Firebolt Reviews & Pricing

by Firebolt Analytics | Founded 2019, San Francisco, CA

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What is Firebolt?

Industry Specialties: Serves all industries

Firebolt is a cloud data warehouse that improves query response time, analyzes business data at higher granularity and delivers a seamless user experience. It empowers organizations to build new business models around data.

Implement vectorization and SIMD capabilities to speed-up query execution by applying instructions on data batches.

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Based on 12 reviews:

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Firebolt Pricing

Based on our most recent analysis, Firebolt pricing starts at $0.

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Starting From: $0
Pricing Model: Still gathering data
Free Trial: Yes, Request for Free

Training Resources

Firebolt is supported with the following types of training:

Documentation
In Person
Live Online
Videos
Webinars

Support

The following support services are available for Firebolt:

Email
Phone
Chat
FAQ
Forum
Help Desk
Knowledge Base
Tickets
Training
24/7 Live Support

Firebolt Benefits and Insights

Why use Firebolt?

Key differentiators & advantages of Firebolt

Control Resource Consumption: Choose the optimal resource for each task and manage resource consumption to gain maximum control.
Manage Resource Requirements: Handle dynamic resource requirements on demand. Decouple storage and compute, enable parallelism and seamless resizing of nodes while mitigating the use of time-consuming and expensive resharding operations.
Run SQL Queries: Run queries on semi-structured data efficiently to avoid unnecessary costs and boost performance.
Deliver a Seamless User Experience: Analyze data at higher granularity to deliver a seamless user experience while ensuring faster query execution.

Industry Expertise

It caters to high-tech industries that support customer-facing analytics.

Firebolt Reviews

Average customer reviews & user sentiment summary for Firebolt:

12 reviews

90%

of users would recommend this product

Key Features

Cost-Based Optimizer: Optimize SQL queries for speed and performance. Build queries tailored for query execution engine.
Manipulation Functions: Cleanse, fix and organize semi-structured data through JSON manipulation functions. Leverage native array manipulation functions to run SQL queries on the data.
Vectorized Processing: Implement vectorization and SIMD to minimize query execution time. Apply instructions on column data batches to optimize CPU cache usage.
Granular Data Pruning: Pull data rows for the query at the granular level to boost performance in data lake environments.
Sparse Indexing: Create and manage sparse indexes to span huge datasets loaded in memory.
File Format: Sort, compress, index and materialize ingested data into proprietary file format to ensure efficient data indexing and merging for quick updates.