Snowflake On Premise Comparisons: 10 Key Critical Differences

This blog is intended for IT decision-makers or data architects who are torn between using Snowflake or an on-premises data warehouse for their BI needs. Data consumption and analytics are at the forefront of technology today, and this is one of the primary objectives why businesses are choosing to utilize data warehouses.

As a data scientist or business analyst, you probably can appreciate the importance of having a data warehouse for analyzing large amounts of data from multiple sources and delivering actionable business intelligence. But should you choose to deploy your data warehouse on premises — in your own data center — or use a Data-Warehouse-as-a-Service (DWaaS) solution such as Snowflake?

Before a business makes this important decision, they should fully understand the differences of both solutions. This blog introduces Snowflake and On Premise Data Solutions. It also gives a detailed Snowflake On Premise comparison using 10 crucial parameters. Read along to decide the best option for your organization!

What is Snowflake?

Snowflake is a cloud based data warehousing business based in California and founded in 2012. Snowflake offers a Data warehousing as a Service (DWaaS) model which requires little maintenance and helps customers to focus on getting value from their data rather than managing the infrastructure in which it’s stored. 

Snowflake runs across Amazon S3, Microsoft Azure, and the Google Cloud Platform. Since the data is stored in the cloud, this allows for analysis using cloud infrastructure thus avoiding the need for an on-premise storage facility. Read more about other cloud data warehouse tools based on G2 rating.

Key Features of Snowflake

The following features are responsible for the high demand of Snowflake Data Warehouse among businesses:

• Scalability: Snowflake is a unique data warehouse as it delivers storage and computation services separately. It utilizes a database for storing data and performs calculations in its virtual Data Warehouse. Therefore, it is enable you high scalability levels at affordable costs.
• Low Maintenance: Snowflake’s architecture is designed with the objective of minimizing the user interaction and effort for any maintenance or performance-related activity.
• Query Optimization: Snowflake’s automatic query optimization will save you the hassle of manually improving queries.
• Load Balancing: Snowflake allows a great level of load balancing for your daily business activities. You can separate your workloads into distinct virtual Data Warehouses. This way your analytical loads will not be affected by busy clusters during peak routine loads.

Introduction to an On-premises Data Warehouse

An on-premises data warehouse is a type of database in which all the computing resources are accessed and managed by or from the premises. This is in sharp contrast to a Snowflake data warehouse where the pool of computing resources is accessed online.

Similar to Snowflake, the on-prem data warehouse servers collect data from heterogeneous sources, store it in a central repository, and analyze the data for reporting. These on-premises data warehouses often require extensive initial outlay — buying all the hardware you’ll need upfront, regardless of how long or how often you’ll use it. The on-premises data warehouse also requires you to invest in a team to manage the infrastructure.

Snowflake On Premise Data Warehouse Comparisons

The following list of comparisons will help you choose the best option for your business:

• Snowflake On Premise Comparison: Maintenance
• Snowflake On Premise Comparison: Cost Efficiency
• Snowflake On Premise Comparison: Scalability
• Snowflake On Premise Comparison: Elasticity
• Snowflake On Premise Comparison: Ease of Use
• Snowflake On Premise Comparison: Speed
• Snowflake On Premise Comparison: Security
• Snowflake On Premise Comparison: Reliability
• Snowflake On Premise Comparison: Time to Market
• Snowflake On Premise Comparison: Universal Access

Maintenance

• One of the simplest differences between on-prem data warehouses and Snowflake is that on-prem data warehouses require highly skilled IT professionals. They have to acquire(buy), install, monitor, and upgrade the data warehouse. They also have to contend with maintaining the software components, security, scalability issues, hardware breakdown, or lack of computing power.
• Snowflake is low maintenance and requires zero management from end users. With Snowflake, there is no hardware or software for you to select, install, configure, or manage. All you need is to connect to a network or the internet, set up an account, adjust some simple configurations, and that’s it.

Cost Efficiency

• The most basic consideration is the high cost of maintaining an on-premises data warehouse. Building an on-premises data warehouse that can give you the features that you can use in Snowflake costs far more than just using Snowflake. The overall entry costs and maintenance costs of an on-premises data warehouse is incurred by the premise that owns it and is minimal in the long run.
• Snowflake is more cost-efficient than an on-premises data warehouse solution. Snowflake has a usage on-demand service and is perceived as a utility. You only pay for what you use, and you don’t incur other costs such as power, cooling, floor space, physical security, hardware, hardware maintenance, redundant encryption capabilities, not-in-use encrypted backup capabilities, network routing/switching hardware, internet access, etc.

Scalability

• Snowflake separates storage and computes with independent scaling. This enables you to increase either your storage or compute power according to your needs, without the confinement in a single monolithic system.
• On-premise data warehouse allows organizations to add storage or compute power at any time and wherever needed for support of the flexible response toward variable workloads and volumes of data.

Elasticity

• While on-premises data warehouse solutions are rigid, with a Snowflake data warehouse, you get an agile data warehouse solution that can alter your infrastructure on demand. Snowflake allows you to spin up more unique systems very quickly. Its built-for-the-cloud architecture combines the elasticity of the cloud, the flexibility of a big data platform, and the power of data warehousing.
• With the separation of storage and computing, you can scale your computing or storage up and down individually based on demand. The metadata service also scales up or down as necessary; Trillions of rows can be scaled up with ease by multiple concurrent users.

Ease of Use

• The Snowflake data warehouse provides several benefits and features that are inherent in it’s design making the process of working with data much simpler. For example, it’s easier to get up and running with a Snowflake virtual data warehouse.
• It only takes minutes to provision your resources. Snowflake also has a built-in intuitive UI for interacting with the Software and running BI analytics.
• Many open-source ETL solutions and pre-packaged data management vendors such as Hevo Data also integrate well with Snowflake allowing you to easily ingest data from various sources.

Speed

• If you need really fast computing or storage and your data science/AI/ML team is located in one location, then on-premises is the way to go. A properly designed on-premises data warehouse can outshine Snowflake when it comes to large scale batch operations where CPU and IOPS matters. In fact, an on-premises server’s IOPS can be 10x faster than Snowflake’s cloud based data warehouse. 
• However, if you have a globally distributed team the Snowflake offers the best solution because they have a multi-cloud infrastructure that leverages data centers in different locations across the globe. Snowflake also has smart routing systems that tend to optimize the path which data travels to minimize latency.

Security

• Since Snowflake is accessed over the public internet, internet related risks such as malware, man in the middle attacks, and eavesdropping can be experienced.
• But in most cases, an on-premises data warehouse is less secure than a Snowflake data warehouse. If you have one inexperienced admin running your infrastructure, hosted on out-of-warranty hardware with backups that may or may not work – your infrastructure is more at risk than if it was hosted by Snowflake, who have 100s of trained engineers working on their environment.
• However, suppose you have the resources to build it correctly. In that case, an on-premises data warehouse can allow your enterprise to exercise full control over the security and connectivity of various mission-critical applications and other access problems. This is why organizations working in highly regulated industries such as health, insurance, banking, and government sectors prefer on-premises data warehouses for fine-grained control and compliance.

Reliability

• If your on-premises data warehouse is located in a makeshift server closet somewhere without proper power redundancy or any DR plan then you can expect that it’s going to have worse uptime than hosting your data on Snowflake. You may need to build those capabilities by hiring more engineers, and having a better infrastructure. All these things cost money.
• A Snowflake data warehouse may be more expensive than doing things cheaply on-prem, but doing things right on-prem is super costly. Couple that with the fact that Snowflake offers strong Service Level Agreements (SLAs) — 99.99% service availability.
• For this reason, many enterprises are overwhelmingly preferring to move their more critical data to cloud-based data warehouse solutions such as Snowflake. This is because it’s several orders of magnitude simpler (and cheaper) to provision highly resilient, multi-region, and with a defined level of software availability.

Time to Market

• By leveraging a Snowflake data warehouse, you get a faster and more versatile platform that decreases time to market and you remain in complete control over your storage and compute clusters without having to worry about maintenance, security, and cost of acquisition. You pay only for what you end up using.
• On the other hand, building a new on-premises data warehouse takes far too much time to get up and running. You have to gather the requirements, design the data warehouse, draft your disaster recovery plans, set up the physical environment for outfitting the server rooms, and purchase the actual hardware.

Universal Access

• Snowflake is platform independent and it’s accessible across many devices with an internet connection. This increases mobility as well as productivity seeing that it allows distributed teams to collaborate on projects.
• Also, if your business operates internationally, Snowflake allows you to localize infrastructure anywhere in the world near-instantly and meet compliance or availability bottlenecks.

Quick Comparison

Factor	Snowflake	On-Premise
Maintenance	Automated and managed service	Requires dedicated IT staff
Cost Efficiency	Pay-as-you-go pricing; lower initial costs	High upfront costs; ongoing maintenance expenses
Scalability	Seamless independent scaling of compute and storage	Limited by hardware, scaling can be costly
Elasticity	Rapid scaling for fluctuating workloads	Slow and complex to add resources
Ease of Use	Intuitive UI; quick resource provisioning	Variable usability; longer setup time
Speed	Potential latency, optimized for global access	Faster local performance for batch operations
Security	Managed security with compliance protocols	Greater control but requires expertise
Reliability	99.99% service availability; built-in redundancy	Depends on infrastructure and maintenance
Time to Market	Fast deployment; minimal overhead	Longer setup due to hardware requirements
Universal Access	Accessible anywhere with internet connectivity	Limited access; harder to scale globally

Conclusion

The article introduced you to Snowflake discussed its important features. It also explained the On Premise Data Solution as an alternative to Snowflake. The article then provided a detailed comparison between Snowflake and On Premise Data Solutions using 10 parameters. The comparisons can help you decide the most suitable option for your organization.

Now, to run SQL queries or perform Data Analytics on your data, you first need to export this data to your Snowflake Data Warehouse. This will require you to custom-code complex scripts to develop the ETL processes. Hevo Data can automate your data transfer process, hence allowing you to focus on other aspects of your business like Analytics, Customer Management, etc.
This platform allows you to transfer data from 150+ multiple sources (including 40+ free sources) to Cloud-based Data Warehouses like Snowflake, Amazon Redshift, Snowflake, Google BigQuery, etc. It will provide you with a hassle-free experience and make your work life much easier.

Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite firsthand.

Share your understanding of Snowflake vs On-Premise Data Solution in the comments below!

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