AWS Knowledge
Redshift vs Snowflake: Which is Better for Analytics?
Piyush Kalra
Oct 26, 2024
Whichever category you fall into, accepting the idea that your data is a priceless asset to generate business intelligence is all that matters. As a result, it follows that making the right choice with regard to a data warehousing platform is not just crucial but paramount. For practitioners such as data analysts, data engineers, or cloud engineers, getting a hold of the major systems such as Amazon Redshift and Snowflake can help a great deal in your decision making.
Understanding data warehousing platforms can provide your company with a competitive advantage. With so many available, Understanding the specific capabilities and functional features of Amazon Redshift and Snowflake is the key to a good choice. Today, we are going to do a comprehensive comparison between the two industry leaders with a sharp focus on their unique attributes.
By comparing their capabilities, performance, pricing, and scaling, we will try to help you with everything needed in order to find the best out of all the variants for your business requirements. Using both these platforms for analysis or for data storage optimization will significantly ease your decision making.
Understanding Data Warehousing
Definition and Purpose
Data warehousing is a system that has been widely used for reporting and data analysis and is often seen as an essential part of business intelligence. This requires compiling data obtained from various sources to help decision-makers get significant information. A data warehouse caters to querying and analysis, performed optimally in a business, which helps in the efficient taking of decisions for performance monitoring purposes.
Significance in Data Analytics
A comprehensive data warehouse, without a doubt, is the holy grail for data analysts and engineers. It not only optimizes data storage but also aids in complex analytics. With data being stored in one central place, it is up to our liking, well arranged, orderly and at ease in retrieval for constructing business decisions and elevating efficiency.
Overview of Redshift and Snowflake
Amazon Redshift
Amazon Redshift’s powerful analytical capabilities make it a game-changer in the field of data warehousing and business intelligence. It allows handling of both structured and semi-structured datasets in vast amounts making it an ideal BI tool for querying large amounts of information spread across data lakes, operational databases and data warehouses. What's remarkable is that this system is totally based on the cloud so businesses big and small will benefit from the vast dataset handling capabilities it offers. This is notable since the current world is heavily integrated with data. Retaining the ability to handle complex queries and making good use of petabyte-sized ranges, Redshift is also highly time-efficient.
Snowflake
Snowflake is a proper modern data platform aimed at unleashing the whole power of the cloud but tightly bringing together AWS, Microsoft Azure and Google Cloud platform together. Unlike redshift, snowflake is fully relying on the public cloud where the whole infrastructure is located and virtual computing instances are used for data processing while the data itself is stored permanently within the cloud. Through a software as a service model, it is possible to develop data warehousing capabilities that can be used for both structured and unstructured data.
Key Features
Both Redshift and Snowflake come with a number of other options that are suited to other analytical tasks, which makes their use in the data manipulation for different purposes quite appealing.
Amazon Redshift:
AWS Integration: Redshift greatly enhances its idea due to deep data integration with many other AWS cloud services.
Cost-Effectiveness: Its pricing model is useful for workloads with predictability, enabling businesses to keep their costs down.
High Performance: Queries in Redshift are fast to compute thanks to the systems’ architecture supporting complex analytics over massive datasets.
Data Compression: It also makes use of sophisticated data compression methods to minimize costs associated with storage and enhance query performance.
Snowflake:
Cloud-Native Architecture: Snowflake relies entirely on the public cloud infrastructure which is enhanced by advanced architectures of the cloud.
Seamless Data Sharing: On Snowflake’s platform, organizations are able to share data freely with other organizations in a safe and governed way.
Automatic Scaling: It has automatic scaling of compute resources, which ensures proper performance without requiring manual controls.
Support for Diverse Data Types: Handling of Semi-structured and unstructured data by snow flakes is seamless and structured analytical needs are met.
Multi-Cloud Flexibility: It also allows users to work on more than one cloud, thus increasing flexibility and eliminating vendor lock-in.
Why Choose a Cloud Data Warehouse?
Benefits Over Traditional Solutions
Choosing to move to a cloud data warehouse is far more advantageous than using traditional on premise solutions. One of the primary advantages is elasticity; cloud solutions allow firms to up or downscale their resources depending on how they are needed. For example, a retail business may only require an increase in their data for a few months during their holiday seasons where they would need additional storage and processing power, which is achievable online. Such flexibility alone can reduce costs by as much as 30% because they incur only those resources that they actually require at the time of their high season.
Furthermore, the requirement of cloud data warehousing means that the physical infrastructure is no longer necessary which is an expensive thing to have in place. This means that companies will cut both the time and finances for maintenance and for upgrades. One study demonstrated that the companies migrating to the cloud were able to cut their IT expenses by about 20% on average which enabled them to shift funds to critical areas of their businesses. In general, these pluses make cloud solutions to be more attractive in the management of the data.
Scalability
Expanding one of the paramount advantages of the cloud data warehousing system is the scaling feature. Redshift and Snowflake handle workload requirements uniquely. However, a disadvantage with Redshift is that it has constant pricing irrespective of the query load, in that case performance decreases for complex queries under a lot of load. This is potentially very disadvantageous to companies that affect such heavy workloads as it has a large holding period of ingests and other user applications which greatly competes for the system’s resources, where performance levels may allocate essential resources unjustly leading to the negative impacts stated before.
Alternatively, with Snowflake, companies can customize workloads due to their data architecture design, which allows for the separation of data and compute. It means large warehouses can be used for heavy loads and other resources for consistent load on applications. So if responsiveness and high performance is your endpoint goal, it makes sense that the company goes for Snowflake. Conversely, if the company seeks consistent cost and does not mind working at off peak hours in terms of performance, Redshift may be the machine learning for you.
You still have to keep in mind: Scaling Redshift will take 15 to 60 minutes, in comparison to Snowflake’s nearly instantaneous scaling feature which gives businesses the ability to work on varying workloads without having to sacrifice on performance.
Cost-Effectiveness and Ease of Use
Cloud data solutions are significantly more cost-effective, as they operate on a pay-as-you-go policy. This means that companies move and purchase storage and cloud computing resources as needed. Switching to the cloud can save up to 30% of what they previously spent on on-prem data warehouses. For instance, if an organization was spending 10 thousand dollars on data stored/year, it can be expected that this company will only spend $7,000 once they switch to a data-could warehouse expecting the rest of the funds to be spent on other important aspects of the business.
Moreover, the platforms are built to be user friendly. Most cloud data warehouses have features such as dashboards and other tools that ease the working experience, promoting quick adjustment of teams to full features of the systems. This in turn improves productivity because employees take less time grappling with different systems but even time analyzing data and making decisions.
Key Features Comparison
Performance
In any performance assessment, the first thing that comes to mind is the speed and efficiency of a query. Snowflake enjoys a good reputation amongst the data specialists for its ability to process queries in a matter of seconds including the complex analytics. However, Amazon Redshift does not fall too short and offers commendable performance especially when used in conjunction with other AWS features. Both systems can handle considerable amounts of data well, however, the determining factor in the end should be the nature of your data loads and their working requirements.
Scalability
Although both systems focus on scalability, they go about it in different ways. The first step Snowflake makes is not only allowing users to auto scale data but also enabling them to pause or resume the action, saving overhead effectively. Conversely, Redshift allows cluster modifications but users may have to deal with them manually, something Snowflake does automatically, which may cause inconvenience. How to decide between these options becomes clear when you understand the differences, such as these.
Pricing Models
Redshift Pricing
Amazon Redshift's pricing operates on the principle of demand elasticity which is suitable for the firms with workloads that can be forecasted with relative ease. In addition, it provides a reserved instance pricing structure which translates into savings for those companies that are ready to make long-term deals and tend to maintain a consistent level of query traffic as well. Moreover, Amazon Redshift Serverless gives the opportunity to pay-per-use by paying only for the exact amount of capacity that is required and charging for that amount only. We have explained about How much does Amazon Redshift Really Cost?
If you're looking to lower your Redshift costs, Pump is a good free tool that may help them. If you get a bill of $100 for example, Pump may be able to lower it to $44. The more you spend on areas, the more savings we can help you make.
Snowflake Pricing
Snowflake only charges its clients based on the amount of time a query is executed, which is to say, if a query is executed for a minute, charge for one minute only. If two minutes are consumed in the execution of a query, then the client will be charged for two minutes in regards to the resources that were utilized. And this means there is no charge for the unused time or underutilized resources.This model offers flexibility around the utilization of compute resources and storage silo, which displays ideal properties for on-demand scaling or on-demand applications. On the other hand this could prove to be quite costly if someone is on a constant high utilization plan, but has the potential to be cost effective on other occasions such as high compute resources being needed but not regularly.
Meanwhile, we are only providing reductions on Amazon Redshift currently. If your goal is to reduce your SnowFlake expenditures, Pump is a good free tool that can help you do this, though we are actively trying to roll out modifications to enhance Snowflake for you in the near future.
Cost Comparison
Keep in mind that these are just suggestions and every business will have a different purpose and requirement, hence their billing frequencies with snowflake and redshift could be completely different.
For businesses that are smaller in size, the pricing method of Snowflake is appealing because they do not require high investment from the beginning. You only pay based on the time you regularly queries, so you do not have to bear high costs in case your workload is not heavy.
On the other hand, organizations with complex requirements that would be best suited to Redshift's pricing model, these are typically larger organizations. Companies with heavy workload patterns that are repetitive can benefit by reserving an instance as the cost per instance reduces quite heavily in a long term 1 to 3 years contract and this ultimately helps to lower overall costs for the business.
Ultimately, the ideal choice will depend on the kind of workload your organization has. If you are a small business struggling to get off the ground then it may be better to use snowflake for its flexibility, but if you are a larger company than the reserved pricing model of Redshift will be more cost effective. Once you analyze the cushion you have for expected use, it becomes easier to identify your ideal platform.
You can use AWS Pricing Calculator to calculate your Redshift cost in a single estimate.
Security Features
Redshift Security
In terms of AWS data security protocols, it’s rather easy to see how Redshift is able to smoothly integrate thanks to its core dependency on Identity and Access Management (IAM) as it allows them to grant permissions with pinpoint accuracy and also provides encryption for both data in use and data that has been stored. Enabling them to make use of the Redshift platform while fulfilling all compliance requirements with robust security features.
Snowflake Security
Snowflake includes support for many security functions, including multi-factor authentication, role-based access control, and custom network policies, to name a few. With end-to-end encryption and EU’s GDPR regulation, hail HIPAA compliance regulatory measures, a business with high security requirements will find Snowflake to be a perfect fit.
Use Cases for Each Platform
When to Use Redshift
For businesses who are already deep within the Amazon ecosystem, they would thrive with Amazon Redshift. Due to its many integrations with various AWS services like Kinesis and Lambda, businesses utilizing these tools will find it useful. The same goes for Redshift specifically for companies that use relatively uncomplicated workloads and a rigid pattern of consumption as such companies can take advantage of reduced cost through reserved pricing.
When to Use Snowflake
Snowflake is nowhere close to Redshift and in fact is much better at handling high degrees of flexibility and multi cloud requirements. Their use case tends to be businesses that require data to be utilized on multiple cloud clients or businesses that have a highly volatile workload. Thanks to its mature SQL language powering Snowflake, it is an appropriate option for anything from big data science, data analysis and complex queries. The solution is intuitive and can automatically scale up, making it a good solution for organizations that need to deploy services quickly and don’t want to maintain operations for very long. In essence, if a lot of compute is required but only for short windows, Snowflake would be the better one.
Pros and Cons of Redshift vs Snowflake
How to Choose the Right Platform for Your Needs
Key Questions
Before deciding, ask yourself:
What is my budget for data warehousing solutions for analytics?
Does my team have experience in AWS or other cloud platforms?
Are my workload patterns predictable or variable?
Factors to Consider
Choosing a data warehousing service for analytics is something that an organization needs to carefully consider in the context of the other technologies used by such an organization and the capabilities of the team as well as specific advanced analytics. So considering these factors and having a fundamental understanding of the merits of each of the different platforms as well as the demerits shall help the firm choose a suitable platform.
Cost Optimization with Pump
If you are looking to reduce costs on your Redshift, we will help you to reduce costs. For example, if your incentive bill is at $100, using Pump can bring it down to $44. This tool is particularly useful as your costs tend to be on the higher end because it saves more when the costs are higher. But while we are at it, why not think about the features we intend to release for Snowflake down the road. Right now the focus is cost saving for Redshift and other AWS and GCP Services.
Conclusion
To both of them Redshift vs Snowflake, there will always be a right and a wrong side to their debate and the important thing is that one side will always be preferable regardless of any in between debates depending on what objectives and what your organization has to get prioritized. Each cloud provider has its unique pros and each supports a different business model. We advise that you assess why you want to implement a Data Warehouse and if possible test both solutions.
