Snowflake vs Redshift – How to choose the better one for you?

Snowflake vs Redshift – How to choose the better one for you?

Data has become the quintessence of businesses. In modern days, more and more key business decisions are being data-driven. To understand, analyse and utilize the data, data warehouses have become critical business solution tools. Enterprises looking to modernize their data warehousing infrastructure are increasingly looking at cloud-based, horizontally scalable data warehouses as part of their strategy.

Of the many cloud based data warehousing platforms two of the most popular are Snowflake and Amazon Redshift. Both have transformed the volume, speed, and quality of business understanding and key-decision making. Choosing one over the other doesn’t necessarily mean that one service is better than the other, it is identifying which solution makes the most sense for your business.

Amazon Redshift

RedShift is defined as a fully-managed, cloud-ready petabyte-scale data warehouse service that can be seamlessly integrated with business intelligence tools. Here you can just start with a few hundred gigabytes of data and scale up or down according to your requirements.

Redshift is a great cloud data warehouse, in a way it kicked off the trend to migrate to cloud data warehouses. 


Snowflake is a powerful RDBMS that offers an analytic data warehouse for both structured and semi-structured data as a Software-as-a-Service (SaaS) model. This means that Snowflake uses an SQL design and architecture that was specifically designed for the cloud making it fast, user-friendly and more flexible than a traditional data warehouse.


If you have used both Snowflake and Redshift you would have noticed far more similarities than differences. However, there are additional features and functions that come with each platform.


If your organization is already working with AWS then it makes Redshift a better option. This is primarily because Redshift can integrate seamlessly with other AWS services like Glue, Athena, CloudWatch etc.

While you can find Snowflake on the AWS marketplace with on-demand functions, it’s important to note that it is challenging to integrate Snowflake with AWS data warehouse tools like Athena and Glue.

However, Snowflake has a variety of integration options for tools outside the AWS marketplace such as Apache Spark, IBM Cognos, Tableau etc.


Snowflake is completely automated and does not require any maintenance activity from the user.

For Redshift, regular Vacuum/Analyse tables is required. This can become a huge headache with very large tables. Snowflake manages this out of the box.

In Redshift all the users need to compete over the same resources. WLM queues are used to manage this. Snowflake allows you to simply start warehouses of different sizes that look at the same data, without copying any data, and allocate them to different users


Both Snowflake and Redshift use columnar storage and massively parallel processing (MPP) for simultaneous computation. However, the key difference is in Snowflake the storage layer is separated from the compute layer in order to optimize their individual performance. This change in architecture makes it difficult to declare a clear winner when it comes to performance.

Snowflake has a lower query run time on unoptimized queries. Redshift query runtime for unoptimized queries improves if the same query is run much more frequently, this is because  Redshift compiles code for the query and cache it. The compiled code is shared across sessions in a cluster, so the same query with different parameters in the different sessions will run faster because of no overhead. Also, in Redshift we can tune our queries and data structures by using the SORTKEY and DISTKEY, which would improve run times for queries involving WHERE and JOIN.

Snowflake services layer does all the query planning and query optimization based on data profiles that are collected automatically as the data is loaded. It automatically collects and maintains the required statistics to determine how to most effectively distribute the data and queries across the available compute nodes.

So, no indexes, no need to figure out partitions and partition keys, no need to pre-shard any data for even distribution, and no need to remember to update statistics. Snowflake also offers a clause called PARTITION BY which helps with WHERE clause, but there are no ways to optimise JOIN in Snowflake.


The main advantage in going for cloud-based data warehouses is the ability to scale. Hence the ease of scalability is a major factor in choosing the platform.

Due to its unique architecture of separating the compute layer and storage layer, Snowflake has a clear advantage in terms of scale. Hence if the query load increases then we can scale the compute layer independently. Thus Snowflake offers instant scaling without the need for redistribution of data. Its auto concurrency allows users to set a minimum and maximum cluster size and the clusters will scale automatically based on the demands.

Redshift can also scale but not as instant as Snowflake. It takes anywhere between adding minutes to hours to add new nodes to its clusters.

Data Structure

Snowflake works well with multiple structured and semi-structured data formats such as JSON.

Redshift follows pure columnar storage format and is optimized for structure data storage making it hard to query JSON data.


Redshift has an end-to-end security which can be tailored to fit your requirements. Integration with AWS CloudTrail provides auditing to help meet compliance.

Snowflake offers different editions based with varying levels of security. Hence you can opt for a lower edition if full security is not required for you.


Snowflake and Redshift have very different pricing models. Redshift calculates costs based on per hour per node basis. So your monthly commitment is

Redshift monthly cost=[Price per Hour]*[Cluster Size]*[Hours per Month]

Snowflake offers a dynamic pricing model – clusters will stop when no queries are running and automatically resume when they are, and they can flexibly resize themselves based on a changing workload. This can potentially save you money when query load decreases.

Both solutions provide 30% to 70% discounts for companies that choose to prepay.


s mentioned before both platforms are brilliant cloud data warehouses and the decision to choose should be based on your data strategy.

  • Query Load: If the query load is going to be high and clusters are going to be running full day then Redshift makes more sense. Snowflake can be used when your query load is scattered across the day and there is idle time for the cluster.
  • Maintenance: Redshift requires a more hands-on approach in terms of maintenance as the likes of data vacuuming and compression is not automated. Snowflake automates these tasks making it easier to maintain.
  • Integration: If you plan to use AWS components in different modules in your architecture then Redshift makes it easier to integrate.  But by splitting computation and storage and offering tiered editions, Snowflake provides businesses the flexibility to purchase only the features they need while preserving the potential to scale.
  • Security: If the need is a full suite of security and compliance then Redshift is the better choice. Snowflake allows varying levels of security during different phases and an option to compromise on compliance in the initial stages for cost savings.
  • Data Handling: Snowflake’s support to handle and query JSON data is more robust than Redshift. In Redshift, the JSON is split into strings making it harder to handle.

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