Redshift vs Snowflake: 6 Key Differences

 

The Six Key Differences of Redshift vs Snowflake:

1. Snowflake charges compute separately from storage, and on a pay-as-you-use model.
2. Snowflake has better support for JSON-based functions and queries than Redshift.
3. Snowflake offers instant scaling, whereas Redshift takes minutes to add more nodes.
4. Snowflake has more automated maintenance than Redshift.
5. Redshift better integrates with Amazon's rich suite of cloud services and built-in security.
6. Redshift Spectrum extends Redshift searching across S3 data lakes.

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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 the big question is, which data warehouse is best for my business? There are three data warehouse giants that you need to consider: Amazon Redshift, Google BigQuery, and Snowflake.

We've already compared Amazon Redshift vs Google BigQuery and Google BigQuery vs Snowflake; but what about Amazon Redshift vs Snowflake?

Here at Integrate.io, we don’t have a dog in this fight. Integrate.io supports all three data warehouses so our clients can build powerful data integration pipelines into any cloud data warehouse of their choice. However, we want to make sure that our clients who are comparing Snowflake and BigQuery make the right choice. 

If we take a look at the powerful relational DBMS database models, Redshift and Snowflake, there are more similarities than differences. However, these differences are quite significant.  

Table of Contents

1. What is Redshift?
2. What is Snowflake?
3. Snowflake vs Redshift: In Depth Comparison
4. Snowflake vs Redshift: Pros & Cons
5. Conclusion

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. Each node in the cluster is then partitioned into what are known as “slices.” Each slice is allocated a portion of the node’s memory and disk space. This helps balance the workload assigned to the node which optimizes query performance. 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.

Amazon Redshift exhibits superior performance by taking advantage of internal networking components. By utilizing high-bandwidth connections, close proximity, and custom communication protocols, the system can achieve high-speed communication between nodes.

For more information on Integrate.io's native Redshift connector, visit our Integration page.

What is Snowflake?

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.

Snowflake’s architecture has the unique feature of being a hybrid of traditional shared-disk and shared-nothing models. With shared disk, the system uses a central data store to which each compute node has access. With shared-nothing, each node in the cluster stores a portion of the entire data set locally.

Snowflake is also a three-layer system that consists of:

Database Storage: Snowflake manages how the information is stored in the database for example file size, structure, and metadata.

Query Processing: Snowflake processes queries using what is known as “virtual warehouses.” Each warehouse represents a cluster node that is independent of others and does not share compute resources across virtual warehouses.

Cloud Services: This layer represents the services that tie together the different components of this system such as authentication, infrastructure management, query parsing, and access control.

This data and analytics solution is also fast, user-friendly, and offers more flexibility than traditional data warehouses. Why is that? Snowflake separates compute and storage functions in their pricing. In that way, you can pay for only the features you need. With this approach, you save costs but have the flexibility to scale as needed.

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.

For more information on Integrate.io's native Snowflake connector, visit our Integration page.

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Snowflake vs Redshift: In-Depth Comparison

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.

Also, if your data is in Amazon S3, DynamoDB, or Amazon EMR, Redshift can take advantage of Massively Parallel Processing to load data quickly. 

Redshift can also present performance challenges if the Sort and Distribution keys are not planned properly. These keys define how information is stored and accessed in the system. They can be complex to set up and maintain. As an example, once you set a distribution key on a table, it can not be changed later on. Thus, you need to fully understand these concepts before implementing them. 

Another item to consider is that Redshift is not suitable for a live app database. Although it is fast for running queries and analytics on large datasets it does not offer the same performance for live apps. You’ll need to pull the information into a caching layer if you need it for this use case.

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 hours of 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, Cluster encryption, Cluster security groups, Data in transit, Load data encryption, SSL connections, and Sign-in credentials. Access in Redshift can be granualized such that you can grant users or groups to have access to specific data in tables.

Redshift clusters can be launched inside your infrastructure Virtual Private Cloud (VPC). This allows you to restrict inbound or outbound access to your clusters. 

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. The base versions offer basic security options, while the options are more robust depending on the version you select. There is a benefit to this method. Using this approach, you can customize your system to purchase only the security features you need. 

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.

An important cost consideration for Snowflake is that, unlike Redshift, it does not need to have a data warehouse up and running just for the ETL part. Using Snowpipe integrated with Snowflake, ETL is not competing with queries for processing. This can reduce the costs as the warehouse can be smaller.

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: 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.
• Primary key and foreign keys are informational only in Redshift. The system does not enforce uniqueness. As such, you’ll need to use some other process to deduplicate data

Snowflake Pros

• 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.

Snowflake Cons

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.

In Summary

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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, Integrate.io can help you get your data in there safely and securely. If you decide to go with both data warehouses, learn more about integrating Redshift and Snowflake together. Reach out to one of our in-house experts for a 14-day free trial.