Firebolt is also a decoupled storage and compute architecture that adds storage and query optimizations for 10x better performance and increased efficiency. It also allows SQL to be run against external data formats to support ingestion. It also lets you choose any engine node type and number for each engine (cluster.) But it currently only runs on AWS.
Redshift has the oldest architecture with the best options. It does not separate storage and compute. While it now has RA3 nodes which allow you to scale compute and only cache the data you need locally, all compute still operates together. You cannot separate workloads. While you can only run Redshift as an isolated workload on AWS, it has the most options on AWS, including the ability to deploy it in your own VPC.
Firebolt provides the same scalability benefits of a decoupled storage and compute architecture. It improves compute efficiency through its optimizations, and by allowing the choice of any sized node and number of nodes for each cluster. It also improves write scalability and supports continuous ingestion. Firebolt also improves network efficiency by only accessing the data ranges needed, not entire partitions.
Redshift is limited in scale because even with RA3, it cannot distribute different workloads across clusters. While it can scale to up to 10 clusters automatically to support query concurrency, it can only handle a maximum of 50 queued queries across all clusters by default. In addition, because it locks at the table level, it is better suited for batch ingestion and limited in its write throughput.
Firebolt has clearly demonstrated storage and compute optimization, along with indexing, make a big difference in performance. Benchmarks by Firebolt, customers and prospects have demonstrated 4-6000x performance gains across a wide range of queries compared to any of the alternatives. This comes in part from more efficient storage access, where its F3 format and remote data access only fetches the data needed, not entire partitions. Query optimization, combined with extensive indexing also make a big difference as demonstrated through specific query examples of the impact of primary, aggregating and join indexes. Choice of any size and number of nodes for each engine helps as well. Firebolt also added native semi-structured data support and continuous, low latency ingestion.
Redshift does provide a result cache for accelerating repetitive query workloads and also has more tuning options than some others. But it does not deliver much faster compute performance than other cloud data warehouses in benchmarks. While its storage access is more efficient, with smaller data block sizes being fetched over the network, it does not perform a lot of query optimization, and has no support for indexes. It also has less support for semi-structured data or low-latency ingestion at any reasonable scale.
Firebolt offers many of the same benefits as Snowflake with its decoupled storage and compute, particularly isolation of workloads and support for high user concurrency. It is the only cloud data warehouse that has optimized compute and storage together for faster ingestion, network and query performance. Its F3 format enables sub-second network access. Indexing and query optimization enables sub-second query performance. It uniquely enables continuous ingestion at scale as well. This makes Firebolt not only well suited for reporting and dashboards, but also much better for interactive and ad hoc use cases, as well as operational and customer-facing use cases.
Redshift was originally designed to support traditional internal BI reporting and dashboard use cases for analysts. Without second-level performance, it cannot support any interactive and ad hoc analytics. It also has a limit of 50 queued queries by default, which limits concurrency, and a lack of support for continuous ingestion. All of these limitations mean Redshift for operational and customer-facing use cases.
Firebolt is the only data warehouse with decoupled storage and compute that supports ad hoc and semi-structured data analytics with sub-second performance at scale. It also combines simplified administration with choice and control over node types and 10x or greater efficiency for the best price-performance. This makes it the best choice for ad hoc, high performance, operational and customer-facing analytics.
Redshift, while it is arguably the most mature and feature-rich, is also the most like a traditional data warehouse in its limitations. This makes it the hardest to manage, and costly overall for traditional reporting and dashboards, and not as well suited for the newer use cases.