dbt Tests vs. Sohovi: What Each One Actually Catches

The quick answer: dbt tests and Sohovi aren't really competing for the same job. dbt tests check that your transformation logic keeps producing what you told it to produce — not_null, unique, accepted_values, referential integrity between models. Sohovi profiles a raw or extracted file and surfaces what you didn't think to test for — inconsistent value formatting, outliers, and cross-field logic errors — before that file ever becomes a dbt source. Most analytics teams that use both stop arguing about whose dashboard is right nearly as often.

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What dbt Tests Are Actually Good At

dbt's built-in and package tests (dbt-utils, dbt-expectations) are genuinely strong at:

• Enforcing that a primary key column stays unique and non-null after every model run
• Checking that a foreign key actually exists in the referenced table
• Constraining a column to an accepted, known set of values
• Failing a CI run the moment a transformation breaks one of these guarantees

If your problem is "did last night's model run introduce a broken join," dbt tests are the right tool, and nothing here is an argument against them.

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Where a Green Test Suite Still Ships a Wrong Number

The gap is specifically in what a team thought to write a test for. A dbt test suite only checks the rules someone already anticipated. Two of the most common ways a report is still wrong despite every test passing:

1. Inconsistent raw values that are individually "valid." A region column with "US", "United States", and "us" all pass a not_null test and an accepted_values test if all three were (understandably) whitelisted — but a GROUP BY region still splits one country into three rows across two dashboards built at different times.
2. Cross-field logic nobody wrote a test for. A date_of_birth recorded after an admission_date, or a billed_quantity that doesn't match a summed usage_events count, isn't caught by any single-column test — it only shows up when you check two columns, or two files, against each other.

Sohovi profiles your datasets for quality issues in minutes — see what's broken before it breaks your pipeline — try Sohovi free.

Neither of these is a dbt failure. It's the space between "the transformation did what the code says" and "the underlying data was ever coherent to begin with."

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What Sohovi Adds

Sohovi profiles a file — a raw extract, a source export, or a comparison between two files — and surfaces exactly the class of issue above, without requiring anyone to have written a rule for it first:

• Automatic profiling flags value-format inconsistency (the three-spellings-of-US problem) and outliers on upload, before a rule exists.
• AI rule suggestions propose the Consistency or cross-field check a team hasn't thought to write yet.
• The free reconciliation tool compares two extracts (the two files feeding two disagreeing dashboards, for instance) and shows exactly which rows and columns diverged.
• A DQ score with a column-level breakdown gives a single number both teams can point to instead of each defending their own dashboard.

None of this replaces dbt tests for guaranteeing transformation logic stays correct run over run — it's upstream of that, on the source data itself.

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Comparison

| | dbt tests | Sohovi | |---|---|---| | Checks transformation logic stays correct | Yes | No | | Catches inconsistent raw value formatting | Only if explicitly tested | Yes, automatically on profiling | | Compares two files/extracts directly | No | Yes (free reconciliation tool) | | Requires SQL/dbt project setup | Yes | No | | Suggests rules you hadn't thought of | No | Yes (AI rule suggestions) | | Runs inside a CI/orchestration pipeline | Yes | Not natively — file-based | | Needs a data engineer | Usually | No |

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How Teams Actually Use Both

The teams that stop having the "whose number is right" argument tend to use dbt tests to guarantee the pipeline keeps doing what it's told, and something like Sohovi to catch the raw-data inconsistency and cross-field logic that a test suite was never going to be written for — either before a new source gets added to a dbt project, or reactively, the moment two dashboards disagree, to find which extract and which column caused it.

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Frequently Asked Questions

Q: Should we replace dbt tests with Sohovi? No — they check different things. dbt tests guarantee your transformation logic; Sohovi profiles the raw data and catches what nobody wrote a test for. Most analytics teams keep dbt tests for pipeline guarantees and add a profiling step for new or suspect sources.

Q: Can Sohovi test dbt models directly? Not as a warehouse connection today. Sohovi works from exported files — export a model's output or a raw source as a CSV and profile or reconcile it that way. For teams whose actual problem is "does this raw CRM export match what the warehouse eventually shows," that export-and-compare step is usually the missing link anyway.

Q: We already have 200 dbt tests passing. Why would we still need this? Because a passing test suite only proves the data matches the rules you already wrote. If two dashboards still disagree, the bug is very likely in a rule nobody wrote yet — which is exactly what profiling and AI rule suggestions are built to surface.

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If two dashboards built off the same warehouse still disagree, the fastest way to find out why usually isn't adding another dbt test — it's profiling the extract each dashboard was actually built from and comparing them directly. Try it free at sohovi.com.

Sohovi validates your dataset before it enters the warehouse — catching format errors, nulls, and duplicates at the source.