BrinqaDL Data Lake
What BrinqaDL is
BrinqaDL is Brinqa's analytics data lake: a BigQuery pipeline that extracts your Brinqa data, models it into a star schema, and publishes ready-to-query views for BI tools and ad hoc SQL. It's built for BI analysts and data engineers who need to build dashboards, run large-scale historical analysis, or join Brinqa data with other systems, without querying the Brinqa Platform directly.
BrinqaDL's data lake is a separate product from the Brinqa Platform's Data Warehouse feature. Data Warehouse is the OLAP store that accelerates list views and dashboards inside the Brinqa Platform. BrinqaDL is a downstream BigQuery pipeline for external analytics and BI tooling.
How the data is organized
BrinqaDL data flows through three layers in BigQuery:
| Layer | Dataset | What it contains | You query it? |
|---|---|---|---|
| Raw UDM | silver_udm | Source system extracts, one table per unified data model | No (internal) |
| Star schema | gold_adm | Facts, dimensions, bridges, and aggregates | No (internal) |
| Consumption views | gold_adm_views | Pre-joined, ready-to-query views (v_*) | Yes |
As a BI analyst or data engineer, you only need the consumption views in gold_adm_views. Views such as v_findings, v_assets, and v_tickets pre-join the star schema, so you don't need to know the underlying fact, dimension, and bridge table structure to answer common questions.
Finding prioritization is not part of the data lake. Brinqa provides it natively through AI Agents. The data lake gives you the underlying evidence to analyze, such as severity, risk score, CVE and threat-intel columns on v_findings and v_threat_intel.
Where to start
- Confirm you have BigQuery access to the
gold_adm_viewsdataset for your Brinqa project. - Read Core concepts for the star schema layout and the terms used throughout these docs.
- Start with the core consumption views:
v_findingsfor security findings,v_assetsfor inventory, andv_ticketsfor remediation tickets. The view reference documents every view's columns. - Connect your BI tool of choice (Looker Studio, Tableau, Power BI, or a plain SQL client) directly to the dataset using standard BigQuery connectors. See Connecting BI tools for setup steps and access requirements.
- For ready-to-adapt SQL once you're connected, the query cookbook covers common patterns like case-insensitive filtering, array columns, and time-travel queries.