Find the analytics bottlenecks before users abandon the stack
Performance Diagnostics
We diagnose slow dashboards, bloated queries, brittle transformations, and warehouse inefficiencies so your reporting layer stops feeling expensive and sluggish.
Best fit
For teams with dashboards that time out, warehouses that keep getting more expensive, or users who stopped trusting the experience because it is too slow.
Find the warehouse, model, and dashboard bottlenecks making analytics feel slow and fragile.
You likely need this when
You likely need this when
Dashboards load slowly or fail during high-traffic periods.
Warehouse costs keep rising without a clear explanation.
Pipelines finish late and downstream teams start the day behind schedule.
Nobody can tell whether the real issue is SQL, modeling, infrastructure, or tool configuration.
Analytics performance is a trust problem
Once reporting becomes slow or unstable, users stop exploring, adoption drops, and every new request feels like it might break the system again.
Where teams usually get stuck
Queries scan too much data or fight the warehouse in inefficient ways.
Transformation layers have grown without clear performance discipline.
Dashboards pull more data than the decision actually needs.
There is little monitoring around freshness, latency, or cost spikes.
How AUXO fixes the problem
Measure warehouse, model, query, and dashboard performance against real usage paths.
Identify the top cost and latency drivers instead of tuning blindly.
Refactor high-impact queries, models, or aggregates for faster response.
Add performance guardrails so the same issue does not quietly return next month.
Where teams usually get stuck
Where teams usually get stuck
Queries scan too much data or fight the warehouse in inefficient ways.
Transformation layers have grown without clear performance discipline.
Dashboards pull more data than the decision actually needs.
There is little monitoring around freshness, latency, or cost spikes.
How AUXO fixes the problem
How AUXO fixes the problem
Measure warehouse, model, query, and dashboard performance against real usage paths.
Identify the top cost and latency drivers instead of tuning blindly.
Refactor high-impact queries, models, or aggregates for faster response.
Add performance guardrails so the same issue does not quietly return next month.
What the diagnostic produces
The work is built to separate symptom from cause so the team fixes the right layer first.
Performance Baseline
Measured benchmarks for dashboard latency, query behavior, transformation runtime, freshness, and cost drivers.
Root-Cause Map
A ranked view of the specific models, queries, joins, dashboard patterns, or configuration issues creating the drag.
Optimization Backlog
A prioritized remediation plan that balances user pain, engineering effort, and expected impact.
Monitoring Guardrails
Recommended checks and alerts for performance regressions, freshness failures, and unnecessary compute burn.
How the diagnostic runs
A focused engagement designed to identify the high-impact problems first instead of tuning random things and hoping.
Baseline current performance
We inspect usage patterns, dashboard load paths, warehouse behavior, and data pipeline timings to establish a measurable starting point.
Deliverables
Trace bottlenecks to root cause
We isolate whether the real issue sits in SQL patterns, data models, orchestration, dashboards, or infrastructure configuration.
Deliverables
Design the fix plan
We build an optimization backlog that sequences quick wins and deeper structural changes with expected tradeoffs.
Deliverables
Validate and harden
We test recommended improvements and define the monitoring rules needed to keep the experience stable after the cleanup.
Deliverables
What changes after the cleanup
The immediate goal is faster systems. The larger goal is restoring user confidence in the analytics experience.
Shorter load times
Dashboards and recurring reports feel responsive enough to use during live decision-making.
More stable delivery
Pipelines and refresh jobs stop introducing preventable delays into reporting cycles.
Lower performance waste
The team gets clearer visibility into where spend and scan volume are being burned for little value.
Better operating discipline
Monitoring and performance ownership improve so regressions are caught before users feel them.
Improvement magnitude depends on platform limits, modeling quality, and whether deeper architectural issues are in scope for remediation.
Questions before you diagnose the stack
The main issue is usually not whether the system is slow. It is whether the team can prove where the drag actually starts.
Can you work with our current warehouse and BI setup?
Yes. The diagnostic is designed to work with the system you already have. If the architecture itself is the issue, the findings will make that explicit.
Do you only give recommendations, or can you help validate them?
We validate the major findings and can support remediation planning. Otherwise you are paying for a fancy list of suspicions.
What if the issue is poor modeling rather than infrastructure?
Then that is what the diagnostic should reveal. Tuning hardware around bad models is just paying interest on a design mistake.
Is this only for very large data teams?
No. Smaller teams often need it more because they feel the drag sooner and have less margin for warehouse waste or manual firefighting.
Stop tolerating slow analytics as normal
If the stack is sluggish, expensive, or fragile, we can isolate the bottlenecks and show where the fix actually belongs.
Ready to discuss your specific needs? Our team typically responds within 24 hours.