The verdict up front: Great Expectations is the industry standard for data engineering teams who want to define quality rules in Python and run them in automated pipelines. Sohovi is for business users — analysts, ops teams, marketers — who need to profile, validate, and clean data without writing code. If your team has a data engineer, Great Expectations; if it doesn't, Sohovi.
What Each Tool Is
Great Expectations (GE) is an open-source Python library for defining "expectations" about your data — declarative rules like "this column should never be null" or "values should be between 0 and 100" — and running them as automated test suites. It integrates with dbt, Airflow, Spark, and major data warehouses. It generates beautiful HTML data documentation ("Data Docs"). It's free, powerful, and requires Python.
Sohovi is a browser-based data quality platform for non-technical users. Upload a CSV or spreadsheet, get an instant profile, define validation rules with point-and-click, run deduplication, and export a quality report — no code, no Python, no local setup.
Sohovi automatically finds every duplicate in your dataset — including near-matches — and shows you exactly which rows are affected.
What "No-Code vs Code-First" Actually Means in Practice
Setting up a basic "email column must not be null" rule
In Great Expectations:
import great_expectations as gx
context = gx.get_context()
ds = context.sources.add_pandas_filesystem(...)
validator = context.get_validator(...)
validator.expect_column_values_to_not_be_null("email")
validator.save_expectation_suite()
You also need to: install Python, install GE (pip install great_expectations), configure a data source, set up a checkpoint to run validations, and learn the CLI. Time to first validation: 1–3 hours for someone comfortable with Python.
Sohovi lets you set up validation rules for any column and instantly see which rows fall outside them — no code or SQL required.
In Sohovi:
- Upload CSV
- Click the email column
- Set rule: "Must not be null"
- Click Apply
Time to first validation: under 3 minutes.
Comparison Table
| | Great Expectations | Sohovi | |---|---|---| | Setup | Python environment, pip install | Browser — no setup | | Writing rules | Python/YAML code | Point-and-click UI | | Who uses it | Data engineers | Business users, analysts | | Pipeline integration | Native (Airflow, dbt, Spark) | File-based (CSV/Excel upload) | | Data profiling | Profiler exists (limited UI) | Automatic, visual | | Deduplication | Not built-in | Yes (exact + fuzzy) | | PII detection | Not built-in | Yes | | Data Docs (reports) | HTML generation | Web-based quality report | | Collaboration | Git-based | Team accounts | | Price | Free (OSS) | Free tier + paid plans |
When Great Expectations Is the Clear Winner
You have data engineers. GE's strength is automation inside pipelines. If your team already runs dbt or Airflow, GE integrates natively — tests run on every pipeline execution without human intervention. That's a fundamentally different use case than Sohovi's file-upload model.
You need pipeline-level data contracts. GE is how engineering teams formalize agreements about what data should look like at each pipeline stage. If you're building infrastructure for data quality monitoring across multiple systems, GE is the right architectural choice.
You want version-controlled quality definitions. GE's expectation suites are files you can check into Git. A team of engineers can review, approve, and diff quality rules the same way they review code.
When Great Expectations Is the Wrong Choice
You don't have a data engineer and need to run quality checks on a CSV before a campaign send. You want to profile a vendor file before importing it. Your ops team needs to spot-check data quality on weekly exports without writing Python. In all of these cases, GE's power becomes friction — you'd spend hours learning the tool to accomplish a 10-minute task.
Frequently Asked Questions
Q: Can a non-technical person learn Great Expectations? GE has improved its documentation significantly, but the learning curve is real. You need Python familiarity, understanding of concepts like validators, checkpoints, and data sources, and comfort with terminal-based setup. Most non-technical users who attempt GE give up in the setup phase. If Python isn't in your toolkit, the time investment doesn't pay off for ad-hoc quality work.
Q: Does Great Expectations work with CSV files? Yes — GE supports Pandas DataFrames (which can be loaded from CSV). But you still need Python to load the file and run the validator. There's no UI for dragging in a CSV.
Q: Is there a version of Great Expectations with a UI? GX Cloud (the commercial product) adds a web UI. It's priced for enterprise data teams and still assumes engineering resources.
Q: Can I use both Sohovi and Great Expectations? Yes — many teams use Sohovi for ad-hoc, business-side quality checks (before a campaign, before a migration, on a vendor file) and Great Expectations for automated pipeline checks. They're complementary, not competing, for teams that have both technical and non-technical users.
If your team doesn't have Python resources and you need data quality answers today — not after three hours of environment setup — try Sohovi free on your own file. Upload a CSV and see your quality profile in under a minute, nothing to install.