Firebolt vs Snowflake

A detailed comparison

Compare Firebolt vs Snowflake by the following set of categories:

Firebolt Snowflake
Elasticity - separation of storage and compute Yes Yes
Supported cloud infrastructure AWS only AWS, Azure, Google Cloud
Isolated tenancy - option for dedicated resources Multi-tenant dedicated resources Multi-tenant dedicated resources (Only VPS isolated)
Compute - node types 1-128 choice of any types 1-128 nodes, unknown types
Data - internal/external, writable storage External tables (used for ingestion) External tables supported
Security - data Dedicated resources for storage and compute, Encryption at rest, RBAC Separate customer keys (only VPS is isolated tenant) Encryption at rest, RBAC
Security - network Firewall and WAF, SSL, PrivateLink whitelist/blacklist control, isolated tenancy option Firewall, SSL, PrivateLink whitelist/ blacklist control, isolated for VPS only

is also a decoupled storage and compute architecture that adds storage and query optimizations for 10x better performance and increased efficiency. While it does have isolated tenancy like Snowflake, it currently only runs on AWS. 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.

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, and while by default it is multi-tenant compute and data, it can run in a Snowflake VPC. But it only provides 1, 2, 4, … 128 node clusters with no choice of node sizes. To get the biggest nodes you need to choose the biggest cluster.

Firebolt vs Snowflake - 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 Snowflake
Elasticity - (individual) query scalability 1-click cluster resize of node type, number of nodes 1-click cluster resize, no choice of node size
Elasticity - user (concurrent query) scalability Unlimited manual scaling Autoscale up to 10 warehouses. Limited to 20 DML writes in queue per table
Write scalability - batch Strong. Multi-master parallel batch Strong
Write scalability - continuous Multi-master continuous writes Limited to 20 DML writes in queue per table. 1 minute or greater ingestion latency recommended
Data scalability No limit No specified storage limit. 4XL data warehouse (128 nodes)

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.

Snowflake delivers strong scalability with its decoupled storage and compute architecture. But it is inefficient at scaling for certain queries that require larger nodes, because the only way to get larger nodes is with larger clusters. It is better suited for batch writes as well because it requires entire micro-partitions to be rewritten for each write. Snowflake also transfers entire micro-partitions over the network, which creates a bottleneck at scale.

Firebolt vs Snowflake - 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 Snowflake
Indexes Indices for data access, joins, aggregation, search None.
Query optimization - performance Index- and cost-based optimization,vectorization JIT, pushdown optimization Cost-based optimization, vectorization
Tuning Choice of any node size, indexing, Optimized F3 storage (on S3). Data access integrated across storage and cache Can only choose warehouse size not node types
Storage format Optimized F3 storage (on S3). Data access integrated across disk, SSD, RAM) Optimized micro-partition storage (S3), separate RAM
Ingestion performance Multi-master, lock-free high performance ingestion with unlimited scale for batch or continuous ingestion Batch-centric (micro-partition level locking, limit of 20 queued writes per table)
Ingestion latency Immediately visible during ingestion Batch write preferred (1+ minute interval). Requires rewrite of entire micro-partition
Partitioning F3 with sparse indexing Micro-partition / pruning, cluster keys
Caching F3 (cache) with aggregating, join, and search indexes Result cache, materialized view
Semi-structured data - native JSON functions within SQL Yes (Lambda) Yes
Semi-structured data - native JSON storage type Yes (Nested array type, compact) Limited VARIANT (single field)
Semi-structured data - performance Fast (array operations without full scans) Slow (can require full load into RAM, full scan)

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.

Snowflake has of a modern storage and compute architecture that is not completely optimized for performance. Its data access is not optimized. Snowflake has no indexing for fetching the exact data it needs. It only keeps track of data ranges within each micro-partition, which range in size from 50 MB to 150 MB uncompressed, and can overlap. Whenever Snowflake does not have the data cached locally in the virtual warehouse, it has to fetch all of the micro-partitions that might have the data, which can take seconds or longer. While Snowflake does some query plan optimization, it does not show up in the performance of queries, which are 4-6000x slower than Firebolt in customer benchmarks. The three biggest reasons are inefficient data access, a lack of indexing, and less query plan optimization. However, Snowflake does provide result set caching across virtual warehouses in addition to SSD caching within each virtual warehouse. This does deliver solid performance for repetitive query workloads after the first query. 

Firebolt vs Snowflake - 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 Snowflake
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 Limited continuous writes and concurrency, slow semi- structured data performance
Data processing engine (Exports or publishes data) Export query results Export query results or table
Data science/ML Export query results Spark, Arrow, Python connectors, integration with ML tools, export query results

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.

Snowflake has broader support for use cases beyond traditional reporting and dashboards. Its decoupled storage and compute architecture enables you to isolate different workloads to meet SLAs, and it also supports high user concurrency. But Snowflake also does not provide interactive or ad hoc query performance because of inefficient data access along with a lack of extensive indexing and query optimization. Snowflake also cannot support streaming or low latency ingestion below one minute ingestion intervals. All of these limitations exclude Snowflake from many operational use cases and most customer-facing applications that require second-level performance.

Firebolt vs Snowflake - 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 Snowflake
Administration - deployment, management Easy to deploy and resize, Easy to add indexing, change node types Easy to deploy and resize. Strong performance visibility, limited tuning
Choice - provision different cluster types on same data Choice of node types, engine sizes Choice of fixed size warehouses
Choice - provision different number of nodes Yes Yes
Choice - provision different node types Yes No
Pricing - compute Choose any node side and number. Compute costs deliver 10x or greater price-performance advantage. $2-$4+ per node. Fast analytics need large cluster ($16-32+/hour) or greater.
Pricing - storage $23/TB All storage.$23/40 per TB per month on demand/up front
Pricing - transferred data None None

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.

Snowflake as a more modern cloud data warehouse with decoupled storage and compute is easier to manage for reporting and dashboards, and delivers strong user scalability. It also runs on more than AWS. But like the others, Snowflake does not deliver sub-second performance for ad hoc, interactive analytics at any reasonable scale, or support continuous ingestion well. It is also often very expensive to scale, especially for large data sets, complex queries and semi-structured data.

Firebolt vs Snowflake - 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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