Walt

Challenge

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As a data engineer, you’re responsible for building reliable pipelines, managing schema changes, and keeping metric definitions intact — all while supporting fast-moving teams.

A renamed column breaks multiple dashboards
Downstream teams report conflicting numbers
You spend cycles debugging queries instead of building
Semantic layers (LookML, dbt) require constant upkeep
Metric logic becomes fragile as the business grows

You need a system that doesn’t just process data — it understands it.

How Walt Helps

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Walt introduces an agentic layer that actively monitors, interprets, and adapts to changes in your data stack. It serves as a buffer between your raw infrastructure and downstream users, reducing manual firefighting and enforcing consistency at scale.

What Walt’s Agentic System Does for Data Engineers

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Capability	How It Helps
Schema Awareness & Change Detection	Automatically monitors schema drift, table renames, and column changes — alerting you to downstream impact instantly.
Metric Logic Alignment	Keeps metrics consistent with evolving models and business logic — preventing mismatches across teams or reports.
SQL Generation & Optimization	Autonomously generates performant, validated SQL based on contextual data — reducing manual query effort and debugging.
Semantic Layer Reinforcement	Infers and strengthens table relationships and joins to continuously update the semantic model without heavy modeling overhead.
Regression Monitoring	Flags unexpected deviations in downstream logic and output — before dashboards or stakeholders are affected.

Sample Data Engineer Workflow

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Scenario:

The product team updates the user_events table, renaming event_type to event_action and splitting it into two columns. No one updated the LookML layer.

With Walt:

Walt detects the schema change via its connector and version awareness
It identifies affected metrics and reports using the old field
Regression monitoring kicks in and flags recent anomalies in output
Walt proposes updated logic and waits for your approval before rollout
You approve, the system auto-patches the SQL, and downstream reports stay intact

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• No broken dashboards

• Clear visibility into impact

• Time saved debugging

A Smarter, Safer Data Stack — Powered by Agents