Keboola holds the moving parts behind a data product: components, runs, transformations, destinations, and the evidence around a failure. Strawberry can read the workspace you have open alongside logs and downstream reports, then prepare an investigation that keeps observed facts separate from a proposed change.
01
Locate the failure before proposing a repair.
A failed report can originate in a source extract, transformation, orchestration step, or destination. Strawberry can collect the visible Keboola run details and surrounding evidence into a chronological investigation rather than jumping directly to a fix.
02
Explain the downstream effect in business terms.
Data teams need to know whether a pipeline issue affects a staging table or a board metric people will use today. Strawberry can compare the relevant outputs and prepare a plain-language impact note before the incident spreads through Slack.
03
Make pipeline changes reviewable before they run.
A transformation adjustment can resolve one anomaly while changing a metric definition or downstream schema. Strawberry can prepare the affected inputs, outputs, validation checks, and rollback question for a change review.
04
Prepare run health before reporting depends on it.
A morning Keboola check should focus on the workflows that feed today’s decision-making, not every historical run. Save those critical paths and failure signals as a skill, then prepare the review before analysts begin publishing numbers.
Keboola Strawberry