Design and Manage Firebolt Databases with DbSchema

Build a clearer workflow for Firebolt: reverse engineer existing schemas into interactive ER diagrams, model changes visually, and generate reviewed SQL scripts before deployment.

DbSchema is built for visual modeling, schema documentation, and deployment. Keep an offline model in Git, collaborate across teams, and publish documentation that developers, analysts, and stakeholders can navigate in minutes.

Download DbSchema See Firebolt Features Download Firebolt JDBC Driver

Engine and Database Architecture

Firebolt is a cloud OLAP database purpose-built for sub-second analytics on large datasets, running on AWS and GCP. A key architectural differentiator is the separation of compute engines from databases: each Firebolt database can be associated with one or more named engines that are independently started, stopped, and sized, allowing teams to run isolated workloads (for example, ingestion vs. interactive queries) on dedicated compute without contention. Firebolt stores data in a proprietary compressed columnar format and builds sparse indexes, aggregating indexes, and join indexes automatically or on demand to accelerate common query patterns. DbSchema connects via the Firebolt JDBC driver and introspects table schemas and index definitions, rendering them as schema diagrams that show column types and index configurations together.

Download DbSchema Free See Firebolt Features

Writing Firebolt Analytical SQL in the SQL Editor

Firebolt's SQL dialect extends ANSI SQL with functions for approximate aggregations, array operations, and semi-structured data manipulation. DbSchema's SQL editor connects over the Firebolt JDBC driver and provides table and column name auto-completion, syntax highlighting, and a results grid that handles the wide, flat result sets typical of OLAP queries. You can write window functions, lateral joins, and Firebolt-specific array unnesting expressions, then save the queries to a shared library for reuse across the team. The editor also shows query execution time, allowing you to measure the impact of adding or modifying aggregating indexes on query performance.

Exploring Large Analytical Datasets

The DbSchema data explorer lets you sample and browse Firebolt table contents without writing SQL, applying column filters and sort orders through a point-and-click interface. Because Firebolt's engines return results at sub-second latency even for large tables, the data explorer pages through results rapidly. This makes it practical to spot-check newly loaded data, verify that a dimension table join key is populated correctly, or inspect the output of a materialized aggregating index before referencing it in a production dashboard. You can export sampled rows to CSV directly from the explorer for use in external validation scripts.

Connection Setup and JDBC URL

To connect DbSchema to Firebolt, download the Firebolt JDBC driver JAR (com.firebolt.FireboltDriver) from the Firebolt documentation and register it in DbSchema's driver manager. The JDBC URL format is jdbc:firebolt://api.app.firebolt.io/mydb, where mydb is your Firebolt database name. Firebolt uses HTTPS on port 443 for all JDBC traffic. Provide your Firebolt service account credentials (client ID and client secret) or user email and password in the connection dialog. If you have multiple engines associated with a database, you can specify the engine name as a JDBC URL parameter (?engine=my_engine) to direct queries to a particular compute engine. For GCP-hosted databases, the URL host changes to the GCP endpoint.

Why Teams Use DbSchema with Firebolt

• Visualize Firebolt table schemas alongside their sparse, aggregating, and join index configurations in a single diagram to plan query optimization strategies.
• Write and benchmark analytical SQL in DbSchema's SQL editor, measuring the performance gain of each new index type without switching tools.
• Explore large dataset samples in the data explorer at sub-second speed to validate data loads and dimension table integrity before connecting BI tools.
• Generate schema documentation for Firebolt databases to maintain an up-to-date data catalog for analytics teams.
• Manage connections to multiple Firebolt engines (ingestion, interactive analytics) from a single DbSchema project.
• Use DbSchema's offline model to design new Firebolt table schemas with index annotations before creating them on the cluster.