Databricks FinOps Cost Accelerator
Multi-workspace Databricks cost visibility from system tables: SKU/job/user attribution, monthly compute-type trends, and reserved-instance utilization. Ships a dependency-free cost-analysis library + offline tests. Works on any Databricks workspace.
⚙ Try the Live Demo interactive
Enter your Databricks compute mix and see the cost breakdown, the all-purpose overspend signal, and estimated savings — computed by the shipped cost-analysis logic.
⚡ Open Cost Explorer📋 What's Inside 18 files
- ✓ README.md
- ✓ LICENSE
- ✓ manifest.json
- ✓ databricks.yml
- ✓ resources/jobs.yml
- ✓ src/01_daily_usage_by_sku_workspace.sql
- ✓ src/02_daily_job_costs_by_workspace.sql
- ✓ src/03_monthly_aggregates.sql
- ✓ src/04_monthly_usage_by_user.sql
- ✓ src/05_reserved_instance_utilization.sql
- ✓ lib/cost_analysis.py
- ✓ tests/test_cost_analysis.py
- ✓ conftest.py
- ✓ guide/01_what-you-get.md
- ✓ guide/02_getting-started.md
- ✓ guide/03_architecture.md
- ✓ guide/04_support.md
- ✓ guides/finops-methodology.md
📁 File Structure 18 files
├── README.md
├── LICENSE
├── manifest.json
├── databricks.yml
├── resources/
│ ├── jobs.yml
├── src/
│ ├── 01_daily_usage_by_sku_workspace.sql
│ ├── 02_daily_job_costs_by_workspace.sql
│ ├── 03_monthly_aggregates.sql
│ ├── 04_monthly_usage_by_user.sql
│ ├── 05_reserved_instance_utilization.sql
├── lib/
│ ├── cost_analysis.py
├── tests/
│ ├── test_cost_analysis.py
├── conftest.py
├── guide/
│ ├── 01_what-you-get.md
│ ├── 02_getting-started.md
│ ├── 03_architecture.md
│ ├── 04_support.md
├── guides/
│ ├── finops-methodology.md
📖 Documentation Preview README excerpt
Databricks FinOps Cost Accelerator
Multi-workspace Databricks **cost visibility built entirely on system tables** —
SKU/job/user attribution, monthly compute-type trends, and reserved-instance
utilization. Works on **any** Databricks workspace with zero organization-specific
dependencies: everything is driven by `system.billing.usage`,
`system.billing.list_prices`, and `system.lakeflow.jobs`.
Why it exists
Databricks bills at the DBU/SKU level, but the questions leadership asks are
"which team, which job, which workspace, and where can we cut 20%?" This
accelerator answers those in a single `databricks bundle deploy` — no custom
cost pipeline to build.
What's inside
- **A Databricks Asset Bundle** — one daily job, five parameterized SQL tasks.
- **Aggregate tables**: daily usage by SKU/workspace, daily job costs (with job
names resolved), monthly compute-type breakdown, per-principal attribution,
and reserved-instance utilization.
- **A dependency-free analysis library** (`lib/cost_analysis.py`) that mirrors
the SQL rollups in pure Python — SKU classification, cost rollups, top jobs,
RI utilization, and an all-purpose-compute right-sizing signal.
- **A unit-test suite** (`tests/`, 9 passing tests) that runs offline.
Quickstart (30 minutes)
```bash
Verify the analysis logic offline first (no Databricks needed):
pip install pytest
pytest tests/ -v
Then deploy:
databricks bundle validate -t dev -p <your-profile>
databricks bundle deploy -t dev -p <your-profile>
```
Set `catalog_name`, `schema_name`, and `warehouse_id` in `databricks.yml`.
Ensure the deploying principal can read `system.billing.*`.
The right-sizing signal
The classic Databricks overspend pattern is interactive **all-purpose** compute
running scheduled work that belongs on cheaper **Jobs**/Serverless compute.
`rightsizing_signal()` (and the RI utilization table) surface exactly that, with
... preview truncated, see full README in product download.