Firebolt vs BigQuery: 6 Comprehensive Differences

With the data growing at a very fast pace, the requirements to store and process data are also increasing. Businesses use Data Warehouses to store the data. Cloud Data Warehouses have become the trend and with big players like Amazon, Google, and Snowflake already in the market, the newest entrant Firebolt has shaken up the fight. The Firebolt vs Bigquery is currently trending in the searches.

Google BigQuery is a cloud-based Data Warehouse from Google. It is a serverless and highly scalable warehouse. It provides various modules like BigQuery ML, BigQuery Omni, BigQuery BI engine for business agility. It was launched in May 2010. Firebolt is a relatively newer Data warehouse. It has a petabyte scale and is around 4x – 600x times faster than the existing solutions. It is also highly scalable and can perform analysis faster.

This article will provide a comprehensive guide on Firebolt and Google BigQuery. Also, it will provide a comprehensive Firebolt vs BigQuery comparison that will help to understand the tools better.

Introduction to Firebolt

Firebolt is a modern data warehouse solution that prides itself on offering a cloud experience that can handle data at a large scale while providing superior performance improvements. One of Firebolt’s main mantras is that it is a data warehouse built by data engineers for data engineers.  This is encompassed by the fact that Firebolt is relatively easy to scale since it employs a decoupled storage and compute architecture that enables workloads to be matched with the right resources as scaling can be done through one-click commands.

Firebolt is, therefore, a comfortable choice for modern data engineering and development teams as production-grade data applications can be built almost seamlessly by leveraging the speed, scale, and elasticity that Firebolt delivers. Another feature of Firebolt that makes it a logical choice for many data engineering teams is its programmability. Firebolt commands and operations can be triggered programmatically via its REST API, JDBC, and SDKs. 

Introduction to Google BigQuery

Google BigQuery is a fully managed cloud-based data warehouse that operates on a serverless architecture. It offers a big data analytic web service for processing very large datasets over petabytes of data. It is a Platform as a Service (PaaS) data warehouse that supports the querying of data using ANSI SQL designed for analyzing data on a large scale, up to billions of rows.

BigQuery automatically allocates computing resources whenever needed so you do not need to provide instances or virtual machines to use BigQuery and it is built to process read-only data. The platform utilizes columnar storage that makes data querying and aggregation of results easier and more efficient, therefore, leading to an agile business model. 

BigQuery offers column-level security that allows for the checking of identity and access status, creating security policies as all data is encrypted in transit by default.

To learn more about Google BigQuery, visit BigQuery’s Official Site.

Firebolt vs BigQuery: Comparison Overview

Aspect	Firebolt	BigQuery
Separation of storage and compute	Yes	Yes
Supported cloud infrastructure	AWS	Google Cloud
Isolated tenancy – option for dedicated resources	Multi-tenant metadata layer	Multi-tenant pooled resources
Service Type	Cloud-based data warehouse with focus on ultra-fast analytics and real-time performance.	Serverless, fully-managed data warehouse for analytics at scale.
Storage Format	indexed file format for fast data retrieval.	Columnar format (Capacitor) for efficient analytics.
Integration	Focus on real-time data sources like Kafka, Snowflake, and ELT/ETL pipelines. Primarily designed for use with modern data tools.	Tight integration with the Google Cloud ecosystem (e.g., Google Analytics, Dataflow, Pub/Sub), as well as third-party data tools.
SQL Support	Fully supports SQL queries with additional features like semi-structured data handling (e.g., JSON).	Fully supports standard SQL and BigQuery ML for built-in machine learning capabilities.
Scalability	Can handle the largest data volumes and concurrency on a single comparable cluster size	Scales very well to large data volumes, and automatically assigns more compute resources when needed
Indexes	Primary indexes, aggregating indexes, join indexes.	None
Performance	Optimized for sub-second query response times due to advanced indexing, data skipping, and SSD-based storage.	High performance on large datasets, with auto-scaling compute resources but may have slightly higher latency compared to Firebolt in real-time scenarios.
Use Cases	– Real-time analytics.- Low-latency, high-concurrency workloads.- Interactive query performance.	– Large-scale data analytics.- Ad-hoc queries on massive datasets.- Seamless integration with Google services.
Pricing	Pay-per-use pricing for both storage and compute resources.	On-demand pricing based on the amount of data processed per query, or flat-rate pricing for predictable costs and large workloads.

Firebolt vs BigQuery: Key Differences

In this section, a comprehensive review of Firebolt vs BigQuery will be performed. The review will narrow down on the key differences between them as relates to the six criteria listed below.

• Firebolt vs BigQuery: Key Differences: Architecture
• Firebolt vs BigQuery: Key Differences: Scalability
• Firebolt vs BigQuery: Key Differences: Performance
• Firebolt vs BigQuery: Key Differences: Pricing
• Firebolt vs BigQuery: Key Differences: Security
• Firebolt vs BigQuery: Key Differences: Ease of Usage/Data Type

1) Firebolt vs BigQuery: Key Differences – Architecture

One of the main differentiating factors to consider when choosing a data warehouse is its architecture. The architecture on which a data warehouse is built defines things like its access to storage and compute resources, the speed, and performance of data operations, and its scalability, therefore Firebolt and BigQuery are no exception. Under this section, we will look at the differences between Firebolt vs BigQuery showing their flexibility, the cloud infrastructure they support, the computing, services, and external data arrangement using the overall architecture as the guide. 

• Firebolt: Firebolt utilizes a shared-nothing architecture that enables flexible scaling of compute nodes while relying on S3 for data storage. It effectively separates the computing process from its storage. Its additional storage and query optimization give room for ten times better performance and increased efficiency. It supports AWS cloud infrastructure only and allows SQL to be run against external formats to support ingestion. Firebolt supports both multi-tenant and isolated tenancy options for computing and storage and it also allows you to choose the type of node engine alongside specifying the number of nodes you want to run in each cluster.
• BigQuery: Google BigQuery was one of the first data warehouses to separate compute and storage. BigQuery’s architecture is a serverless, highly scalable, and cost-effective cloud offering that possesses Massively Parallel Processing (MPP) used to query data by reading thousands of rows in a second. In BigQuery, data is stored in replicated, distributed units and processed in compute clusters made up of nodes. BigQuery is a fully managed and flexible data warehouse that supports Google Cloud only and allows you to leverage the processing power of Google’s massive infrastructure by transferring data into BigQuery where querying of your data is done using standard SQL queries.

2) Firebolt vs BigQuery: Key Differences – Scalability

Scalability which is generally defined as a system’s ability to increase or decrease its resources to meet demands is an important metric in all data operations. Data warehouses are expected to be scalable especially because we are in the age of big data where scalability is measured in line with performance levels of dedicated resources. Below is a comparison of Firebolt vs BigQuery in the scalability department.

• Firebolt: The scalability of Firebolt is partly derived from the benefits associated with architectures that separate their compute and storage operations as this improves efficient optimization and allows for the selection of nodes to form a cluster. It also supports write scalability and continuous ingestion of data by users at any time as it has unlimited manual scaling, strong multi-master parallel batch processes with unlimited continuous updates.
• BigQuery: BigQuery separates its compute and storage therefore users can decide how to scale the processing and memory resources based on their needs. Petabytes of data are executed both vertically and horizontally leading to high scalability of data in real-time. Scalability on BigQuery is automatic and allocation of each query on-demand, or reserved and flex slots is possible but limited to 100 concurrent users by default but can easily be upgraded to higher limits.

3) Firebolt vs BigQuery: Key Differences – Performance

The performance of data warehouse solutions has to do with indexing, query optimization performance, ingestion performance/latency, and semi-structured data performance. Below is the performance comparison of Firebolt vs BigQuery.

• Firebolt: Firebolt was built from the ground up with performance considerations and huge datasets in mind. As a result of this new approach to data architecture that combines the best from the worlds of high-performance database architectures and the infinite storage of data lakes, Firebolt boasts performance improvements of between 4-6000x when compared to similar data warehouses and query engines. Firebolt’s indexes for data access, join, and aggregation greatly increases the performance level. This is made possible from Firebolt’s efficient storage F3 format and remote data access where only required data is fetched and not the entire partition. Having the choice to choose the size and number of nodes for each cluster also greatly enhances the performance of Firebolt along with its native semi-structured data support and continuous, low latency ingestion.
• BigQuery: BigQuery supports partitioning of storage and computes as it separates both operations thereby resulting in improved query performance. BigQuery does not use indexing but has lower latency for message-based injection by ingesting one row at a time, therefore, making it immediately available for querying. It delivers fast and large query speeds on data sets with sizes up to a petabyte and data can easily be queried using standard SQL or through Open Database Connectivity (ODBC).

4) Firebolt vs BigQuery: Key Differences – Pricing

Pricing information varies from one data warehouse product to another as the resources on offer are different. However, pricing usually has to do with computing and storage pricing, compute of instant types, and provision of additional nodes.

• Firebolt: Firebolt is easy to deploy and resize, add indexing, and change the instance type, therefore, providing on-demand and pre-purchasing pricing plans. Since Firebolt’s storage and compute are separated, you can choose any node size or number of nodes to pay for making it a cost-effective approach for small-scale businesses, enterprises, and corporations alike. Firebolt also supports ad hoc and semi-structured data analytics.
• BigQuery: BigQuery platform offers on-demand and flat-rate pricing subscription models. In on-demand pricing, you are charged for the number of bytes processed by each query, and with flat-rate pricing, you purchase slots regarded as virtual CPUs for dedicated processing capacities to run queries. In both subscription models, you are charged for the amount of data returned from each querying known as analysis pricing, and for the amount of data storage used known as storage pricing. 

5) Firebolt vs BigQuery: Key Differences – Security

All corporations that engage in data projects want to know first and foremost that their data is safe. Data security is, therefore, a huge part of any data warehouse offering. Below is a comparison of the security features of Firebolt vs BigQuery.

• Firebolt: Firebolt architectural security network supports Firewall and WAF, SSL, PrivateLink whitelist/blacklist control, isolated tenancy option, etc.
• BigQuery: Google BigQuery is compliant with security standards like Firewall (Google Cloud), HIPAA, FedRAMP, PCI DSS, ISO/IEC, TLS, whitelist/blacklist control part of GCP, SOC 1, 2, 3, etc.

6) Firebolt vs BigQuery: Key Differences – Ease of Usage/Data Type

This section deals with how easy it is to use either of the data warehouse products and the data types they handle. 

• Firebolt: Firebolt is relatively easy to use when stacked against other data warehouse solutions as it doesn’t force you to give up on granularity over your data. You can analyze your data over various time frames and create reports that are suitable for informative dashboards. Firebolt does require you to have solid SQL and Data Warehouse knowledge, and it supports JSON, XML, Avro, Parquet. 
• BigQuery: BigQuery is a very user-friendly platform that requires common knowledge of SQL commands, ETL tools and it supports JSON, XML, BigQuery ML.

Conclusion

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

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