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Snowflake was one of the first decoupled storage and compute architectures, making it the first to have nearly unlimited compute scale and workload isolation, and horizontal user scalability. It runs on AWS, Azure and GCP. It is multi-tenant over shared resources in nature and requires you to move data out of your VPC and into the Snowflake cloud. “Virtual Private Snowflake” (VPS) is its highest-priced tier, and can run a dedicated isolated version of Snowflake. Its virtual warehouses can be T-shirt sized along an XS/S/M…/4XL axis, where each discrete T-shirt size is bundled with fixed HW properties that are abstracted from the users. Snowflake has recently added support for Snowflake managed Iceberg tables.
Athena is serverless and built on a decoupled storage and compute architecture that queries data directly in S3, without the need to ingest/copy the data. It runs in multi-tenancy with shared resources. Users do not have control over the compute resources Athena chooses to allocate per query from the shared resource pool. For folks requiring additional or dedicated resources, they can reserve dedicated processing capacity in the form of Data Processing Units (DPU), with each DPU providing 4 vCPU and 16 GB RAM. RPU allocation ranges from 24 - 1000 per region.
Firebolt is built on a natively decoupled storage & compute architecture, on AWS only. Data has to be copied outside of your VPC into the Firebolt, where both your compute and data run in a dedicated and isolated tenant. A “Firebolt Engine” can be granularly configured across # of nodes and different CPU/RAM/SSD combinations.
Snowflake scales very well both for data volumes and query concurrency. The decoupled storage/compute architecture supports resizing clusters without downtime, and in addition, supports auto-scaling horizontally for higher query concurrency during peak hours.
Athena is a shared multi-tenant resource, with no guarantees on the amount or availability of the resources allocated for your queries. From a data volume perspective, it can scale to large volumes, but large data volumes can suffer from very long run times and frequent time outs. Query concurrency is maxed at 20. If scalability is a top priority, Athena is probably not the best choice.
Firebolt can handle the largest data volumes and concurrency on a single comparable cluster size, thanks to its superior hardware efficiency. Thanks to its decoupled storage & compute architecture it scales very well to large data volumes. However, resizing an engine size isn’t instant and requires orchestration if avoiding downtime is necessary. A single Firebolt engine can support hundreds of concurrent queries, avoiding the need to scale out for most use cases. Scaling horizontally for even higher concurrency is manual.
Snowflake typically comes on top for most queries when it comes to performance in public TPC-based benchmarks when compared to BigQuery and Redshift, but only marginally. Its micro partition storage approach effectively scans less data compared to larger partitions. The ability to isolate workloads over the decoupled storage & compute architecture lets you avoid competition for resources compared to multi-tenant shared resource solutions, and the ability to increase warehouse sizes can often enhance performance (for a higher price), but not always linearly. Snowflake’s recently released “Search optimization service” delivers index-like behavior for point queries, but comes at an additional cost.
Athena, (and Presto) are designed to query data where it is, sacrificing storage-compute optimizations. This makes it very convenient for easy and immediate querying but at the expense of performance. This typically puts Athena behind cloud data warehouses in terms of performance. But Athena still does relatively well in performance benchmarks, especially when external storage is managed by experts. While it supports partitions, there is no support for indexing, and together with the fact that resources are pooled from a shared multi-tenant service, low-latency and consistent performance are not Athena’s sweet spot. A cloud data warehouse be more performant better than Athena in most cases.
Firebolt is the fastest when it comes to query performance when compared to cloud data warehouses and services like Athena. Its unique approach to storage and indexing results in highly aggressive data pruning that scans dramatically less data compared to other technologies. While other technologies scan partitions or micro-partitions, Firebolt works with indexed data ranges that are significantly smaller. In addition, Firebolt lets users accelerate queries further with multiple index types (Aggregating index, Join index), and using its decoupled storage & compute architecture workloads can be easily isolated to guarantee consistent performance.
Snowflake is a well rounded general purpose cloud data warehouse, that can also span beyond traditional BI & Analytics use cases into Ad-Hoc and ML use cases. Thanks to the flexible decoupeld storage & compute architecture that allows you to isolate and control the amount of compute per workload, it’s possible to tackle a broad spectrum of workloads. However, like its close siblings Redshift & BigQuery, it struggles to deliver low-latency query performance at scale, making it a lesser fit for operational use cases and customer-facing data apps.
Athena is a great choice for Ad-Hoc analytics. You can keep the data where it is, and start querying without worrying about hardware or pretty much anything else, given that Athena is serverless and takes care of everything behind the scenes. However, it is not a great fit when you need consistent and fast query performance, and/or high concurrency. This is why it is typically not the best choice for operational and customer-facing applications. It can be also easily and flexibly used for batch processing, which is often leveraged for ML use cases.
Firebolt stands out by being the fastest cloud data warehouse when compared to Snowflake, Redshift, BigQuery and Athena. It’s great for delivering sub-second analytics at scale, while remaining hardware efficient and high concurrency friendly. This makes it a great choice for operational use cases and customer-facing data apps. Given that it is not as feature-rich and integration rich as the more mature data warehouses makes it a lesser fit for a general-purpose Enterprise data warehouse. It is also not the best fit for ad-hoc use cases, because of the need to predefine indexing at the table level.
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