Common Issues

Solutions to the most frequently encountered issues with LakeSentry. For collector-specific problems, see Collector Issues. For data pipeline questions, see Data Freshness & Pipeline Status.

Quick reference

Symptom	Likely cause	Jump to
Can’t log in or access the app	Authentication or invitation issue	Login and access problems
Dashboard shows no data	Connector not set up or collector not running	Missing data
Costs don’t match Databricks console	Different time ranges, cost model, or attribution scope	Cost discrepancies
Metrics appear stale or outdated	Pipeline lag or collector schedule issue	Stale metrics
Some workspaces or resources missing	Region connector or permission gap	Incomplete coverage
Insights not appearing	Detection thresholds or insufficient history	Missing insights

Login and access problems

”Invitation expired” or no access

If you can’t log in or see a “no access” message:

1. Check your invitation — Invitations expire after 7 days. Ask your organization admin to resend the invitation from Settings > Access.
2. Verify the email address — You must log in with the exact email address the invitation was sent to. Check for typos or alias differences.
3. Check your role — If you can log in but can’t access certain features, your role may not have the required permissions. See Roles & Permissions for the permission matrix.

SSO or OAuth login failures

If authentication redirects fail or loop:

• Clear browser cookies for app.lakesentry.io and try again.
• Try an incognito/private window to rule out cached credentials.
• Check your identity provider — If your organization uses SSO, verify your account is active in the IdP.

Missing data

No data at all after setup

If the dashboard is completely empty after connecting your Databricks account:

1. Check connector status — Go to Settings > Connector and verify the connector shows “Synced” status.
2. Check region connectors — Each region needs its own connector. If no region connectors are set up, there’s no data pipeline. See Region Connectors.
3. Check collector status — The collector must have run at least once. Look at the region connector detail page for “Last ingestion” time. If it shows “Never,” the collector hasn’t pushed data yet. See Collector Deployment.
4. Wait for pipeline processing — After the first collector run, data takes 15–30 minutes to flow through the processing pipeline before appearing in dashboards.

Tip

The most common reason for missing data after initial setup is that the collector job hasn’t been scheduled yet. Follow the Quick Start Guide to ensure all setup steps are completed.

Some data types missing

If dashboards show cost data but certain features or resource types are empty:

Missing data	Likely cause	Fix
SQL warehouse data	Warehouse system tables not accessible	Grant SELECT on system.compute.warehouse_events to the service principal
Job/pipeline data	Missing system.lakeflow.* permissions	Grant access per Account & Connector Setup
MLflow data	Optional tables not enabled	Grant access to MLflow system tables per optional tables
Model serving data	Optional tables not enabled	Grant access to serving system tables
Storage data	Optional tables not enabled	Grant access to storage system tables

Check the region connector’s “Tables received” list to see which tables the collector is successfully extracting.

Cost discrepancies

LakeSentry costs differ from Databricks console

Cost differences between LakeSentry and the Databricks billing console are common and usually explainable:

Time range alignment

• LakeSentry and Databricks may use different time zone conventions. LakeSentry uses UTC for all cost calculations. Verify both tools are set to the same date range.
• Databricks billing data has a 1–4 hour delay (see Data Freshness). If you’re comparing “today’s” costs, the most recent hours may not be included yet.

Cost model differences

• By default, LakeSentry uses Databricks list prices for DBU costs. If your organization has negotiated pricing, the numbers will differ unless you’ve configured DBU price overrides.
• LakeSentry separates DBU costs (compute charges) from cloud infrastructure costs (VM, storage, networking). The Databricks console may show them combined.

Scope differences

• LakeSentry shows costs only for workspaces and regions with active connectors. If some workspaces aren’t connected, their costs won’t appear.
• Attribution groupings (by team, project, or tag) may slice costs differently than the Databricks console’s per-workspace view.

Note

Small rounding differences (less than 1%) are expected due to floating-point arithmetic in aggregations. Differences larger than 5% usually indicate a time range mismatch or missing workspaces.

Attribution shows “unattributed” costs

If a significant portion of costs appear as “Unattributed”:

• New resources — Resources without tag or ownership data default to unattributed. Configure Attribution Rules to assign them.
• Shared compute — Interactive clusters shared across teams may lack clear ownership signals. Use tag-based or pattern-based attribution rules.
• System overhead — Some Databricks costs (like platform fees or photon charges) don’t map to specific user workloads.

See Cost Attribution & Confidence Tiers for how attribution works and how to improve coverage.

Stale metrics

Dashboard data appears outdated

If metrics haven’t updated recently:

1. Check pipeline status — Go to Settings > Connector and look at the “Last ingestion” timestamp for each region. If it’s more than 2 hours old, there may be a collector or pipeline issue.
2. Check collector health — Look at the connector status indicator:
   • Green (Synced) — Collector running normally, data has been received
   • Red (Error) — Connector is in an error state or validation failure
   • Gray (Awaiting data) — Connector is set up but has not received data yet
3. Check for Databricks-side delays — Some system tables have inherent latency. See Data Freshness for expected lag by data type.

Metrics show zeros or flat lines

If metrics suddenly drop to zero:

• Collector stopped running — The Databricks job may have been disabled, deleted, or failed. Check the job status in Databricks.
• Permissions revoked — The service principal may have lost access. Re-grant permissions per Account & Connector Setup.
• Region connector disconnected — Check connector status. If it shows “Error,” investigate the error message.

Incomplete coverage

Some workspaces are missing

If certain Databricks workspaces don’t appear in LakeSentry:

• Check region mapping — Workspaces in regions without a region connector aren’t monitored. Add a region connector for each active region. See Region Connectors.
• Check workspace access — The service principal must have workspace-level access in each workspace it should monitor. Account-level access alone isn’t sufficient for all system tables.

Historical data is missing

LakeSentry only shows data from when the collector started running. It cannot backfill data from before the first collection.

• Billing data — Databricks system tables retain billing history. LakeSentry can extract historical billing data on the first run, covering up to the connector’s lookback setting (default 180 days, subject to plan limits).
• Compute and workload data — These tables have shorter retention. The collector captures what’s available at first run and tracks incrementally after that.

Tip

Connect LakeSentry as early as possible to maximize historical data capture. The longer you wait, the less historical compute and workload data will be available.

Missing insights

No anomalies or waste findings

If the Insights page is empty despite having cost data:

• Insufficient history — Anomaly detection requires at least 5 runs to establish a baseline. New connections won’t generate anomalies until enough history accumulates. See Anomaly Detection.
• Below minimum thresholds — Anomaly detection ignores work units with baseline costs under $10 and cost deltas under $50. Low-cost environments may not trigger any anomalies.
• No waste conditions detected — Waste detection looks for specific patterns (idle clusters, overprovisioned resources). If your environment is well-optimized, there may genuinely be nothing to flag.
• Auto-dismiss rules — Check if auto-dismiss rules are filtering out insights. Auto-dismiss rules are configured at the system level and can filter insights based on criteria like savings thresholds, age, or severity.

Insights aren’t updating

• Insights are regenerated on each detection cycle, which runs after new data is processed. If the data pipeline is delayed, insight generation is also delayed.
• Check the Data Freshness page for pipeline status.

Getting help

If you’ve worked through the relevant sections above and the issue persists:

1. Note the specific symptom and any error messages you see.
2. Check Settings > Connector for connector status and last ingestion times.
3. Review the FAQ for additional answers.
4. Contact LakeSentry support with these details.

Next steps

• Collector Issues — Troubleshooting the data collector specifically
• Data Freshness & Pipeline Status — Understanding data latency and pipeline health
• FAQ — Answers to frequently asked questions