Snowflake vs. Redshift Comparison: Choosing the Right Data Warehouse
It is more about suitability than superiority between these two cloud data warehouses. After covering the following topics, you will be able to assess which one is more up your alley.
- Snowflake vs Redshift: Differences
- Snowflake vs Redshift: Pros and Cons
- Snowflake vs Redshift: Which is suitable for you?
- Key Takeaway
In an IoT solutions database or diverse data ecosystem, you will find that a cloud-based data warehouse that offers nearly limitless expansion, ease-of-use, and scalability works well. First, let us take a brief look at the two contenders separately.
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Snowflake was first launched on AWS as a SaaS platform that loads, analyzes, and reports on large data volumes. It has a pay-as-you-use model and it does away with the hefty expenses on hardware and instead, is deployed in the cloud within a short span of time, we’re taking minutes here!
In 2018, Snowflake launched on the Microsoft Azure cloud infrastructure giving customers the choice of cloud platforms. It opened up a significant opportunity and an added advantage for large corporations who work with multi-cloud deployment.
This data warehouse requires no hardware or software, thus eliminating the need for dedicated resources for setup, maintenance, and support of in-house servers. The data can be easily transferred into Snowflake with the help of an ETL solution.
Its architecture and data-sharing capabilities are what sets Snowflake apart from other software. The architecture enables storing and computing to scale separately, which means that payment for both can be done separately. Its sharing functionality allows quick sharing of governed and secure data in real time.
Amazon Redshift is a fully-managed cloud data warehouse that has been designed for large-scale data set storage, analysis, and database migrations. It has a petabyte-scale capacity. By making use of Redshift, one can use data to gain valuable business insights.
Redshift has a column-oriented database that is aimed at connecting to SQL-based clients and BI tools making it well-suited for large analytical queries against sizable data sets. The data is, therefore, available to users in real time. Redshift makes for fast performance and efficient querying.
Redshift easily balances robust customization options and easy setup and maintenance. This makes it a powerful cloud database solution. The three key aspects of Redshift are:
- Customizable performance
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Snowflake vs Redshift: Differences
In case you have already worked with Redshift and Snowflake, you must have come across the abundance of similarities between the two. However, their unique functionalities and capabilities are where their differences lie. Let us dive straight into understanding the differences between Snowflake and Redshift.
Snowflake vs Redshift: Database Features
It is very easy to share data between different accounts with Snowflake. There is no need to copy the data in order to share it with other users and customers. Snowflake is highly efficient when it comes to handling third-party data. At the moment, Redshift does not offer the same capability. Unlike Snowflake, Redshift does not support semi-structured data types such as object, array, and variant.
Let us discuss the limitations on string characters. Redshift varchar is limited to 65,535 character data types; the column length needs to be chosen ahead. In the case of Snowflake, it is limited to 16MB and the maximum string size is set as the default value. Hence, there is no performance overhead, and it is not necessary to know the string size value from the very beginning.
Snowflake vs Redshift: Maintenance
When it comes to Redshift, users have to compete over resources and have to look at the same cluster. Management has to be done with WLM queues, which can get quite challenging especially when it comes to understanding a complex set of rules.
On the other hand, Snowflake does not have a problem in this area. One can start different-sized data warehouses to look at the same data without the need to copy it, thus making it easier to allocate the data to different users and tasks.
Snowflake is a great performer when it comes to table vacuuming and analysis. Redshift experiences challenges in scaling up or down. Plus, it becomes greatly expensive and can end up in significant downtime. Storage and computing are separate in Snowflake and as a result, there is no need to copy data to scale up or down. The data computing capacity can be switched as you see fit.
Snowflake vs Redshift: Pricing
Usually, it is advisable to consider long-term benefits before looking at the price tag. Snowflake and Redshift both have on-demand pricing. Associated features are, however, packaged separately. Both have very different pricing models.
Snowflake pricing is done separately for storage and computing usages. Additionally, concurrency scaling is automatically included with all editions of Snowflake.
Redshift pricing, on the other hand, packages both storage and computing together. It offers a fixed amount of concurrency scaling daily, which is charged per second once the usage is exceeded.
There is a possibility of scoring large discounts with Redshift if a one- or three-year contract is committed. It offers the option to pay by the hour, by type, and nodes in each cluster or by the measure of scanned bytes.
Snowflake packs five editions, which also come with additional features having an ascending level of price. One can choose to do away with some of the features and do cost-cutting on the features that are not aligned with one’s business requirements. The volume, types of data, geographical location, and cloud platform determine the different editions.
As a result, it can be concluded that Redshift is less costly as compared to Snowflake when it comes to on-demand pricing. But, to actually see savings, you will have to commit to Redshift’s one- or three-year RI.
Before choosing Snowflake or Redshift, it is essential to consider the necessary resources for the business’ specific volume of data, data processing, and requirements for data analysis. Choosing the right data warehouse will deliver an enhanced long-term ROI through constant improvement of speed, accuracy, and efficiency of data-driven actions.
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Snowflake vs Redshift: Security
Security is at the heart of any big data project. Consistently maintaining it can be challenging with new data sources regularly opening up new potential vulnerabilities. Subsequently, a huge gap will form between new data and secured data.
It is not really a case of Amazon Redshift vs Snowflake when it comes to security as they both offer tight security. While Snowflake tackles security and compliance through a nuanced approach, Redshift does it in a comprehensive manner.
Redshift includes features and tools such as access management, sign-in credentials, Amazon Virtual Private Cloud (VPC) load data encryption, cluster security groups, data in transit, cluster encryption, and SSL connections.
Snowflake also offers tools and features similar to Redshift for security and compliance with regulatory bodies. Since certain features are absent in some of the versions, one has to be careful of the edition while choosing.
The end-to-end encryption that is offered by Redshift can be customized to meet any security requirement. One can also isolate the network within a VPC and link it via a VPN to an already existing IT infrastructure. AWS CloudTrail integration helps with auditing to meet compliance requirements.
Leaving aside Snowflake’s always-on encryption and VPC/VPN network isolation capabilities, its key difference from Redshift lies in Snowflake’s scope of security and compliance capabilities. Security and compliance capabilities grow stronger depending on the edition. Consider all the provisions that you will require before deciding on a suitable edition.
Snowflake vs Redshift: Integration and Performance
If a company is already leveraging AWS services, CloudWatch, DynamoDB, Athena, etc., Redshift seems like the natural choice for seamless integration. Snowflake, however, does not have the same integrations, and it can prove to be a challenge to integrate with the data warehouse with AWS services and tools.
Snowflake does have on-demand functions though and makes up for the lack of the above-mentioned integrations with a variety of other options such as Apache Spark, Qlik, IBM Cognos, Tableau, etc. In this regard, both software have an edge.
Both Redshift and Snowflake offer free trials as well as proof-of-concept support for companies to receive first-hand experience in terms of the value that the software delivers.
All in all, it seems like Snowflake is a better platform to start off and grow with, while Redshift proves to be a solid cost-effective solution for enterprise-level applications.
Snowflake vs Redshift: Pros and Cons
Let us explore the pros and cons of Redshift and Snowflake to give you a better understanding of which one is more suitable for your requirements.
Amazon Redshift Pros
- It is extremely user-friendly.
- It requires very little administration.
- It provides seamless integration with a variety of AWS services.
- Its spectrum can run complex queries with ease if the data is stored on Amazon S3 and the scaling of the compute and storage is allowed independently.
- It provides simple, safe, and reliable backup.
- It provides on-demand pricing by the hour for both data storage and compute power per node.
- It is good for aggregating or denormalizing data during reporting.
- It has superfast querying capabilities for analytics. It also allows concurrent analysis.
- It provides multiple formats for data output including JSON.
- Anyone with a background in SQL can use PostgreSQL syntax and easily handle the data.
- It provides strong database security capabilities with Amazon’s extensive integrated compliance program.
Amazon Redshift Cons
- It is not suitable for transactional systems.
- Sometimes, an older version of Redshift has to be used while waiting for a new patch by AWS.
- Redshift Spectrum charges more based on scanned bytes.
- It lacks modern features and data types.
- Its dialect is similar to PostgreSQL 8.
- It, sometimes, has issues with hanging queries in external tables.
- You have to rely on other means to verify the integrity of the transformed tables and no constraints are enforced.
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- It is extremely user-friendly and compatible with most other technologies.
- It is easy to set up and run.
- It provides seamless integration with Amazon AWS.
- It is ideal for businesses that run mainly on the cloud.
- It has a highly intuitive built-in SQL interface.
- It provides straightforward integration with the cloud platforms.
- It supports a wide range of third-party partners and technologies.
- It provides secure views and user-defined functions.
- It charges data storage and compute separately and are based on different cloud providers and tiers.
- Account-to-account data sharing is possible through database tables.
- True SaaS can be integrated with data storage, cloud services, and query processing.
- It is not the right option if on-premise technology is used and it does not easily integrate with cloud-based services.
- Whenever a virtual warehouse is started, a minute’s worth of Snowflake credits is used up and after that, it is charged by the second.
- Snowflake’s SQL editor needs to be updated to manage the autocomplete functions better than now.
Snowflake vs Redshift: Which is suitable for you?
To further understand which solution is suitable for specific requirements, let us compare the two data warehouse solutions in question. We will be revisiting what we have covered earlier but with business requirements in mind:
- Features: Redshift comes in a bundle of compute and storage, providing the capacity to scale to an enterprise-level data warehouse. But, by splitting compute and storage and offering tiered editions, Snowflake splits the two and offers tiered-based editions that give flexibility to companies to buy only what they need while retaining the ability to scale at the same time.
- JSON: If we are looking at JSON storage, Snowflake’s support is more robust than Redshift. This gives Snowflake the ability to store and query JSON with native built-in functions. In the case of Redshift, JSON is split into strings that make it a much harder query to work with.
- Security: Are you thinking about only what you need or everything that you could ever need? Redshift’s customizable encryption solutions do sound impressive, but think again! because you might find that Snowflake’s security and compliance is more specific to your data strategy.
- Data duties: Tasks like data vacuuming and compression with Redshift can get pretty hands-on when it comes down to maintenance. In this regard, Snowflake is the clear winner as it automates tasks and solves the issues, saving time.
Gauging the above-mentioned factors against your data strategy and requirements will clarify which data warehouse solution is suitable for you.
Snowflake and Redshift are both prime considerations when it comes to business intelligence. The choice between Snowflake or Redshift will be relative to your resources, business requirements, and data strategy. It has to be based on your daily usage patterns and the amount of data you will have to handle.
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