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.
Introduction to Firebolt
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.
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Introduction to Snowflake
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.
Get clarity on Snowflake pricing – our Snowflake calculator simplifies it all in a few clicks.
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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 Summarised Comparison of Firebolt vs Snowflake
| Aspect | Firebolt | Snowflake |
| Indexes | Indexes for data access, joins, aggregation | None |
| Query Optimization – Performance | Index- and cost-based optimization, vectorization JIT, pushdown optimization. | Cost-based optimization, vectorization |
| Tuning – CPU, RAM, SSD, Storage | Choice of any node size (CPU/RAM/SSD), tuning options | Can only choose warehouse size, not node types |
| Type of data processing | real-time data processing and analysis. | batch processing, real-time processing, and stream processing. |
| 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 | Sparse indexing | Micro-partition / pruning, cluster keys |
| Caching | F3 (cache) (Aggregate index, join index) | Result cache, materialized view |
| Security | end-to-end encryption, role-based access control, and data masking. It also supports integration with external security tools like AWS KMS and Azure Key Vault. | multi-factor authentication, encryption at rest and in transit, and access control. It is compliant with various industry standards and regulations like SOC 2, HIPAA, and GDPR. |
| Developer tools & integration | SDK for building custom connectors, data transformation functions, and UDFs. It also provides integration with popular development tools like VS Code and Git. | developer tools like Git and Jenkins. |
| Semi-structured Data – Performance | Fixed view, dynamic, changing data | Fixed view |
Understanding the Differences between Firebolt and Snowflake
The following are the key differences between Firebolt and Snowflake:
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.
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.
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Conclusion
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.
Learn more about, Firebolt vs BigQuery.
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You can try Hevo for free by signing up for a 14-day free trial. You can also have a look at the unbeatable Hevo Pricing that will help you choose the right plan for your business needs!
FAQs
1. Is Firebolt better than Snowflake?
Firebolt offers faster query speeds for real-time, small-scale analytics, while Snowflake is more versatile, scalable, and handles larger, more complex workloads. The choice depends on performance needs versus flexibility.
2. What is the difference between Snowflake and Redshift?
Snowflake is fully cloud-native, auto-scales compute/storage separately, and supports semi-structured data well. Redshift requires more manual tuning, is tightly integrated with AWS, and scales less easily.
3. What is the Snowflake equivalent in AWS
The AWS equivalent to Snowflake is Amazon Redshift for cloud-based data warehousing. AWS Athena (serverless query) and S3 (storage) also offer similar functionalities when combined.
Skand is a dedicated Customer Experience Engineer at Hevo Data, specializing in MySQL, Postgres, and REST APIs. With three years of experience, he efficiently troubleshoots customer issues, contributes to the knowledge base and SOPs, and assists customers in achieving their use cases through Hevo's platform.
