About five years ago, there was plenty of hype surrounding big data and analytics. Today, the industry has mainly lived up to the hype and transformed into the underlying force that drives businesses forward.
Over the years, the sheer amount of data generated every second has grown exponentially. This has led to the emergence of enterprise cloud data warehouse technology that’s highly efficient in handling analytics workloads.
Data warehouses are now critical to efficiently utilizing data to derive deep insights. So now we’re often asked questions like, Snowflake vs. Redshift, what’s the difference? Which is the best cloud data warehouse solution?
There are several cloud warehouse solutions, but they all offer different performance characteristics (depending on the type of workload), competitive pricing models, discounts, security protocols, and scalability options.
If we take a look at the powerful relational DBMS database models Snowflake and Amazon’s Redshift, there are more similarities than differences. However, these differences are quite significant.
What is Redshift?
Redshift can be described as a fully-managed, cloud-ready petabyte-scale data warehouse service that can be seamlessly integrated with business intelligence (BI) tools. So all you have to do is Extract, Transform, Load (ETL) into the warehouse to start making smarter business decisions.
Amazon makes it quite easy for you to start out with a few hundred gigabytes of data and scale up or down seamlessly, based on immediate demands. This allows businesses to leverage their data to acquire valuable business insights about themselves or their customers.
To launch your cloud data warehouse, you have to launch a set of nodes known as a Redshift cluster. Once you have provisioned the cluster, data sets can be uploaded to run data analysis queries.
Regardless of the size of your data set, you can take advantage of fast query performance by using the same SQL-based tools and BI applications.
Like Redshift, Snowflake is also a powerful relational database management system. It’s offered as an analytic data warehouse for both structured and semi-structured data that follows a Software-as-a-Service (SaaS) model.
This means that it’s not built on top of an existing database or a big data software platform (like Hadoop). Instead, Snowflake uses an SQL database engine with unique architecture that was specifically designed for the cloud.
This data and analytics solution is also fast, user-friendly, and offers more flexibility than traditional data warehouses.
If you have used both Redshift ETL and Snowflake ETL, you’ll already know that there’s an abundance of similarities between the two solutions. However, there are additional unique capabilities and other functionalities that come with each platform.
If you’re considering running your data analytics workload entirely on the cloud, for example, the similarities between these two robust cloud data warehousing solutions are far greater than their differences.
Snowflake offers cloud-based data storage and analytics in the form of the Snowflake Elastic Data Warehouse. In this scenario, users can analyze and store data using cloud-based hardware and software.
Next, data will be stored in Amazon S3. If you’re using Snowflake ETL, you can leverage the public cloud ecosystem without using technologies like Hadoop.
Both of these cloud warehouse systems are powerful and offer some unique features when it comes to managing data. But, there are definitely differences.
To choose the right solution for your company, you should also compare integrations, database features, maintenance, security, and (of course) costs.
Snowflake vs. Redshift: Integration and Performance
If your company is already working with AWS, then Redshift might seem like the natural choice (and with good reason). However, you can also find Snowflake on the AWS Marketplace with on-demand functions.
If you’re already leveraging AWS services like Athena, Database Migration Service (DMS), DynamoDB, CloudWatch, and Kinesis Data Firehose (to name a few), the good news is that Redshift can be integrated seamlessly.
However, if you’re going to use Snowflake, it’s important to note that it doesn’t have the same integrations as Redshift. This, in turn, will make it challenging to integrate the data warehouse with tools like Athena and Glue.
However, Snowflake makes up for this with a variety of integration options like Apache Spark, IBM Cognos, Qlik, and Tableau, to name a few. As a result, you can say that both solutions are just about even (so it’s not really a case of Snowflake vs. Redshift).
While Redshift is the more established solution, Snowflake has made some significant strides over the last couple of years.
Data optimization options like materialized views and dist keys, dashboards have the potential to run up to 150 faster than the source databases.
Snowflake vs. Redshift: Database Features
Snowflake makes it quite easy to share data between different accounts. So if you want to share data, for example, with your customers, you can share it without ever having to copy any of the data.
This is a highly efficient approach to working with third-party data and could become the norm across platforms. But at the moment, Redshift doesn’t offer the same kind of support. In fact, Redshift doesn’t support semi-structured data types like Array, Object, and Variant. But Snowflake does.
When it comes to Strings, Redshift Varchar limits data types to 65535 characters. You also have to choose the column length ahead.
In Snowflake, Strings are limited to 16MB and the default value is the maximum String size (so there’s no performance overhead). As a result, you don’t have to know the String size value at the beginning of the exercise.
Snowflake vs. Redshift: Maintenance
With Amazon’s Redshift, users are forced to look at the same cluster and compete over available resources. In fact, you have to use WLM queues to manage it, and this can be quite challenging when you consider the complex set of rules that have to be understood and managed.
This problem doesn’t exist with Snowflake. You can seamlessly start different data warehouses (of various sizes) to look at the same data without copying it. So, these can be allocated to different users and tasks quite easily.
When it comes to Vacuuming and Analyzing the tables regularly, Snowflake provides a turnkey solution. With Redshift, this can become a problem as it can be challenging to scale up or down.
Redshift Resize operations can also quickly become extremely expensive and lead to significant downtime. As compute and storage are separate in Snowflake, you don’t have to copy data to scale up or down. You can just switch data compute capacity at will.
Snowflake vs. Redshift: Security
For any successful big data project, security will be at the heart of all activities. However, this can be difficult to maintain consistently as every new data source can potentially open up new vulnerabilities. This can lead to a gap between the data that’s being generated and the data that’s being secured.
When it comes to security, it’s not a case of Snowflake vs. Redshift as both products boast enhanced security. However, Redshift also provides features and tools to manage it like Access management, Amazon Virtual Private Cloud, Cluster encryption, Cluster security groups, Data in transit, Load data encryption, Sign-in credentials, SSL connections, and Sign-in credentials.
Snowflake also offers similar tools and features to ensure security and compliance with regulatory bodies. But you have to be aware of which edition you’re working with as the security features aren’t available across all versions.
Snowflake vs. Redshift: Costs
Both Snowflake ETL and Redshift ETL have very different pricing models. If you take a closer look, you’ll find that Redshift is less expensive when it comes to on-demand pricing. Both solutions provide 30% to 70% discounts for companies that choose to prepay.
With one-year or three-year Reserved Instance (RI) pricing, you can access additional savings that you’ll miss out on a standard on-demand pricing model. Redshift calculates costs based on a per hour per node basis.
So you can calculate your monthly commitment as follows:
Redshift Monthly Cost = [Price Per Hour] x [Cluster Size] x [Hours per Month]
Snowflake’s charges heavily depend on your monthly usage pattern. This is because each bill is generated at hour granularity for each virtual data warehouse. Furthermore, data storage costs will also be separate from computational costs.
For example, storage costs on Snowflake can start at an average compressed amount at a flat rate of $23 per terabyte. This will be accrued daily and billed each month. But compute costs will be approximately $0.00056 per second or per credit (on Snowflake On Demand Standard Edition).
However, this can quickly become confusing because Snowflake offers seven tiers of computational warehouses with the smallest cluster costing one credit or $2 per hour. These costs will double as you go up a level.
As a result, it’s safe to conclude that Redshift is less expensive compared to Snowflake on-demand pricing. But to benefit from significant savings, you’ll have to sign up for their one or three-year RI.
Snowflake vs Redshift: General Pros & Cons
Amazon Redshift Pros
- Amazon Redshift is highly user-friendly.
- It also demands very little administration. For example, all you have to do is create a cluster, select a type of instance, and then manage scaling.
- It can be seamlessly integrated with a variety of AWS services (in the world’s largest cloud ecosystem of capabilities).
- If your data is stored on Amazon S3, Spectrum can easily run complex queries. You just have to allow scaling of the compute and storage independently.
- It’s highly suitable for aggregating/denormalizing data in a reporting environment.
- Provides lightning-fast querying for analytics and allows for concurrent analysis.
- Offers multiple data output formats, including JSON.
- Developers with an SQL background can leverage PostgreSQL syntax and work with the data seamlessly.
- On-demand reserved instance pricing that covers both compute power and data storage, per hour and per node.
- In addition to enhanced database security capabilities, Amazon also has an extensive integrated compliance program.
- Provides safe, simple, and reliable backups.
Amazon Redshift Cons
- Not appropriate for transactional systems.
- Sometimes you have to roll back to an old version of Redshift while you wait for AWS to release a new patch.
- Amazon Redshift Spectrum will charge extra, based on the bytes scanned.
- Redshift lacks modern features and data types, and the dialect is a lot like PostgreSQL 8.
- There can be problems with hanging queries in external tables.
- To verify the integrity of transformed tables, you’ll also have to rely on other means. What’s more, constraints are not enforced.
- Snowflake works great for enterprises that operate primarily on the cloud.
- This data warehouse solution is extremely user-friendly and compatible with most other technologies.
- The SQL interface that’s built in is also highly intuitive.
- Integration is straightforward because Snowflake itself is a cloud-based data warehouse.
- Easy to set up and get running.
- Supports an extensive ecosystem of third-party partners and technologies.
- Set and Forget Model: True SaaS can be integrated with cloud services, data storage, and query processing.
- Data storage and compute charges will be based on different tier and cloud providers and charged separately based.
- Allows secure views and secure user-defined functions.
- Account-to-account data sharing can be enabled through database tables.
- Integrates seamlessly with Amazon AWS.
- If you’re running a business using on-premise technology which doesn’t easily integrate with cloud-based services, Snowflake is the wrong option.
- A minute’s worth of Snowflake credits will also be used whenever you start a virtual warehouse but charged by the second after that.
- Snowflake’s SQL editor should also be updated to manage autocomplete functions much better than it’s doing right now.
The choice between Redshift and Snowflake will be relative to your resources and specific business demands. For example, if your organization is tasked with managing massive workloads that can range from the millions to billions, then the clear winner here is Redshift.
While their offering is cost-effective, companies also have the option of reducing their expenses by choosing query speeds at a lower price point for daily-active clusters.
As Redshift is a popular Amazon product, there’s also detailed documentation and support that can help your team overcome any potential hurdle that may lie ahead. However, at its most basic, your data warehouse decision has to be made based on your daily usage patterns and the amount of data you’re going to work with.
Regardless of which cloud data warehouse you choose to go ahead with, Xplenty can help you get your data in there safely and securely. So feel free to reach out to one of our in-house experts.