When choosing a cloud data warehousing solution, Snowflake and Firebolt are often considered top contenders. Both platforms offer robust features but cater to different business needs. This blog dives deep into their differences across key metrics, including architecture, performance, cost, scalability, and more.

Snowflake vs Firebolt - Features Comparison 

Data Storage and Architecture

Snowflake: Snowflake’s shared data architecture separates storage and computation. This design allows users to scale resources independently, ensuring flexibility for diverse workloads.

Firebolt: Firebolt’s advanced indexing and optimized data storage are tailored for faster query performance. Its unique approach to data organization minimizes latency, making it ideal for high-speed analytics.

Performance

Snowflake: Snowflake provides excellent performance for general workloads and enterprise-level applications. Its auto-scaling capabilities support varying demands seamlessly.

Firebolt: Firebolt is engineered for speed, offering exceptional query performance for analytics-focused use cases. Its architecture is optimized for high-volume, real-time queries.

Ease of Use

Snowflake: With a user-friendly interface and a broad feature set, Snowflake is accessible to non-technical users and data teams alike.

Firebolt: Firebolt’s design prioritizes developers and data engineers, offering more detailed configurations that cater to technical users.

Scalability

Snowflake’s Scalability

Snowflake’s multi-cluster architecture allows seamless scalability. Whether you need to scale up for intensive workloads or scale down to save costs, Snowflake’s design ensures efficiency.

Firebolt’s Scalability

Firebolt scales efficiently for smaller, high-speed workloads. While it’s not as versatile as Snowflake for large-scale applications, it’s a strong contender for specialized analytics tasks.

Snowflake Challenges

If you are an existing Snowflake customer - or if you are using Redshift, Athena, Azure Synapse or Google BigQuery for that matter - you probably know this. Snowflake is good for traditional data warehouse workloads such as reporting and dashboards. That is what Snowflake was built for; to move traditional batch-based reporting and dashboard-based analytics to the cloud.

But Snowflake is not good for:

• Ad hoc analytics or anything requiring sub-second, first-time query performance
• Large complex queries against massive data sets
• Semi-structured data queries 
• Streaming analytics or continuous ingestion using technologies like Kafka
  

Snowflake has limitations, like all the other 1st and 2nd generation cloud data warehouses, that either make Snowflake not fast enough, too expensive, or both. Here’s a summary of the biggest challenges:

Batch-based ingestion

Traditional data warehouses have always been batch-oriented for a variety of reasons.  When Snowflake targeted traditional data warehouse use cases, they kept that limitation.  Snowlake locks an entire partition with each write, and limits write queues to 20 writes per table. Snowpipe and other loading mechanisms tend to batch in intervals of 1 minute or more. It is not designed for continuous ingestion.

Query performance

The first time data is needed, Snowflake pulls data from storage into a virtual warehouse and puts it into local storage. Once a query is executed, the results are stored in a cache. While this speeds up executing the same query, or executing different queries against the same data, the first query is slow.  According to the latest FiveTran benchmarks, for example, querying 1 terabyte with an 8 node virtual warehouse that costs $16-32 an hour, which is $150,000-$300,000 annually, takes 8 seconds on average to perform the queries, and as much as minutes. 

Semi-structured data

‍Snowflake can ingest JSON, capture metadata about it, store it directly in a VARIANT field (column) and process the JSON directly using native functionality. But it is slow.  Whenever Snowflake executes queries or calculations, it has to load all the JSON into available RAM first, and then do full scans to find specific fields. To get enough RAM, you need to keep doubling the size of your Snowflake virtual warehouse, which grows each node a little, until each individual node is big enough. Otherwise the JSON will cause data to spill over to disk, which makes performance plummet.

Snowflake Cost and Pricing Models‍

Perhaps you have heard of the term “credit fever,” a story of a query that broke the budget, or rules like killing a Snowflake process that runs for more than x minutes.  Snowflake charges for compute. If multi-cluster auto scaling is on, the wrong queries can devour credits. High performance or semi-structured data workloads can also consume credits because each larger warehouse size doubles the cost.   

Key differences between Snowflake and Firebolt

Firebolt, as a 3rd generation cloud data warehouse, added specific analytics innovations on top of the best of Snowflake - including innovations in storage, indexing and query optimization - to improve performance and cost. The combination has made Firebolt much better and more cost effective for ad hoc interactive, high performance, semi-structured data, and operational or customer-facing analytics that require continuous ingestion.

Some key differences from the more detailed comparison between Firebolt and Snowflake:

Performance

‍Firebolt performs orders of magnitude faster than alternatives. One customer achieved 3x faster performance and 10x lower cost, or a 30x price-performance advantage compared to their Snowflake deployment. The demo that Firebolt shows in its product showdown shows a first-time query executing in roughly 1.3 seconds on an 8 node cluster that costs roughly $1.7 an hour … against over 20 terabytes and 42 billion rows of data.

Semi-structured data

‍Firebolt stores JSON natively in a nested structure and provides native Lambda functionality within compliant SQL that does not require full scans or loading the data into RAM. The performance advantage is even greater in this case.

Firebolt Cost and Pricing Models

‍Firebolt lets you choose your AWS instance types and number of nodes for each cluster. You can have 4 massive nodes if you choose. With Firebolt, each node can deliver 10x greater efficiency, and you see the price of each node type as you select it, giving you the ability to choose the best price-performance. This combination of greater control and choice over resources, greater efficiency per node, and different pricing is what leads to a 10-100x lower total cost.

Choosing the Right Cloud Data Warehouse for Your Business

When selecting a cloud data warehousing solution, both Snowflake and Firebolt present compelling cases, but Firebolt stands out as the optimal choice for businesses prioritizing speed, efficiency, and cost-effectiveness in analytics-focused workloads. While Snowflake excels in traditional data warehousing and batch processing, Firebolt's third-generation architecture, innovative indexing, and optimized query performance cater to modern analytics needs. For organizations seeking unmatched speed, operational analytics, and lower total costs, Firebolt offers a transformative solution that outpaces its competitors. Experience the speed and efficiency of Firebolt for yourself—start your 30-day free trial today and revolutionize your data analytics!

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