Why Is Snowflake Better Than AWS? | NextPhase.ai

Let’s look at some ways Snowflake is better than AWS and Redshift, so it’s easier for you to choose between these solutions.

JSON Function and Query Support
One major way Snowflake was clearly superior to AWS for a long time was JSON function and Query support. While things have changed significantly in recent times, there are still many ways that Snowflake still remains the superior solution for certain use cases.

Snowflake has always been equipped to support both structured and semi-structured data, thanks to native support for JSON queries and functions. AWS Redshift, on the other hand, made use of a purely columnar storage format that is optimized for the storage of structured data. While it would be possible to use JSON with Redshift, the support was not native and there would be major speed implications.

However, AWS has now introduced native JSON support in Redshift, bringing the two solutions much closer than before. It has also implemented support for PartiQL, a query language that works with semi-structured data. However, in spite of these changes, Snowflake remains a better option for those using Parquet, Arvo, or XML.

Instant Auto-Scaling
One feature that Snowflake has always had is instant and automatic scaling. Scaling is often a big consideration for businesses looking for a cloud-hosted data warehouse solution. With Snowflake, the scaling process takes advantage of the fact that storage and compute clusters are entirely independent of each other. This means that you can scale up in one or both areas in a matter of seconds, without the need to copy any data.

Automated Maintenance
Maintenance with Snowflake is completely automated and can be set up in a manner to happen without the need for any end user input. When it comes to Vacuum/Analyze tables, Snowflake manages all this on its own, not the case with Redshift which requires these processes to be carried out manually.

Decoupled Storage and Computing
Snowflake has always maintained its storage and compute clusters independent of each other. The advantages of having this model of architecture are obvious, leading to more efficient operation, easier scaling up or down, and more convenient maintenance tasks with minimal user input.

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