Yes, commitment based discounts are available. Contact our sales team for more information.
Yes, commitment based discounts are available. Contact our sales team for more information.
Sub-plan result caching allows Firebolt to reuse intermediate query artifacts, such as hash tables computed during previous requests when serving new requests, reducing query processing times significantly.It includes built-in automatic cache eviction for efficient memory utilization while maintaining real-time, fully transactional results.
Firebolt is engineered to handle hundreds of analytical queries per second; without compromising speed. It offers unparalleled cost efficiency with industry-leading price-to-performance ratios and scales seamlessly to handle terabytes of data with minimal performance impact.
To optimize query performance in Firebolt, follow these guidelines for selecting a primary index:
Frequently Queried Columns: Choose columns often used in WHERE clauses or joins for faster data retrieval.
Range Queries: Include columns used in range filters, like dates, to improve performance in range-based queries.
Data Distribution: Pick columns with many unique values (high cardinality) to ensure even data distribution.
Sorting: Select columns based on how data is typically sorted in queries to minimize the amount of scanned data.
For more detailed information, check out Firebolt’s comprehensive guide on primary indexes.
These steps ensure efficient data pruning and faster query execution.
Firebolt's aggregating index pre-calculates and stores aggregate function results for improved query performance, similar to a materialized view that works with Firebolt's F3 storage format. Firebolt selects the best aggregating indexes to optimize queries at runtime, avoiding full table scans. These indexes are automatically updated with new or modified data to remain consistent with the underlying table data. In multi-node engines, Firebolt shards aggregating indexes across nodes, similar to the sharding of the underlying tables.
Warming up an aggregating index preloads the data into the cache, improving query performance. Use the CHECKSUM function on a query matching the index definition to warm up the index, leading to faster execution when it is utilized.
Solution:
Use the CHECKSUM function to preload specific data into the cache. Focus on frequently accessed columns or data ranges to optimize performance and minimize cache usage.
Example:
-- Warm-up the entire table SELECT CHECKSUM(*) FROM playstats;
-- Warm-up specific columnsSELECT CHECKSUM(GameID, PlayerID, CurrentScore) FROM playstats;
-- Warm-up specific data rangeSELECT CHECKSUM(*) FROM playstats WHERE CurrentLevel BETWEEN 1 AND 5;
Warming up tables using CHECKSUM ensures data is stored in the cache, improving performance for large tables or frequently queried datasets. Use filters or column selection to target relevant data efficiently.
Querying cold data, or data not yet cached in Firebolt's local SSD storage, may result in slightly slower performance compared to querying hot (cached) data. However, Firebolt's efficient caching mechanisms ensure that even cold data is accessed quickly, minimizing the performance impact.
The Shuffle operation is the key ingredient to executing queries at scale in distributed systems like Firebolt. Firebolt leverages close to all available network bandwidth and streams intermediate results from one execution state to the next whenever possible. By overlapping the execution of different stages, Firebolt reduces the overall query latency
Firebolt’s engine uses vectorized execution, which processes batches of thousands of rows at a time, leveraging modern CPUs for maximum efficiency. Combined with multi-threading, this approach allows queries to scale across all CPU cores, optimizing performance.*
Boncz, Peter A., Marcin Zukowski, and Niels Nes. "MonetDB/X100: Hyper-Pipelining Query Execution." CIDR. Vol. 5. 2005.
* Nes, Stratos Idreos Fabian Groffen Niels, and Stefan Manegold Sjoerd Mullender Martin Kersten. "MonetDB: Two decades of research in column-oriented database architectures." Data Engineering 40 (2012).
Firebolt uses advanced query processing techniques such as granular range-level data pruning with sparse indexes, incrementally updated aggregating indexes, vectorized multi-threaded execution, and tiered caching, including sub-plan result caching.These techniques both minimize data being scanned and reduce CPU time by reusing precomputed, enabling query processing times in tens of milliseconds latency on hundreds of TBs of data.
Firebolt scales to manage hundreds of terabytes of data without performance bottlenecks. Its distributed architecture allows it to leverage all available network bandwidth and execute queries at scale with efficient cross-node data transfer using streaming data shuffle.
Firebolt engines can scale up and out to handle high-concurrency workloads. Firebolt supports adding up to 10 clusters within a single engine to manage spikes in concurrent queries. These clusters can be dynamically added on-demand, ensuring optimal performance even during peak loads.
Firebolt offers observability views through information_schema, allowing you to access real-time engine metrics. These insights help you size your engines for optimal performance and cost efficiency. Read more here- https://docs.firebolt.io/general-reference/information-schema/views.html
Firebolt has been benchmarked against several major data warehouses, including Snowflake, BigQuery, and Redshift. These benchmarks highlight Firebolt's superior performance in low-latency, high-concurrency queries, especially for fast aggregations and real-time analytics. See further details in our benchmark Github repo and our benchmark articles about handling concurency, high-volume ingestion and DML operations.
Engine consumption data is available in the information_schema.engine_metering_history table, which provides hourly usage details at the account and engine level, including resource consumption and cost metrics. This data can also be retrieved via an API request.
In Firebolt, an “engine” refers to a virtual compute resource that provides the processing power to execute queries, load data, and perform various SQL driven tasks. Unlike traditional cloud data warehouses, Firebolt engines can be resized, paused, and resumed in a much more granular, and cost effective way to optimize performance and cost.
Firebolt is ACID compliant and treats every operation as a transaction. For example, data from a COPY FROM operation is visible only after the entire operation is successful, ensuring data integrity. This eliminates partial updates ensuring data integrity at all times.
Everything in Firebolt is done through SQL. Firebolt’s SQL dialect is compliant with Postgres’s SQL dialect and supports running SQL queries directly on structured and semi-structured data without compromising speed. Firebolt also has multiple extensions in its SQL dialect to better serve modern data applications.
Yes, Firebolt integrates seamlessly with dbt (data build tool). Firebolt’s dbt adapter allows you to model, transform, and manage your data workflows using dbt. This integration combines dbt’s transformation capabilities with Firebolt’s high-performance query engine, enabling ELT workflows. You can also define models in dbt to run directly on Firebolt, helping you process large volumes of data more efficiently. For more details, visit Firebolt's blog on ELT with dbt.
Yes, transferring data between different AWS regions incurs cross-region data transfer costs according to AWS pricing. Firebolt itself does not add additional fees for cross-regional data transfers, but users should consider AWS network charges when moving data across regions.
Firebolt provides comprehensive billing view that break down both compute (engine) consumption and storage usage. You can access detailed information on engine usage through the information_schema.engines_billing table and storage usage through the information_schema.storage_billing table. These tables and UI view offer granular insights into usage by specific engines, storage by table, and usage patterns, allowing for better cost tracking and resource optimization. The billing details can be viewed by hour, day, or month in the Firebolt UI, helping users stay informed about their resource consumption.
Firebolt stores unsaved scripts in your browser’s local storage, which has a limit of around 5 MB. If multiple websites use local storage, it can get full, causing unsaved scripts in the Firebolt SQL editor to be erased.
To avoid this:
Save your scripts regularly.
Clear your browser cache/cookies to free up local storage and prevent data loss.
Remember, clearing your cache will also remove other saved data, so use this solution carefully.
Firebolt values transparency and customer feedback when planning its roadmap. To view the current roadmap or see open feature requests, reach out to Firebolt’s support or your customer success manager. Additionally, Firebolt’s team actively gathers feedback from users and considers feature requests as part of ongoing development efforts. Regular updates are communicated through newsletters and user forums. Stay connected to get insights into upcoming releases and features tailored to your needs.
Firebolt values transparency and customer feedback when shaping its AI roadmap. To explore upcoming features or see open feature requests, reach out to Firebolt’s support or your customer success manager. Firebolt actively incorporates user feedback into its AI development efforts, with regular updates shared through newsletters, user forums, and product announcements. Stay connected to gain insights into innovations like query optimization for AI apps, text-to-SQL capabilities, and other enhancements for AI Apps tailored to your needs.
An engine has three key dimensions:
Type - This refers to the type of nodes used in an engine.
Cluster - A collection of nodes of the same type.
Nodes - The number of nodes in each cluster.
An engine comprises one or more clusters. Every cluster in the engine has the same type and the same number of nodes.
Firebolt offers multiple data import options, including: COPY FROM SQL command for importing data from S3 buckets with built-in schema inference and automatic table creation. The 'Load data' wizard in the WebUI to explore, set options, infer schema, and load data into Firebolt tables. Direct read for CSV and Parquet files from S3 using read_csv and read_parquet table-valued functions. External tables for data stored in Amazon S3 buckets, supporting formats such as CSV, Parquet, AVRO, ORC, and JSON.
At present, Firebolt does not have a direct API connection to external data sources like Google Sheets. However, you can leverage third-party tools or custom ETL pipelines to load data from sources like Google Sheets into Firebolt for analysis.
Firebolt is built natively on AWS and currently does not support running directly on Google Cloud Platform (GCP) or MS Azure. You would need to use AWS as the backend for Firebolt, but you can still ingest data from other cloud platforms through various data ingestion tools and connectors, or by loading data from those platforms into S3.
Firebolt's billing is generally sent monthly, aligning with the AWS billing cycle. The bill email provides a breakdown of engine usage and storage consumption, giving you visibility into your total cost. Because Firebolt runs on AWS infrastructure, its billing is influenced by the resources consumed in AWS, and the timing of Firebolt’s billing is closely aligned with AWS bills for the same period.
There are four node types available in Firebolt: Small, Medium, Large, and X-Large. Each node type provides a certain amount of CPU, RAM, and SSD. These resources scale linearly with the node type. For example, an "M" type node provides twice as much CPU, RAM, and SSD as a "S" type node.
For more information, check out the Engine Fundamentals article in our documentation.
When deciding between a fact or dimension table in Firebolt, it's important to consider how the data will be used and queried, as this choice impacts performance and how data is handled in multi-node engines.
Fact tables are typically large and contain measurable events, like sales or sensor readings. They usually hold foreign keys to dimension tables and measures that are aggregated (e.g., sums or averages). Fact tables benefit from aggregate indexes, which optimize heavy aggregations.
Dimension tables describe the entities in fact tables, such as product details or customer information. Dimension tables are usually smaller, updated more frequently, and replicated across nodes for faster lookups. Join indexes can be applied to dimensions to speed up lookup queries.
In general, choose a fact table when you need to aggregate large volumes of data, and a dimension table for smaller, descriptive datasets primarily used for lookups. For multi-node engines, keep in mind that fact tables are sharded, while dimension tables are replicated.
Yes, Firebolt integrates with a wide range of popular BI and data tools, including Looker, Tableau, and Power BI, among others. These integrations allow users to leverage Firebolt’s performance while visualizing and analyzing data in their preferred tools. Additionally, Firebolt offers JDBC and ODBC drivers to facilitate connectivity with other tools.
Firebolt supports deployment in multiple AWS regions, allowing you to choose the most appropriate region for your data and workloads. However, Firebolt does not currently offer seamless, cross-region deployments within a single account. To deploy across multiple regions, you need to create separate accounts in each region.
Firebolt Unit is a normalized measurement of consumption. FBU normalizes consumption management irrespective of node type, number of nodes, number of clusters, duration of consumption, etc. Thanks to Firebolt’s multidimensional scaling, per-second billing, and auto-stop/start capabilities, compute consumption can be a fraction of a minute. FBU eliminates the need to keep track of individual node types, nodes, and the number of clusters. There’s no binding to specific instance types, so you are free to use pre-paid credits on any node type.
You can use anywhere from 1-128 nodes per cluster in a given engine.
For more information, check out our documentation.
In Firebolt's UI, numeric values are automatically displayed with commas for readability (e.g., 123,456,789). However, this may be undesirable for fields like IDs or other values where commas aren’t needed.
Solution:
To remove commas from numbers in the UI, CAST the numeric field to TEXT using ::TEXT. This ensures that the number is displayed as a plain text string, without commas.
Example:
SELECT
playerid AS playerid_default,
playerid::text AS playerid_text,
nickname,
email
FROM players
LIMIT 10;
In this example, playerid_default will display with commas, while playerid_text will display the number without commas.
This method only affects how numbers are displayed in the Firebolt UI and does not alter the underlying data or its formatting in external tools.
While this is coming soon, Firebolt does not natively support geospatial data types or queries. However, you can still store and manage geospatial data using standard data types like strings and numeric values, and process geospatial information via external tools or data pipelines integrated with Firebolt.
On Firebolt Data is stored in Amazon S3, which inherently offers durability and availability features leveraging copies of data stored in 3 Availability Zones per Region. However, Firebolt does not natively provide cross-region disaster recovery (DR) at this time, so manual processes would need to be in place for cross-region DR setups. Compute High Availability across Availability Zones is a roadmap item.
You can use up to 10 clusters per engine.
For more information, check out our documentation.
Use PARTITION BY when you need to split the table into distinct data segments for better data management or to prune large amounts of data quickly. Partitioning allows for efficient data removal (e.g., ALTER TABLE...DROP PARTITION).
Use the Primary Index when you want to organize the order of data for optimal query performance. The primary index helps Firebolt efficiently prune data during queries based on filter conditions.
Example:
If you often query by playerid but also need to manage data by tournamentid, you could use playerid in the primary index and tournamentid in PARTITION BY. This would allow you to both optimize query performance and manage large data segments.
CREATE TABLE playstats_partition (
playerid integer,
tournamentid integer,
stattime timestampntz
) PRIMARY INDEX playerid
PARTITION BY tournamentid;
If you encounter errors due to missing credentials when accessing AWS S3 from Firebolt, ensure that you have the correct IAM roles and policies assigned. Alternatively, you can provide AWS keys directly within your external table definition using the CREDENTIALS parameter. Check your AWS permissions and Firebolt’s documentation for troubleshooting credential errors.
Firebolt does not yet support automatic cross-region replication. If you need to replicate data across regions, you will need to handle the data replication process manually using external tools or services like AWS DataSync or S3 cross-region replication.
Each node type consumes a specified number of FBUs per hour. Compute consumption is billed in one-second increments. For example, a type ‘M’ node consumes 16 FBUs per hour. The same node running for one minute will consume FBU calculated as such: Consumed FBU = (Available FBU per hour / 3600) x ( 1 x 60 seconds) = (16/3600) x 60 = 0.27 FBUs.
No. Engines and databases are fully decoupled in Firebolt. A given engine can be used with multiple databases, and conversely, multiple engines can be used with a given database. On Firebolt, all engines can write to the same database. No need to segregate engines as read-write and read-only.
For more information, check out our Engine Permissions Documentation.
To implement LEFT() and RIGHT() string functions in Firebolt, you can use the SUBSTR() function, as Firebolt does not natively support these functions.
LEFT() Alternative
To replicate the LEFT() function, use SUBSTR() to extract characters from the left side of a string. For example:
SELECT SUBSTR(nickname, 1, 6) FROM players WHERE nickname = 'murrayrebecca';
-- This returns "murray"
This extracts the first 6 characters from the string.
RIGHT() Alternative
For the RIGHT() function, combine SUBSTR() with LENGTH() to extract characters from the right side of the string. For example:
SELECT SUBSTR(nickname, LENGTH(nickname) - 6) FROM players WHERE nickname = 'murrayrebecca';
-- This returns "rebecca"
This extracts the last 7 characters by calculating the length of the string and subtracting the desired number of characters.
These methods allow you to achieve the same functionality as LEFT() and RIGHT() using SUBSTR() in Firebolt.
Firebolt can be integrated with Coralogix through OpenTelemetry. Firebolt’s OTel Exporter allows you to export Firebolt engine metrics, query logs, and other telemetry data to any OpenTelemetry-compatible platform, including Coralogix. This integration enables real-time monitoring and troubleshooting, giving you better insights into engine performance, query execution, and resource usage. You can refer to Firebolt's GitHub repository for additional setup details and code samples.
No. While there is no theoretical limit on the number of databases you can use with a given engine, note that the configuration of your engine will determine the performance of your applications. Based on the performance demands of your applications and the needs of your business, you may want to create the appropriate number of engines.
For more information, check out our Engine Permissions Documentation.
While streaming ingestion is on the roadmap, Firebolt currently don't have a native straming ability. However, Firebolt has the ability to run hiligh preforment micro-batching to persist data to S3 in Parquet or Avro format for near real-time ingestion.
This error occurs when Firebolt cannot convert data from a text format (e.g., CSV or TSV) to the expected column data type defined in the external table schema.
Common Scenarios:
Mismatched Data Types: If a column contains a value that doesn’t match the expected type (e.g., a string in a numeric column).
Example: A file contains the value "abc" in a column defined as LONG, which leads to the error.
Header Rows in Files: If a CSV file includes a header row and it's not excluded, Firebolt tries to interpret the header text as data.
Solution: Use SKIP_HEADER_ROWS in the TYPE parameter of the CREATE EXTERNAL TABLE DDL.
Troubleshooting Tip: Use a text editor to inspect the first few rows of the file for mismatches. If the issue isn’t obvious, use SELECT...LIMIT and OFFSET to locate problematic rows and identify the file using the SOURCE_FILE_NAME column.
Example query:
SELECT SOURCE_FILE_NAME, COUNT(*)
FROM (SELECT *, SOURCE_FILE_NAME FROM my_external_table LIMIT 10000 OFFSET 0)
GROUP BY SOURCE_FILE_NAME;
System settings in Firebolt allow you to control query execution behavior and performance, providing flexibility when needed. This is particularly useful when you want to override default settings for specific queries via the REST API.
To adjust settings such as the time_zone, you can embed them directly in the URL of your API call. For example, if you need to set the time_zone to UTC, include the parameter in the API call URL.
Example API call:
curl --location 'https://<user engine URL>?engine=<engine_name>&database=<database_name>&time_zone=UTC' \
--header 'Authorization: Bearer <authentication_token>' \
--data "SELECT TIMESTAMPTZ '1996-09-03 11:19:33.123456 Europe/Berlin'"
This query sets the time_zone system setting to UTC for the duration of the query. Each new API call requires you to include the necessary system settings again if you want to apply specific overrides.
The considerations for splitting into separate databases include governance, logical isolation, and performance aspects related to metadata caching. Here are the key points:
Governance and Isolation: Different databases can have different owners and permissions, allowing for better governance. This is particularly important when different teams or departments manage their own data.
Logical Grouping: Currently, without support for custom schemas, databases serve as the primary mechanism to logically group tables and views. This will change when custom schemas are introduced.
Performance on Metadata Caching: The packdb caches metadata per database. A single large database with all tables may complicate this caching process, although the practical impact is likely minimal except in specific scenarios.
Cross Database Queries: At present, cross-database queries are not supported, making it impractical to have a separate database for each table if joins are required. When cross-database queries are supported, they may incur some performance degradation compared to querying within the same database due to metadata storage methods.
Security: From a security perspective, Role-Based Access Control (RBAC) can be applied at the table level to restrict access to specific users, enhancing data security.
In summary, while there are some advantages to splitting databases, such as improved governance and security, the current limitations regarding cross-database queries and potential performance issues should be carefully considered before making a decision.
The typical start-up time for a Firebolt engine is 10-15 seconds, but this is not guaranteed due to potential resource constraints on AWS.
For more information, check out our Sizing Engines Documentation.
Start with a small node type (CREATE ENGINE ingest_engine TYPE=S NODES=1) and monitor CPU and RAM utilization via information_schema.engine_metrics_history. Scale out the engine (e.g., ALTER ingest_engine SET NODES=4) as needed to increase throughput. As a general rule of thumb, most ingestion workloads benefit from paralellism, specifically when importing multiple files. Adding to that, Firebolt will be even more efficient when files are roughly equivalent in size.
When using a NOT IN filter, rows where the column value is NULL are excluded from the results, even though NULL is not in the list of values. This is because SQL treats comparisons with NULL as UNKNOWN, which prevents those rows from being returned.
How to include NULL in NOT IN results:
To include rows with NULL values, add an explicit condition checking for NULL using OR column IS NULL.
Example:
SELECT *
FROM players
WHERE playerid NOT IN (1, 2, 3) OR playerid IS NULL;
This query will include rows where playerid is either NOT IN the list or is NULL, ensuring that NULL values are part of the result set.
While this is on our roadmap, Firebolt does not natively integrate with Delta Lake or Databricks. However, you can use data transfer solutions to migrate data between Firebolt and Databricks or Delta Lake via standard ETL tools, enabling the two platforms to coexist in a broader data architecture
Queries are saved in information_schema.engine_query_history for as long as the engine remains active. However, the engine_query_history view is cleared upon an engine restart unless you have enabled the Persistent Query History feature.
All operations in Firebolt can be performed via SQL or UI. To create an engine, you can use the “CREATE ENGINE” command (shown above), specifying a name for the engine, number of clusters the engine will use, number of nodes in each cluster and the type of the nodes used in the engine. After the engine is successfully created, users will get an endpoint that they can use to submit their queries. For example, you can create an engine named MyEngine with two clusters, each with two nodes of type “M” as below:
CREATE ENGINE IF NOT EXISTS MyEngine WITH TYPE = “M” NODES = 2 CLUSTERS = 2;
This creates an engine named "MyEngine" with two clusters, each containing two nodes of type "M". For more details, see the documentation.
Firebolt boosts data ingestion performance through parallel processing, multi-node scaling as the engine grows, and pipelined execution for efficient resource use. Using COPY FROM enables linear scaling with the number of nodes, accelerating ingestion speed with larger engines—ideal for latency-sensitive ELT scenarios.
Firebolt is continuously expanding its integration ecosystem to support a wide range of data sources and connectors. If your preferred connector isn't listed in the current documentation, don’t worry! Firebolt’s development team is actively working on adding new integrations, and you can expect ongoing enhancements to its capabilities.
In the meantime, you can reach out to Firebolt support to inquire about upcoming connectors or even request a specific integration. Firebolt also supports custom connectors through its API and can integrate with many systems using standard protocols like JDBC and ODBC, giving you the flexibility to connect to external sources in various ways.
Firebolt provides multidimensional scaling to help right-size workloads. Autostop and Autostart are features that help reduce costs by eliminating idle time. Firebolt also provides global visibility of consumption and costs through built-in organizational governance and account-level consumption breakdown.
The behavior of quote escaping is controlled by the setting standard_conforming_strings. When this setting is enabled (the default behavior), backslashes are treated literally, and strings are parsed without escaping. This ensures consistent handling of literal strings and avoids unexpected transformations. If standard_conforming_strings is disabled, backslashes can be used as escape characters, altering how strings are interpreted. For more information, check our documentation.
In Firebolt, you can scale an engine across multiple dimensions. All scaling operations in Firebolt are dynamic, meaning you do not need to stop your engines to scale them.
Scale Up/Down You can vertically scale an engine by using a different node type that best fits the needs of your workload.
Scaling Out/In You can horizontally scale an engine by modifying the number of nodes per cluster in the engine. Horizontal scaling can be used when your workload can benefit by distributing your queries across multiple nodes.
Concurrency Scaling Firebolt allows you to add or remove clusters in an engine. You can use concurrency scaling when your workload has to deal with a sudden spike in the number of users or number of queries. Note that you can scale along more than one dimension simultaneously. For example, the command below changes both the node type to “L” and the number of clusters to two.
ALTER ENGINE MyEngine SET TYPE = “L” CLUSTERS = 2
;
All Scaling operations can be performed via SQL using the ALTER ENGINE statement or via UI. For more information on how to perform scaling operations in Firebolt, see the Guides section in documentation.
Yes, Firebolt supports data migration from Redshift through standard ETL tools. You can move data from Redshift to Firebolt by exporting Redshift data to S3 and then using Firebolt’s COPY FROM command to ingest data into Firebolt tables.
Firebolt provides engine consumption and spend information in the Web UI. Additionally, granular engine-level consumption can be found via the information_schema.engine_metering_history view that details the hourly consumption of all the engines within an account. Users can also drill down into how the topology of their engines (node type, number of nodes and number of clusters) was modified over time, providing visibility into the FBU consumption of their engines.
Use ARRAY_SORT to sort one array and apply the same order to the other. For example, if you have array1 and array2:
array1: [4, 1, 3, 2]
array2: [Z, X, Y, R]
SELECT
ARRAY_SORT(x, y -> y, ARRAY_AGG(array2), ARRAY_AGG(array1)) AS sorted_array2
FROM your_table;
This ensures the order in array1 is applied to both arrays, maintaining their alignment.
Output:
sorted_array1: [1, 2, 3, 4]
sorted_array2: [X, R, Y, Z]
For more information, check our documentation.
No. Scaling operations in Firebolt are dynamic and do not require stopping the engine, so your applications will not experience downtime.
For more information, check out our Engine Fundamentals Documentation.
Firebolt provides a custom Airflow connector that allows you to orchestrate and automate your Firebolt data workflows directly from Airflow. This integration helps in managing ETL processes, scheduling queries, and handling data pipelines efficiently.
Yes, during our POC process, Firebolt's team will provide you with fast and accurate cost estimates based on real usage data. During the POC, our team will closely support you, analyzing engine usage, query patterns, and resource consumption to deliver a precise cost breakdown. With our efficient benchmarking and expert guidance, you’ll quickly understand your projected costs, ensuring transparency and confidence in scaling with Firebolt.
Yes, customer access is managed via Auth0, while organizational access is controlled using Okta. All accesses are logged and monitored, and alerts are in place for any unauthorized configuration changes across our systems.
Use the source_file_name virtual column to filter rows based on the Parquet file name. For example:
SELECT $source_file_name, *
FROM external_table
WHERE $source_file_name ILIKE '%filename.parquet%';
This query retrieves rows where the source_file_name contains the specified file name. %filename.parquet% can be replaced with any pattern to match your file name.
For more information, see Using metadata virtual columns docs
There is no limit to the number of regex expressions you can use with REGEXP_LIKE_ANY.
Your queries will continue to run uninterrupted during a scaling operation. When you perform horizontal or vertical scaling operations on your engine, Firebolt adds additional compute resources per your new configuration. While new queries will be directed to the new resources, the old compute resources will finish executing any queries currently running, after which they will be removed from the engine.
For more information, check out our Engine Consumption Documentation.
While this is on our roadmap, Firebolt currently does not have a native integration with Kafka. However, you can ingest Kafka data into Firebolt using intermediate storage systems like S3.
We use tools like SCA, SAST for code analysis, along with practices such as Fuzzing, scanning for pipeline weaknesses (like the use of unverified external sources), and secret scans as part of our secure software development lifecycle.
Yes, an AWS account is linked to an organization. However, it is not possible to link accounts within an organization to different AWS accounts, billing is on the Organization level. For more information, check our documentation.
For high concurrency, use multiple clusters within your engine. Clusters help handle more simultaneous queries by distributing the load. Keep in mind that cache is shared across nodes in a cluster, but not between clusters, so the right balance depends on your workload. You can also consider using auto-scaling to dynamically adjust resources based on demand.
First, on your S3 account, confirgure the permission policy found in the help center article, https://docs.firebolt.io/Guides/loading-data/configuring-aws-role-to-access-amazon-s3.html#use-aws-iam-roles-to-access-amazon-s3. While still in your AWS Identity and Access Management (IAM) Console, start the process to upload data through the plus sign icon in the develop space. After selecting an ingestion engine, you can select 'IAM Role' as your authetnication method and you can create an IAM role in the application. Copy the trust policy here and follow the rest of the instructions in the article to apply to your AWS account. Note that you don't actually have to upload anything to create the IAM role.
You can label a query by setting the query_label system setting before running it:
cursor.execute("set query_label = '<label>';")
cursor.execute("your_query_here")
Here’s a full example using the Firebolt Python SDK:
id = '****'
secret = '****'
connection = connect(
database="<db_name>",
account_name="<account_name>",
auth=ClientCredentials(id, secret)
)
cursor = connection.cursor()
cursor.execute("start engine <engine_name>")
cursor.execute("use engine <engine_name>")
cursor.execute("use database <database_name>")
cursor.execute("set query_label = '123';")
cursor.execute("select 1;")
print(cursor.fetchone())
connection.close()
Yes, semi-joins (implemented via WHERE IN clauses) can be more performant than explicit joins, as Firebolt has built-in optimizations that leverage semi-joins for better data pruning. Using semi-joins helps reduce intermediate row counts earlier in query execution, especially beneficial for high-cardinality datasets.
In Firebolt's query profiling, CPU time refers to the actual processing time on CPU cores, while thread time represents the total wall-clock time across all threads and nodes. When thread time is significantly higher than CPU time, it typically indicates waits due to data loading from storage (like S3) or node concurrency constraints. This distinction helps diagnose bottlenecks related to IO-bound or compute-bound workloads.
Firebolt proatively maintains a status page at https://firebolt.statuspage.io/ where we keep you notified about any active incidents that may cause interruption to your access or services. From this page, you can also hit the 'subscribe' button to stay informed by phone, RSS, email, or Slack.
Query performance in high-cardinality joins is significantly impacted by data cardinality, joins resulting in large intermediate row outputs, and data shuffles across nodes. Firebolt users should leverage the EXPLAIN ANALYZE functionality to identify expensive operations such as table scans, joins, and shuffles. Reducing data volume before joins through effective indexing, semi-joins, or aggregation indexes can mitigate these impacts.
You can find "Format Script" when you click on the three dots on the SQL Tab.
For users, it’s mainly about governance and logical isolation. Separate databases allow for different owners and permissions. Since custom schemas aren’t available yet, databases are the main way to group tables and views (this will change once schemas are supported).On the backend, metadata caching happens per database, so a single large database could add slight load. However, this is unlikely to have a practical impact unless in very large or complex cases.
You can add more users to your Firebolt account by either adding them through the web application under or with SQL commands. First create a login, using the email address of your invitee as the login_id. Next, associate the login to a user and assign them the appropriate permissions. Your invitee wiill automatically receive an email invitation to join your account. For more information visit our documentation.
Yes, primary indexes significantly impact query performance in Firebolt. Ensuring correct and optimized indexes is crucial, especially during migration. Indexes should be carefully reviewed and implemented based on query patterns and use cases.
Yes, Firebolt provides monitoring capabilities through its information schema and metadata. Users are encouraged to implement custom monitoring and alerting processes on their side, although Firebolt also monitors performance and proactively alerts users to critical issues.
Switching production workloads to Firebolt typically involves updating configuration to point to Firebolt endpoints. If all validation is complete and data is already present, this process is straightforward.
Firebolt recommends using aggregating indexes where possible for regularly queried granularities (e.g., daily or weekly), and employing pre-joined or pre-aggregated tables to simplify and speed up dashboard queries. Ensure indexes align closely with filter criteria to optimize query performance across various granularities.
Firebolt recommends an incremental ingestion approach using S3 as a staging area. Data from PostgreSQL can be segmented (e.g., by ID range or time interval), pushed to S3, and loaded into Firebolt using the Firebolt SDK. This method ensures manageable load times and easy scaling by controlling the volume of data incrementally loaded.
Yes. Firebolt recommends using separate engines for ingestion and query processing. Separating these concerns ensures ingestion tasks do not degrade query performance, leading to predictable and stable user experiences.
When a tenant comprises a large percentage of data (e.g., 20-25% of all data), avoid subqueries or joins that initially select large volumes of data and subsequently discard most rows. Instead, optimize queries and table structures to filter data as early and narrowly as possible, potentially using aggregated or pre-joined tables.
Primary indexes should include the most frequently used filters, such as tenant_id and date/time columns if queries consistently filter data by tenant and date ranges. A well-chosen primary index ensures queries access only relevant data partitions, maintaining fast performance even as data volumes scale significantly.
Aggregating indexes in Firebolt pre-compute aggregated values to significantly speed up aggregation queries. They perform best when aggregations occur on a single fact table. They are less effective or infeasible when aggregation queries require multiple table joins because an aggregating index must be built on a single table only.
Firebolt supports both using views and pre-joined tables. However, if most of the query execution time is spent on joins rather than aggregations, pre-joining tables (i.e., creating wider, denormalized tables during data ingestion) is often more performant. Views are effective for reusable SQL but may become slower with complex joins at scale. Aggregating indexes, which can pre-materialize aggregation results for fast query responses, work best on single tables without cross-table joins.
Setting up Apache Superset with Firebolt involves: - Installing Superset locally or on a server. - Configuring the Firebolt connector with appropriate credentials and connection parameters. - Testing queries in Superset to ensure Firebolt’s indexing structure is leveraged efficiently. - Optimizing queries for dashboard performance by using Firebolt’s indexing features to minimize latency. In this case, there were some challenges with reinstalling Superset, but Firebolt’s team is available to assist with setup and troubleshooting.
Yes, Firebolt can support a single table design that includes multiple reporting dimensions, such as unique counts, event times, and injected data. This consolidation can improve performance by reducing the need for complex joins and maintaining a single source of truth for analytics. However, when merging different data use cases into a single table, it is important to: - Optimize indexing to balance performance across different query patterns. - Consider partitioning or using aggregating indexes to precompute frequent aggregations. - Evaluate whether all reporting needs can be met within a single table without sacrificing efficiency.
One approach is to restructure the table by setting the primary index on event_time to better leverage Firebolt’s indexing capabilities. Additionally, an aggregating index on event_time can be beneficial. However, if queries still take longer than expected (e.g., 15 seconds for 30 days of data), it may help to review: - The structure of the primary index and ensure it aligns with the query’s filtering. - Whether unnecessary dimensions are included in the dataset, increasing granularity unnecessarily. - If joins or aggregations can be optimized, possibly through pre-aggregated tables. Firebolt’s architecture is designed to improve query efficiency by avoiding costly full scans and optimizing indexing structures.
Engine consumption data is available in the information_schema.engine_metering_history table, which provides hourly usage details at the account and engine level, including resource consumption and cost metrics. This data can also be retrieved via an API request.
Firebolt support engineers have the ability to access customer accounts for troubleshooting via Okta—only if they have the specific permissions. While it is generally recommended to keep support access open for fast incident resolution, you can request to block or limit their access if you have strict security requirements.
It is not clearly documented whether you can rename an existing organization URL. The typical workaround is to contact Firebolt support to see if they can rename it. If that is not feasible, you might need to recreate the organization under a new domain (e.g., using an email address at “velo”) and then migrate data or user setups.
Firebolt has “GenAI” initiatives on its product roadmap. While exact capabilities may evolve, the published information highlights plans for: AI-Assisted Querying (e.g., query recommendations, natural language querying), Auto-Tuning & Optimization powered by machine learning, and Improved Developer Experience leveraging AI-based insights. For a deeper discussion of upcoming features, Firebolt can arrange a roadmap review session with its product team.
Organization Level (Authentication): You manage logins (email addresses) at the organization/workspace level. Account Level (Authorization): Each account defines its own users, roles, and permissions. A single login can exist in multiple accounts with different roles. Support Access: Firebolt support engineers can access accounts via Okta (with appropriate permissions), but you can opt to block this access if desired (not recommended).
By subscribing through AWS Marketplace, you can consolidate Firebolt billing under your existing AWS billing arrangements. You will be directed to complete a few additional steps (“more clicks”) to finalize the purchase. Once completed, charges for your Firebolt usage appear in your AWS bill, simplifying vendor management if you prefer a single billing channel.
Before auto vacuum, you would typically schedule vacuum after a certain number of inserts or on a time-based schedule (e.g., nightly). Firebolt’s auto vacuum feature (released around early 2025) automatically triggers a non-blocking vacuum every few hundred transactions in the background, substantially reducing or eliminating the need for manual vacuum scheduling. This occurs with minimal overhead and typically does not require an engine size increase.
Separate Accounts: Each account cleanly isolates its data and can map to separate AWS buckets or IAM roles. Single Account with Multiple Databases: Environments share the same account, so you must carefully permission each database. Most teams that maintain separate AWS resources (e.g., dev vs. staging vs. production buckets and roles) find it more straightforward to mirror that approach with separate Firebolt accounts.
Concurrency & Overlapping Updates: If two CDC operations try to update the same row simultaneously, one transaction may fail. Implement a retry mechanism if you anticipate this scenario. Vacuum Operations: Frequent small inserts create multiple “tablets.” Vacuum consolidates and optimizes these for better query performance. Firebolt’s new “auto vacuum” (rolling out in early 2025) will greatly reduce the need for manual vacuum scheduling by automatically running a non-blocking vacuum in the background after a set number of transactions.
The “1.8 TB” figure refers to the SSD cache associated with a particular engine, not the total limit on data Firebolt can handle. Firebolt stores your full data in S3 for effectively unlimited capacity. Only the segments (tablets) relevant to a query are pulled into the SSD cache for faster processing. If your dataset exceeds 1.8 TB, Firebolt will still process it by cycling portions of data into and out of the SSD cache (first-in, first-out).
In most cases, no. The sub-result cache benefits queries that overlap in the underlying data scanned or join results. If the tenant ID changes and there is little or no data overlap, the previous sub-results become irrelevant, so the cache will not offer a speed-up.
Query performance primarily depends on the amount of data scanned. If queries remain selective (e.g., filtering by tenant ID and a truncated date range), Firebolt only scans relevant slices, keeping query times stable as data grows. Broad queries (e.g., SELECT * over wide date ranges) will naturally slow as more data must be scanned. Best practices include indexing on commonly used filters, leveraging caching, and avoiding unbounded queries to maintain good performance over time.
Order columns by frequency of use in queries. For example, if tenant_id and closed_at_day appear in most filters, list them first. Within equally common columns, order from lowest cardinality to highest cardinality (fewest unique values to most). This approach ensures that Firebolt’s indexing effectively prunes unnecessary data scans for highly repetitive or frequently queried columns.
Firebolt’s ingestion engine can be turned off when not actively loading data. Many teams schedule ingestion windows (e.g., hourly or daily) and then auto-stop the engine to save on costs. Billing is based on actual runtime, so you are not charged for idle ingestion clusters.
Firebolt can ingest data at terabytes-per-hour scale, supported by internal benchmarks (e.g., half a terabyte in ~800 seconds on four S-sized engines). Actual throughput depends on factors such as file format, table schema, partitioning, and engine size. Organizations can scale up (larger engines or more engines) to accelerate big batch loads and scale down for smaller, more frequent delta loads.
Firebolt offers multiple engine sizes (S, M, L, etc.). Smaller engines can handle many queries per minute if those queries are well-optimized. Heavier workloads or larger datasets may require a bigger engine or multiple concurrent engines. Firebolt charges by actual runtime (hourly or per second). Costs can be reduced by auto-stopping engines when not in use.
Queries with highly selective filters (e.g., smaller date ranges or high-selectivity columns) scan less data and often run in sub-second time. Queries that must scan large portions of the dataset (e.g., SELECT * over a broad date range) naturally take longer, especially on first (cold) runs when data must be read from storage. Firebolt’s sub-result caching reduces execution time for repeated or similar queries by caching portions of join results and aggregations. Proper indexing on commonly used filter columns can also significantly reduce the amount of data scanned, improving performance.
A viable workaround is to export query results to a file in S3, then read and process that file in smaller chunks. While it adds complexity (you must manage file paths, permissions, and cleanup), it avoids buffering the entire result set in application memory until real streaming is available in the Firebolt Node.js SDK.
Firebolt has introduced an auto-vacuum feature that runs on the write engine. It triggers after a set number of transactions and reclaims space without blocking ingestion. Manual vacuuming is largely unnecessary in most use cases now. A dedicated engine is not typically required; auto-vacuum operates seamlessly on the existing write engine.
Use derived date columns (e.g., day-level granularity) to lower cardinality. For instance, store closed_at_day by truncating a TIMESTAMP to DATE. Incorporate the derived column (e.g., closed_at_day) into the primary index alongside other frequently used filters (e.g., tenant_id), allowing Firebolt to skip irrelevant data segments. Because raw timestamp columns can be extremely granular, indexing them directly often leads to poor selectivity. Restructuring the schema to include day- or hour-level columns can significantly improve performance. Leverage Firebolt’s caching features (result cache, sub-result cache) for repeated queries.
Firebolt is designed for a “decoupled compute” architecture where you can spin up separate engines for different workloads. A dedicated write engine handles ingestion, while one or more read engines handle queries. This ensures that write operations do not slow down queries and vice versa. You can also configure auto-start/auto-stop so that engines only run (and incur costs) when needed.
Firebolt’s Customer Success and Support teams provide complementary query optimization guidance—including index design, join performance tuning, and ingestion configuration—at no extra cost. Users can reach out via Slack or support tickets for best practices and troubleshooting. In many cases, optimization is a collaborative, ongoing process. If you notice a slower query, you can share it with Support; sometimes the solution is a schema or indexing change, other times a product fix may be required.
Batch inserts are generally recommended for Firebolt. Inserting rows one at a time creates excessive overhead on the engine, leading to performance issues (especially on smaller engines). By sending records in small to moderate batches (e.g., once per second or at some reasonable time interval), the engine processes data more efficiently without overloading resources.
Firebolt advises creating a brand-new organization under the desired domain or AWS account via the standard sign-up process. Once the new organization is set up, copy over any needed configurations, tables, or data from the old organization. Because a new organization starts as a new trial, you also receive fresh Firebolt usage credits. After verifying that everything works correctly in the new organization, the old one can be retired or deleted.
Firebolt does not strictly require more tables; however, to achieve high-performance queries on large datasets, many teams choose to create specialized tables with carefully designed primary indexes. Although Firebolt can perform joins directly in SQL, the discussion emphasized that pre-joining or restructuring certain data tables often yields better performance. This approach leverages Firebolt’s indexing and reduces the run-time cost of large joins.
In the discussion, the Firebolt team recommended creating new, pre-joined (or otherwise streamlined) tables rather than performing large, multi-table joins at query time. This approach, sometimes called "join elimination," can significantly reduce query overhead. In addition, the Firebolt team highlighted the importance of setting appropriate primary indexes on these new tables to further optimize performance.
Firebolt provides documentation on connecting with Apache Superset. Their internal analytics team actively uses Superset, making it easier to provide support for any setup or troubleshooting questions.
Yes, Firebolt provides a query execution plan. Users can view a visual representation after query execution or generate a text-based plan using the EXPLAIN command. This helps users compare performance against other engines like Athena.
Aggregating indexes in Firebolt store precomputed aggregations for faster query performance. They update automatically upon new data ingestion, reducing query execution time significantly. The trade-off is a slightly increased ingestion time since the indexes must be maintained.
Users should avoid scanning large datasets unnecessarily by leveraging filtering on indexed columns. For example, filtering on both "event date" and "brand" significantly improves performance. Additionally, aggregating indexes can be used to precompute and store frequently used aggregations, reducing query execution time.
Firebolt does not natively save queries across different user accounts. Users need to manually copy queries and store them externally, such as in Slack, Google Docs, or a shared repository, to ensure accessibility across their team.
Firebolt users should define primary indexes based on frequently filtered columns, such as event dates and brand identifiers. By including relevant dimensions in the index, query performance can be significantly improved, as seen in the session where adding "brand" as a primary index reduced query execution time from minutes to seconds.
Users can verify the correct setup by ensuring they have obtained the API token, created the service account, and set up the appropriate automation steps. They can test ingestion by running queries to check if data has been successfully loaded into Firebolt.
Firebolt is built on advanced indexing, vectorized query execution, and efficient storage optimizations, ensuring sub-second query performance even on large datasets. While it is not a real-time platform, Firebolt is ideal for AI-driven analytics, interactive dashboards, and personalized AI applications that demand ultra-fast queries on your data.
Firebolt supports vector search but does not generate embeddings. Unlike dedicated vector databases, which specialize in unstructured data, Firebolt integrates vector search within a high-performance analytical data warehouse. This allows you to run hybrid queries (structured + unstructured) efficiently without managing separate systems.
If you already have embeddings generated from models like OpenAI, Hugging Face, or your own ML pipeline, Firebolt can store and query them at high speed and low latency, enabling AI-powered search and recommendations within your existing analytics environment.
Firebolt is optimized for low-latency, high-performance queries, but it is not a real-time processing platform. It excels in fast analytics on large-scale data but is not designed for event-driven streaming workloads. If your AI use case requires sub-second query execution, Firebolt is a great fit.
Firebolt is purpose-built for AI applications that require low-latency analytics. Unlike traditional cloud data warehouses, Firebolt delivers sub-second query performance for AI-driven workloads while supporting vector search and AI-driven optimizations. It enables faster and more efficient AI-powered analytics without the high costs and performance bottlenecks of legacy solutions.
Firebolt’s AI-related features, such as vector search, are included within our standard pricing model. While these capabilities do utilize compute resources, there are no separate licensing fees or AI-specific upcharges. You only pay for the compute and storage you use, ensuring cost efficiency without hidden AI-related costs.
For a detailed breakdown of how AI workloads impact pricing, reach out to our team for a tailored estimate based on your use case.
If it's a new account, you'll need to set up a new user within that account, although this user can be linked to the existing service account.
If it's a new organization, you'll need to establish both a new service account and a new user within that organization and any associated accounts.
Scaling with More Clusters:
This approach is ideal when you need to improve query concurrency—i.e., the ability to handle multiple queries simultaneously without significant performance degradation.
Scaling with a Higher Number of Nodes:
This is suitable when you find that the CPU utilization is consistently high, and queries are CPU-intensive. Adding more nodes spreads the workload across more computing units, thus alleviating CPU bottlenecks.
Scaling with Bigger Nodes:
This method is effective when the workload requires more memory or higher disk I/O capacity than what is currently available.
Set up SSO authentication: https://docs.firebolt.io/godocs/Guides/security/sso/sso.html
Configure your IdP: https://docs.firebolt.io/godocs/Guides/security/sso/configuring-idp-for-sso.html#custom.
You can query the INFORMATION_SCHEMA.ENGINES table to check the last known status of an engine:
SELECT engine_name, last_started, last_stopped
FROM INFORMATION_SCHEMA.ENGINES;
This will show when each engine was last started and stopped.
Firebolt values transparency and customer feedback when shaping its AI roadmap. To explore upcoming features or see open feature requests, reach out to Firebolt’s support or your customer success manager. Firebolt actively incorporates user feedback into its AI development efforts, with regular updates shared through newsletters, user forums, and product announcements. Stay connected to gain insights into innovations like query optimization for AI apps, text-to-SQL capabilities, and other enhancements for AI Apps tailored to your needs.
Queries are saved in information_schema.engine_query_history for as long as the engine remains active. However, the engine_query_history view is cleared upon an engine restart unless you have enabled the Persistent Query History feature.
FBU consumption is reported in real time and can be used to calculate costs by multiplying the consumed FBU by the price listed on the pricing page or a custom deal rate. However, the Billing and Consumption page updates daily, and AWS storage costs have a ~48-hour delay. For more information, check our documentation.
1. Use a WITH clause to define a Common Table Expression (CTE) and query its result.
2. Create a VIEW based on the result set and query the VIEW.
There is no limit to the number of regex expressions you can use with REGEXP_LIKE_ANY.
Use the source_file_name virtual column to filter rows based on the Parquet file name. For example:
SELECT $source_file_name, *
FROM external_table
WHERE $source_file_name ILIKE '%filename.parquet%';
This query retrieves rows where the source_file_name contains the specified file name. %filename.parquet% can be replaced with any pattern to match your file name.
For more information, see Using metadata virtual columns docs
Use ARRAY_SORT to sort one array and apply the same order to the other. For example, if you have array1 and array2:
array1: [4, 1, 3, 2]
array2: [Z, X, Y, R]
SELECT
ARRAY_SORT(x, y -> y, ARRAY_AGG(array2), ARRAY_AGG(array1)) AS sorted_array2
FROM your_table;
This ensures the order in array1 is applied to both arrays, maintaining their alignment.
Output:
sorted_array1: [1, 2, 3, 4]
sorted_array2: [X, R, Y, Z]
For more information, check our documentation.
The behavior of quote escaping is controlled by the setting standard_conforming_strings. When this setting is enabled (the default behavior), backslashes are treated literally, and strings are parsed without escaping. This ensures consistent handling of literal strings and avoids unexpected transformations. If standard_conforming_strings is disabled, backslashes can be used as escape characters, altering how strings are interpreted. For more information, check our documentation.
Yes, an AWS account is linked to an organization. However, it is not possible to link accounts within an organization to different AWS accounts, billing is on the Organization level. For more information, check our documentation.
When an engine is resized dynamically, queries in execution will continue under the engine's original configurations until completion or until a timeout of 24 hours, after which they will be dropped if still running. The changes in engine size or type will only affect new queries submitted post-resize. Please check our documentation for more information.
Yes, Firebolt allows for dynamic resizing of engines during operation. You can adjust the number of nodes or the node type without stopping the engine, which lets workloads continue with minimal disruption. Use the ALTER ENGINE command to resize an engine. Newly started clusters post-resize will initially perform slower until they are warmed up.
What is the system engine, and how is it used for metadata-related queries?The system engine in Firebolt is a lightweight, always-available engine specifically designed for metadata-related queries and administrative tasks. It supports various commands:
Access Control Commands: Manage roles, permissions, and users.
Metadata Commands: Execute queries on information schema views, such as information_schema.tables and information_schema.engines.
Non-Data Queries: Perform operations like SELECT CURRENT_TIMESTAMP()
that do not involve table data.
Typical Use Cases:
Retrieve information about databases, tables, indexes, and engines.
Manage system configurations or user permissions.
Execute DDL operations like creating tables and views, and managing all engine-related operations (start, stop, drop, alter).
Stopping an engine in Firebolt results in the eviction of the local cache. This leads to a "cold start" upon restarting the engine, as queries initially must fetch data directly from storage, slowing down performance until the cache is replenished with frequently accessed data. To minimize performance degradation, consider pre-warming the engine with essential queries or data after it is restarted. For more information please check our documentation article Work with Engines using DDL.
Firebolt values transparency and customer feedback when planning its roadmap. To view the current roadmap or see open feature requests, reach out to Firebolt’s support or your customer success manager. Additionally, Firebolt’s team actively gathers feedback from users and considers feature requests as part of ongoing development efforts. Regular updates are communicated through newsletters and user forums. Stay connected to get insights into upcoming releases and features tailored to your needs.
Firebolt stores unsaved scripts in your browser’s local storage, which has a limit of around 5 MB. If multiple websites use local storage, it can get full, causing unsaved scripts in the Firebolt SQL editor to be erased.
To avoid this:
Save your scripts regularly.
Clear your browser cache/cookies to free up local storage and prevent data loss.
Remember, clearing your cache will also remove other saved data, so use this solution carefully.
Yes, our insurance includes:
- Commercial General Liability
- Workers' Compensation and Employers' Liability
- Crime Insurance
- Professional & Technology Errors and Omissions
- Cyber Security Liability
Customer data is stored in S3 buckets with high availability and durability. Our recovery objectives are:
- RTO (Recovery Time Objective): 12 hours
- RPO (Recovery Point Objective): 1 hour
- SLA (Service Level Agreement): 99.9%
Researchers can report vulnerabilities by contacting security@firebolt.io.
Customers own their data and can delete it via commands like DROP DATABASE. Regardless, and upon contract termination, all customer data is deleted within 30 days.
Besides our runtime binary hardening, Firebolt leverages a runtime protection tool that provides deep visibility and protection at the process level.
Firebolt database itself inherently reduces the risk of SQL injection by minimizing the use of certain vulnerable constructs. Customers are still encouraged to implement additional controls at their application level such as:
- Ensure all user inputs are strictly validated before being processed.
- Escape potentially dangerous characters that could be used in unexpected ways.
- Include SQL injection tests in your regular security testing and code review processes.
Yes, we support encryption for data at rest and in motion. More on our technical best practices can be found in our Security blog: “Building Customer Trust: A CISO's Perspective on Security and Privacy at Firebolt”
Yes, customers can choose the region in which they run the service to meet data sovereignty requirements. More on our Available Region page.
Yes, this feature is supported. More details on our Identity Management page.
Yes, both IP Allow/Deny-listing is supported. More details on our Network Policy page.
Firebolt employs a comprehensive security strategy that includes network security policies, encryption practices, tenant isolation, and governance controls. We are committed to safeguarding your data through state-of-the-art security systems, policies, and practices.
We use AWS Shield, WAF, and other logical layers to protect against DDoS. Additionally, we leverage auto-scaling to maintain availability during attacks by dynamically adjusting resources like EC2 instances, ELBs, and other global services capacity. (Though some scenarios may require manual intervention).
We use tools like SCA, SAST for code analysis, along with practices such as Fuzzing, scanning for pipeline weaknesses (like the use of unverified external sources), and secret scans as part of our secure software development lifecycle.
Yes, customer access is managed via Auth0, while organizational access is controlled using Okta. All accesses are logged and monitored, and alerts are in place for any unauthorized configuration changes across our systems.
Yes, our policies, including Disaster Recovery (DR) and Business Continuity Plans (BCP), are tested regularly to ensure effectiveness.
For Data Subject Access Requests (DSARs) or any privacy-related inquiries, please reach out to us at privacy@firebolt.io
Firebolt processes customer data in compliance with both GDPR and CCPA regulations. We securely collect, store, and manage data according to the highest standards, ensuring that all GDPR and CCPA requirements are met.