide.
The compute cost of Snowflake is largely dependent on cluster sizes ranging from XS to 6XL. Here’s the Snowflake credit chart based on cluster size.
Snowflake offers data-sharing features at zero cost within the same cloud platform & region. As the Snowflake compute clusters are configurable, your data teams have the upper hand over cost & resource optimization. Depending on your data needs and budget, you can seamlessly scale up or scale down your Snowflake data platform.
On the flip side, Google BigQuery leverages a query-based pricing model that charges for the amount of data scanned. BigQuery offers on-demand and flat-rate pricing models for compute resources.
Even though BigQuery offers storage at lower costs than Snowflake, the compute slots skyrocket the overall licensing cost as the users have no control over the scalability of resources.
Here’re the BigQuery’ s pricing options.
Without proper skillsets in data wrangling and BigQuery’s pricing model, your business can end up paying a huge licensing cost.
Snowflake’s hybrid architecture and pay-as-you-go pricing model enable users to tune their compute resources based on needs and henceforth optimize the compute costs. The users can auto-scale and auto-suspend the compute resources whenever required. In addition, Snowflake enables workload isolation to operate without concurrency issues.
On the contrary, BigQuery takes over the behind-the-scenes processes of computing. Flat-rate subscribers have control over scalability of compute slots but is not flexible as like Snowflake. Also, BigQuery restricts concurrent users to 100 by default.
Snowflake truly wins the competitive edge over BigQuery in the aspects of ease of use & intuitiveness. The intuitive web UI is accessible across all the leading browsers such as Chrome, Firefox, Safari, Opera & edge. Snowflake does offer a native web interface, Snowsight to render a unified and easy-to-use experience.
Snowflake receives an average usability rate of 4.6/5 in Gartner product reviews. Picking Snowflake would be the appropriate decision if your organization is planning to establish a data-driven culture. All you need would be beginner-level user adoption training and a cognitive data strategy!
BigQuery has a decent user-interface that supports data workloads. However, the researchers feel BigQuery is clunky and lacks screen space for efficient analytics. BigQuery does offer a cloud console with a graphical user interface (GUI) for data operations.
BigQuery scores a decent 4.5/5 for usability in Gartner product reviews.
Again, in performance, Snowflake has the leg up over all the cloud data warehouses in the market.
A series of 2019 benchmark tests was conducted with an industry-standard fictional e-commerce TPC-DS dataset. As part of this performance testing, 103 queries comprising of 30TB data were examined across leading cloud data platforms such as Snowflake, BigQuery, AWS RedShift, etc. Snowflake outperforms BigQuery in many aspects and has taken only 5793 seconds to complete 103 tests, whereas BigQuery required 6x of Snowflake (ie. 37283 seconds) to perform the tests. However, in certain use cases, BigQuery seems to be faster than Snowflake.
Both Snowflake & BigQuery has better performance curve than many other cloud data platforms in the marketplace and they’re striving to be better with periodical feature upgrades.
Snowflake renders a near-zero maintenance data platform for its users. Your data admins can seamlessly manage user roles, accessibility permissions, resource optimization, data security, and governance in Snowflake. Based on the needs, Snowflake also lets data admins scale up or scale down the compute resources independently by enabling workload isolation.
BigQuery automatically handles the data management operations and as it’s a serverless DaaS solution, users have no control over compute elasticity. Based on the query, the compute “Slots” are scaled up or down and the administrators or users have no control.
Snowflake provides advanced encryption standards (AES) for both data at rest & in transit. The platform restricts granular column accessibility permissions but does provide accessibility for schemas, tables, views, procedures, & other database objects. Snowflake adheres to SOC1 Type II, SOC2 Type II, SOC3 Type II, HIPAA, PCI DSS, FedRAMP, DSS, and ISO/IEC compliances.
On top of these standard encryption features, Snowflake offers
BigQuery adopts the built-in security features of Google cloud services and offers accessibility permissions columns, datasets, tables, and views. The cloud platform is compliant with SOC1 Type II, SOC2 Type II, ISO 27001, HIPAA, and PCI DSS security standards. As like Snowflake, BigQuery offers several dynamic data masking and encryption features such as policy tags, cloud key management services (Cloud KMS) and AEAD encryption.
Both Snowflake and BigQuery have matured backup & recovery features.
Snowflake elevates enterprise data protection with two major features: Time-travel & fail-safe.
Enables accessing historical data (i.e., data that has been changed or deleted) at any point within a defined period. The standard data retention period is 1 day, but the Enterprise & Business Critical customers can configure the time travel up to 90 days.
Offers a 7-day period during which historical data may be recoverable by Snowflake. This period starts immediately after the Time-Travel retention period ends. The fail-safe feature can be leveraged in case of extreme operational failures.
BigQuery safekeeps a track record of 7-day historical data. To extend the data retention period, Google BigQuery has a feature called table snapshots with loads of limitations.
Snowflake enables seamless data sharing with customers, third parties, partners, and vendors by leveraging Snowflake data exchange platform. Users can create reader accounts for non-native users and share the Snowflake data objects. Also, Snowflake doesn’t create another data copy and share the objects. Instead, it enables zero-copy clone accessibility which makes real-time streaming & data governance much easier. The data sharing features on the Snowflake are with no extra infrastructure cost. This feature cuts down the additional cost spent on data-sharing tools and other resources.
BigQuery has analytics hub feature that enables users to share data and insights within organizational boundaries. However, the data sharing process is complicated and has too many limitations in the aspects of quantity, formats, and geography.
Snowflake offers excellent customer support and it’s rendered in two categories: Premier and Priority. Priority tickets are resolved with utmost care and attention than premier tickets.
Snowflake has an extensive online community and resource library to educate and share knowledge. The platform also includes course materials, certifications, and workshops for various organizational roles.
BigQuery offers product support in three tiers: basic, enhanced, and premier.
Google does have an active online community and outsourced courses to learn BigQuery.
Is it a puzzle still?
Both Snowflake & BigQuery offers trial versions to kick-start your cloud transformation journey. We can help you pick the right cloud data warehousing platform by considering your business model, use case, existing human capital, cloud investments, and other aspects.
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