What Data Quality & Observability capabilities does VividLogic offer?

Our Data Quality & Observability work covers Data Quality Testing, Observability Dashboards, Alerting, and Incident Response.

Data Quality & Observability

We build data quality and observability that makes your data trustworthy. From testing to monitoring to alerting, our data engineering ensures data breaks loudly, not silently.

Our Approach

We implement data quality tests at the point of ingestion and transformation. Our observability includes dashboards, alerts, and runbooks that keep your data trusted.

Capabilities

Data Quality Testing

dbt tests, Great Expectations, or custom quality rules.

Observability Dashboards

Monitoring dashboards for pipeline health.

Alerting

Proactive alerts when data issues occur.

Incident Response

Runbooks and processes for data incidents.

How It Comes Together

A typical data platform architecture

Ingest

Source systems and events land reliably in a raw layer.

Transform

dbt models turn raw data into trusted, tested metrics.

Warehouse

Governed storage optimized for your query patterns.

Analyze

Dashboards and reports surface insight for decision-makers.

Activate

Reverse ETL syncs insight back into the tools your team uses.

In Practice

Data pipelines that fail silently are more dangerous than pipelines that fail loudly, because a loud failure gets fixed and a silent one erodes trust in every downstream report for weeks before anyone notices. We implement quality checks at the point data enters your pipeline and again at each major transformation, not just as a final validation step, so problems get caught close to their source where they're cheapest to diagnose and fix. Tests are written for the failure modes that actually matter to your business, a schema change from a source system, a sudden drop in row count, a metric drifting outside historical bounds, rather than generic checks that generate noise without catching real issues. Every observability implementation includes alerting routed to the team that can actually act on it, with enough context in the alert to start debugging immediately, and a runbook for the failure modes we've identified during implementation. The goal is a system where data quality issues get caught and fixed before a stakeholder ever notices something looks wrong in a report.