Your AI companion for Databricks.

Make the analysis traceable before making it persuasive.

A chart or notebook cell can look conclusive while hiding an unclear population, stale run, undocumented transformation, or metric definition that changed elsewhere. The real work is connecting the output to the assumptions that make it meaningful.

Strawberry can read the Databricks and supporting browser context you select, then structure an investigation brief around the business question, observed result, source definition, and validation gap. It turns a pile of technical pages into something a reviewer can audit.

Give an incident the evidence it needs to move forward.

A failed job does not by itself explain whether the problem is input data, a dependency, an environment change, or an assumption inside the workflow. Fast recovery depends on a handoff that preserves the failure evidence and makes the next checks explicit.

Use Strawberry to combine the Databricks job context with the incident thread, repository, and service pages you open. It can prepare a technical investigation note that distinguishes visible failure signals from hypotheses and assigns the questions that need a specialist.

Keep metric questions attached to their definitions.

Teams often debate a number after it has been copied into a slide, far away from the dashboard, source model, or written definition that would settle the issue. The answer is not another summary. It is a chain of evidence that can be inspected by the people who own the metric.

Strawberry can organise the Databricks dashboard context and related documentation into a metric review. It can show the displayed value, its stated definition, conflicting sources, and the exact question that requires a data owner’s answer.

Make data-quality review a calm recurring practice.

Recurring analysis is valuable when it detects changing inputs, broken runs, and documentation drift early. It becomes risky when it quietly runs production work, reconfigures pipelines, or turns an anomaly into an automatic business decision.

Every Friday, have Strawberry collect the named Databricks job pages, dashboards, definitions, and documentation into a report-only quality packet that flags changing inputs and failed runs. Keep computation, deployment, permissions, and remediation under the platform’s existing controls: an unchecked write can rerun expensive workloads, expose data, or alter a production pipeline.

Frequently asked questions