Data Quality or Data Chaos? Catch Issues Early With Automated Scanning

Don’t let bad data creep into your dashboards. This post breaks down how we use tools like Soda and dbt to catch data issues before they cause downstream damage—giving your team confidence in every report, model, and decision.

As your business grows, so does your data—and with that comes a new set of challenges. It’s not just about having data anymore. It’s about trusting it.

Poor data quality can wreak havoc downstream: broken dashboards, incorrect reports, failed integrations, or worse—decisions made on the wrong information.

That’s why automated data quality scanning is no longer a nice-to-have. It’s a necessity.

The Problem: Garbage In, Garbage (and Confusion) Out

You’ve built the pipelines, connected the sources, and set up your reports—but suddenly your numbers aren’t adding up.

• A date field is coming through as a string.
• A NULL value breaks your customer segmentation model.
• New records aren’t being picked up because someone renamed a column in the source system.

Sound familiar?

Manually chasing down these issues doesn’t scale. And if you don’t catch them early, they quietly pollute your analytics until trust is gone and no one uses the reports

The Solution: Combine Soda and dbt for End-to-End Data Quality

At Emerald Codeworks, we approach data quality in layers:

• Soda is our tool of choice for scanning historic or raw data—the kind that lives in legacy systems or staging areas before it ever hits your warehouse
• Once the data enters the warehouse and goes through dbt models, we rely on dbt’s built-in testing framework to enforce schema contracts, detect nulls, catch duplicates, and validate assumptions in real-time.

This two-tiered approach means we’re checking data before and after transformation, not just hoping for the best once it reaches production.

How It Works in Practice

1. Scan Historic Data with Soda

We run Soda checks on raw or imported data—especially when onboarding data from new clients or legacy databases. These scans help identify:

• Unexpected nulls in important columns
• Outliers in numeric ranges
• Schema mismatches across systems

2. Validate Data in the Warehouse with dbt Tests

As data flows through dbt models, we layer on tests like

• not_null and unique constraints
• Relationship tests between dimension and fact tables
• Custom assertions using SQL logic (e.g., no orders older than the customer’s signup date)

3. Automate and Monitor

How Emerald Codeworks Can Help

• dbt tests run with every model build
• Failures raise alerts to Slack or your monitoring tool of choice
• Failed tests block downstream tables, preventing bad data from reaching reports

This gives us continuous confidence in both our source data and the pipelines that process it.

Why It Works

By combining Soda for exploratory scanning and dbt for continuous validation, you get the best of both worlds: