Firebolt vs Snowflake: 6 Critical Differences

Last Modified: December 29th, 2022

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With organizations generating huge volumes of both Structured and Unstructured data, the demand for data storage is on the rise. Organizations need data storage options that can be accessed from any location with the ability to scale when necessary. Cloud storage was developed to meet these demands. 

Cloud storages relieve organizations from the need to purchase and set up physical infrastructure for data storage. Instead, the Cloud service provider takes up this responsibility. The Cloud also scales up and down very well to meet the changing demands for data storage space. Cloud storage can be accessed from any location using any device. These are some of the reasons why organizations are moving their data from On-premise storage to the Cloud. 

Today, there are many Cloud storage providers. Firebolt and Snowflake are good examples of Cloud Data Warehouses. In this Firebolt vs Snowflake article, we will be discussing the key differences between these two Cloud storage providers to help you choose the right one for your business. 

Table of Contents

Introduction to Firebolt

Firebolt Logo
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Firebolt is a Cloud Data Warehousing solution that helps its users to streamline their Data Analytics and access to insights. It offers Fast Query Performance and combines Elasticity, Simplicity, Low cost of the Cloud, and Innovation in Analytics. It was developed for AWS with a powerful SQL query engine that separates computes and storage, enabling users to spin up many isolated resources on a similar database. 

Businesses that use Firebolt’s Data Warehouses, deliver Petabyte-scale High-Performance and Interactive Analytics in a matter of weeks, enabling employees and analysts to analyze huge volumes of data and improve the ROI for data collection. 

The Firebolt Data Warehouse comes with all that you need to give your users an unbelievable data experience. It is 4-6000x faster than other Cloud Data Warehouse providers like Snowflake, Athena, Amazon Redshift, and others. 

Note that there are no impossible data challenges with the Firebolt Database. It is suitable for aggregating data that lacks granularity. If you need to make changes to your Schema frequently, make your Semi-Structured data ready for analytics, or your queries are too slow even after optimizing them, choose the Firebolt Data Warehouse. It turns all impossible data problems into easy everyday tasks. 

Introduction to Snowflake

Snowflake Logo
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Snowflake refers to a Data warehouse-as-a-Service (DaaS) platform developed for the Cloud. Its data architecture uses the scalable, elastic Azure Blobs Storage as the internal storage engine and Azure Data Lake to store the Unstructured, Structured, and On-premise data ingested via the Azure Data Factory. 

The Snowflake Data Warehouse provides security and protection of data using Amazon S3 policy controls, SSO, Azure SAS tokens, and Google Cloud Storage access permissions. You can also scale your storage depending on your storage needs. 

Snowflake Architecture
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In the next section, we will be discussing Snowflake vs Firebolt to know the key differences between the two cloud storages. 

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A fully managed No-code Data Pipeline platform like Hevo Data helps you integrate and load data from 150+ sources (Including 40+ Free Data Sources) to a destination of your choice such as Snowflake and Firebolt in real-time in an effortless manner. Hevo with its minimal learning curve can be set up in just a few minutes allowing the users to load data without having to compromise performance. Its strong integration with umpteenth sources allows users to bring in data of different kinds in a smooth fashion without having to code a single line. 

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  • Connectors: Hevo supports 100+ integrations to SaaS platforms, files, databases, analytics, and BI tools. It supports various destinations including Google BigQuery, Amazon Redshift, Snowflake, Firebolt Data Warehouses; Amazon S3 Data Lakes; and MySQL, MongoDB, TokuDB, DynamoDB, and PostgreSQL databases to name a few.  
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  • Scalable Infrastructure: Hevo has in-built integrations for 100+ sources like Firebolt and Snowflake, that can help you scale your data infrastructure as required.
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  • Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
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You can try Hevo for free by signing up for a 14-day free trial.

Understanding the Differences between Firebolt and Snowflake

The following are the key differences between Firebolt and Snowflake:

Download the Guide to Select the Right Data Warehouse
Download the Guide to Select the Right Data Warehouse
Download the Guide to Select the Right Data Warehouse
Learn the key factors you should consider while selecting the right data warehouse for your business.

Firebolt vs Snowflake: Performance

Snowflake uses data ranges to prune partitions during queries. However, it doesn’t use Indexing which improves performance. Firebolt uses Optimized Aggregate, Sparse, and Join Indexes for improved query performance. Snowflake supports cost-based optimization and query vectorization. However, its first-time run for queries takes seconds to minutes. Firebolt gives sub-second performance even for first queries. It uses Vectorized Processing, Cost-based Optimization, JIT compilation, and more. 

Firebolt vs Snowflake: Integrations

The types of integrations/platforms supported are an important factor when comparing Cloud storage options. Snowflake can be integrated with more than 140 data sources, Data Analysis, and Business Intelligence platforms. Examples include Alooma, Sisense, Datom, Dbschema, and others. 

Snowflake Integrations
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On the other hand, Firebolt only supports integration with three Business Intelligence platforms namely Looker, Tableau, and Sisense. Snowflake supports integration with all these tools. 

Firebolt vs Snowflake: Pricing

Snowflake’s pricing plan is based on the amount of Storage and Computes they sell you. They don’t offer any incentives to improve the efficiency or performance of each node. Firebolt charges users based on the amount of storage and computing used by query engines. Engines are billed per second and there are a large variety of engine types to fine-tune workloads.

Firebolt vs Snowflake: Handling Semi-Structured Data

The way semi-structured data is handled is an important factor when doing a Firebolt Snowflake comparison. Snowflake keeps Semi-Structured data in a VARIANT field. Snowflake creates metadata to help with processing, and all the JSON for a particular node should be loaded fully into RAM and the engine runs full scans. The process becomes slow when there is not enough RAM. 

Firebolt comes with Native Lambda Expressions and the best storage that can be used in SQL and they are efficient for storage, don’t require full scans or loads into RAM and offer a faster performance while utilizing fewer resources. 

Firebolt vs Snowflake: Optimized Storage

Snowflake comes with an Optimized Partition File system and supports sorting with Cluster Keys. However, for every write, a rewrite of the partition is needed. Snowflake doesn’t support continuous writes. 

On the other hand, the Firebolt File Format (F3) spans many tiers including RAM, SSD, and disk. Firebolt supports Multi-Master Continuous Ingestion, Automatic Rebalances, and Single-row inserts. 

Firebolt vs Snowflake: Continuous Updates

You can also compare Firebolt vs Snowflake in terms of Continuous Updates. Snowflake has a Partition-level Locking feature and a limit of 20 DML writes in the queue for each table. These two features restrict Snowflake’s ability to receive continuous updates from Kafka and other streaming technologies. 

Firebolt’s Multi-Master Writes feature helps it to support Continuous Updates at any scale. The Multi-Master writing can be done without partition or table locking across instances directly into F3. 

A Summarised Comparison of Firebolt and Snowflake

Firebolt vs Snowflake Summary
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This blog talks about the differences between Firebolt and Snowflake in great detail touching upon 6 key factors to keep in mind before making a decision for your business.

Extracting complex data from a diverse set of data sources can be a challenging task and this is where Hevo Data saves the day! Hevo Data offers a faster way to move data 100+ data sources such as Databases, SaaS applications, CRMs, etc., into your Data Warehouses such as Firebolt or Snowflake to be visualized in a BI tool. Hevo Data is fully automated and hence does not require you to code.

You can try Hevo for free by signing up for a 14-day free trial. You can also have a look at the unbeatable pricing that will help you choose the right plan for your business needs!

Nicholas Samuel
Freelance Technical Content Writer, Hevo Data

Skilled in freelance writing within the data industry, Nicholas is passionate about unraveling the complexities of data integration and data analysis through informative content for those delving deeper into these subjects.

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