Firebolt vs Redshift

A detailed comparison

Compare Firebolt vs Redshift by the following set of categories:

Firebolt Amazon Redshift
Elasticity - separation of storage and compute Yes Redshift RA3, Spectrum only
Supported cloud infrastructure AWS only AWS only
Isolated tenancy - option for dedicated resources Multi-tenant dedicated resources Isolated tenant, use own VPC
Compute - node types 1-128 choice of any types Any size, and types
Data - internal/external, writable storage External tables (used for ingestion) Internal writable, external (Spectrum)
Security - data Multi-tenant with customer keys or isolated tenant for storage and compute. Encryption at rest RBAC Isolated/VPC tenant for storage and compute, Encryption at rest, RBAC
Security - network Firewall and WAF, SSL, PrivateLink whitelist/blacklist control, isolated tenancy option Firewall, SSL, PrivateLink whitelist/blacklist control, isolated/VPC tenancy

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 vs Redshift - Architecture

The biggest difference among cloud data warehouses are whether they separate storage and compute, how much they isolate data and compute, and what clouds they can run on.

Firebolt Amazon Redshift
Elasticity - (individual) query scalability 1-click cluster resize of node type, number of nodes Manual
Elasticity - user (concurrent query) scalability Unlimited manual scaling Autoscale up to 10 clusters, 15 queries per cluster, 50 queued queries total, max.
Write scalability - batch Strong. Multi-master parallel batch 1 master cluster
Write scalability - continuous Multi-master continuous writes Limited (table-level locking)
Data scalability No limit Only with RA3: 128 RA3 nodes, 8PB of data.

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 vs Redshift - Scalability

There are three big differences among data warehouses and query engines that limit scalability: decoupled storage and compute, dedicated resources, and continuous ingestion.

Firebolt Amazon Redshift
Indexes Indices for data access, joins, aggregation, search None.
Query optimization - performance Index- and cost-based optimization,vectorization JIT, pushdown optimization Limited cost- based optimization
Tuning Choice of node size indeexing, Optimized F3 storage (on S3). Data access integrated across storage and cache Choice of (limited) node types
Storage format Optimized F3 storage (on S3). Data access integrated across disk, SSD, RAM) Native Redshift storage (not Spectrum)
Ingestion performance Multi-master, lock-free high performance ingestion with unlimited scale for batch or continuous ingestion Batch-centric (table-level locking)
Ingestion latency Immediately visible during ingestion Batch-centric (minute-level)
Partitioning F3 with sparse indexing Distribution, sort keys
Caching F3 (cache) with aggregating, join, and search indexes Result cache
Semi-structured data - native JSON functions within SQL Yes (Lambda) Limited
Semi-structured data - native JSON storage type Yes (Nested array type, compact) No
Semi-structured data - performance Fast (array operations without full scans) Slow (flattens JSON into table)

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 vs Redshift - Performance

Performance is the biggest challenge with most data warehouses today.
While decoupled storage and compute architectures improved scalability and simplified administration, for most data warehouses it introduced two bottlenecks; storage, and compute. Most modern cloud data warehouses fetch entire partitions over the network instead of just fetching the specific data needed for each query. While many invest in caching, most do not invest heavily in query optimization. Most vendors also have not improved continuous ingestion or semi-structured data analytics performance, both of which are needed for operational and customer-facing use cases.

Firebolt Amazon Redshift
Reporting Yes Yes
Dashboards Fixed view, dynamic / fast queries, changing data Fixed view
Ad hoc Sub-second to seconds first-time query performance Sec-min first-time query performance
Operational or customer-facing analytics (high concurrency, continuously updating / streaming data) Yes. Support continuous writes at scale, fast semi-structured data performance Slower query performance. Limited continuous writes and concurrency, limited semi- structured data support
Data processing engine (Exports or publishes data) Export query results Unload data as Parquet
Data science/ML Export query results Invoke ML (SageMaker) in SQL

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 vs Redshift - Use cases

There are a host of different analytics use cases that can be supported by a data warehouse. Look at your legacy technologies and their workloads, as well as the new possible use cases, and figure out which ones you will need to support in the next few years. They include:

Reporting where relatively static reports are created by analysts against historical data, and used by executives, managers, and now increasingly by employees and customers

Dashboards created by analysts against historical or live data, and used by executives, managers, and increasingly by employees and customers via Web-based applications

Interactive and ad hoc analytics within dashboards or other tools for on-the-fly interactive analysis either by expert analysts, or increasingly by employees and customers via self-service

High performance analytics that require very large or complex queries with sub-second performance.

Big data analytics using semi-structured or unstructured data and complex queries or functionality

Operational and customer-facing analytics built by development teams that deliver historical and live data and analytics to larger groups of employees and customers

Firebolt Amazon Redshift
Administration - deployment, management Easy to deploy and resize, Easy to add indexing, change node types Easy to provision, harder to configure and manage
Choice - provision different cluster types on same data Choice of node types, engine sizes No
Choice - provision different number of nodes Yes Yes
Choice - provision different node types Yes Yes(Limited)
Pricing - compute Choose any node side and number. Compute costs deliver 10x or greater price-performance advantage. $0.25-13 per node on demand, 40% less for up front
Pricing - storage $23/TB Stored data only. RA3: $24 per TB per month. S3: AWS S3 costs.
Pricing - transferred data None Spectrum: $5 per TB scanned (10MB min per query)

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.

Firebolt vs Redshift - Cost

This is perhaps the strangest, and yet the clearest comparison; cost. There are a lot of differences in the details, but at a high level, the main differences should be clear.

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