Work Units (Jobs & Pipelines)
Work Units is LakeSentry’s unified view of Databricks jobs and Delta Live Tables (DLT) pipelines — the scheduled and triggered workloads that drive most of your compute spend. Each work unit tracks a single job or pipeline across all of its runs, giving you a cost history and performance baseline.
Work unit list
Section titled “Work unit list”The main page shows all jobs and pipelines across your connected workspaces:
| Column | What it shows |
|---|---|
| Name | Job or pipeline name (with creator shown underneath) |
| Type | Job or pipeline |
| Significance | Category badge (Top Spender, Growing, or Long Running) |
| Trend | Cost sparkline for the selected time range |
| Cost | Total cost for the selected time range |
| Cost/Run | Average cost per run |
| Runs | Total number of runs (all time) |
| Avg Duration | Mean execution time |
| Success | Success rate as a percentage |
| Last Run | When the work unit last executed |
Sorting and filtering
Section titled “Sorting and filtering”Sort by name, cost, significance score, or last run time. Common sorting strategies:
- Cost descending — Find the most expensive work units
- Significance descending — Focus on what matters most
- Last run descending — See the most recently active work units
Filters available:
| Filter | Options |
|---|---|
| Workspace | Specific workspace |
| Type | Jobs, Pipelines, or All Types |
| Search | Filter by name or external ID |
Category badges
Section titled “Category badges”Every work unit receives a significance score based on cost impact, execution frequency, and historical reliability. Badges help you prioritize:
| Badge | Condition | Meaning |
|---|---|---|
| Top Spender | Significance score >= 45 | High cost impact — always worth monitoring |
| Growing | Cost change > 15% | Cost is trending upward compared to the prior period |
| Long Running | Avg. duration > 30 min | Execution time is notably long |
Work unit detail view
Section titled “Work unit detail view”Click any work unit to see its dedicated detail page.
Cost trend
Section titled “Cost trend”A bar chart showing daily cost over time. Each bar represents one day’s total cost for the work unit. The chart includes:
- Trend area — A shaded area overlay showing the cost direction over the period
- Average line — The mean daily cost across the visible range
This visualization makes cost spikes immediately visible. A day that stands well above the average is likely worth investigating.
Run history table
Section titled “Run history table”A detailed table of all runs in the selected time range:
| Column | What it shows |
|---|---|
| Run ID | Databricks run identifier |
| Compute | Which cluster or warehouse executed the run |
| Status | Run result state (e.g. SUCCEEDED, FAILED, ERROR, CANCELLED, TIMEDOUT) |
| Start time | When the run started |
| Duration | Wall-clock execution time |
| Cost | Total cost for this run (DBU + cloud) |
Performance metrics
Section titled “Performance metrics”Aggregate metrics for the work unit over the selected period:
| Metric | What it measures |
|---|---|
| Total cost | Sum of all run costs (with DBU/cloud breakdown) |
| Total runs | Number of runs, with recent runs in the last 7 days |
| Avg. duration | Mean execution time |
| Success rate | Percentage of runs that completed successfully |
Cost breakdown by SKU
Section titled “Cost breakdown by SKU”The detail page includes a cost breakdown by Databricks SKU (billing category), showing how the work unit’s cost distributes across different SKUs such as Jobs Compute, Jobs Light, or All-Purpose Compute. Each SKU shows its cost and proportion of the total.
Details panel
Section titled “Details panel”The detail page includes a panel with key metadata:
- External ID — The Databricks job or pipeline ID (with a link to open in Databricks)
- Workspace — Which workspace the work unit belongs to
- First Seen — When the work unit was first observed
- Last Run — When the work unit last executed
- Significance — The significance score (if available)
Data dependencies
Section titled “Data dependencies”Shows upstream and downstream table dependencies for the work unit, helping you understand data lineage. Upstream tables are those read by the work unit, and downstream tables are those written to, along with their consumers.
Jobs vs. DLT pipelines
Section titled “Jobs vs. DLT pipelines”While both appear as work units, there are some differences in how they are tracked:
| Aspect | Jobs | DLT pipelines |
|---|---|---|
| Run granularity | Per job run | Per pipeline update |
| Cost source | Per-run billing via job_run_id linkage | Daily cluster usage aggregates |
| Cluster info | Job cluster or existing cluster | Pipeline cluster (auto-provisioned) |
Next steps
Section titled “Next steps”- Compute — View the clusters that execute your work units
- SQL Analysis — Query-level cost analysis
- Attribution Rules — Configure how work unit costs are assigned to teams
- Anomaly Detection — How cost spikes are detected
- Insights & Actions — Acting on work unit optimization opportunities