Bad data is like a banana peel in a cartoon. It sits there quietly. Then your dashboard slips on it. Your team gasps. Your model makes weird choices. Your boss asks, “Why is revenue negative?” That is why data validation matters.
TLDR: Great Expectations is a popular tool for checking data quality, but it is not the only option. Tools like Soda, Amazon Deequ, Pandera, TensorFlow Data Validation, and dbt tests with Elementary can also help you catch bad data early. Each one has a different style, so the best choice depends on your stack, team size, and how much automation you want.
Why data validation tools matter
Data moves fast now. It jumps from apps to warehouses. It flows into dashboards. It feeds machine learning models. It gets copied, joined, filtered, and transformed.
That sounds useful. It also sounds risky.
A tiny data issue can create a big mess. A missing column can break a report. A strange value can confuse a model. A duplicate record can make sales look amazing. Until someone checks.
Data quality platforms help you check your data before it causes chaos. They act like friendly guards at the data gate. They ask simple questions:
- Is this column present?
- Are values in the right range?
- Are there too many nulls?
- Did the row count suddenly drop?
- Does this data look normal?
Great Expectations is very well known for this. It lets teams write “expectations” for data. For example, you can expect a customer ID to never be null. You can expect an age column to be between 0 and 120. Nice and tidy.
But there are other great tools too. Some are more developer friendly. Some are better for big data. Some are better for machine learning. Some are simple and quick.
Let’s meet five of them.
1. Soda
Soda is a popular data quality platform with a clean and modern feel. It helps teams test data in databases, warehouses, and pipelines. It is often used with tools like Snowflake, BigQuery, Redshift, Databricks, and PostgreSQL.
Soda uses a simple language called SodaCL. It looks friendly. It is not scary. You can write checks like:
- Row count should be greater than zero.
- Missing values should be less than 5%.
- Duplicate customer IDs should not exist.
- Revenue should never be negative.
This makes Soda nice for data engineers and analytics engineers. It also has a cloud platform for monitoring results. That means teams can see failures, trends, and alerts in one place.
Why it is fun: Soda feels like a data health app. Your tables get checkups. If something looks sick, Soda waves a red flag.
Best for:
- Teams that want easy data quality checks.
- Modern data warehouse users.
- People who want alerts and monitoring.
- Teams that like readable test files.
Simple example: Imagine you run an online store. Soda can check that every order has an order ID, a customer ID, and a positive total amount. If totals become negative, Soda can shout before the dashboard lies.
Things to know: Soda is easy to start with, but advanced workflows may need setup. If you want a polished monitoring layer, the cloud product is a big part of the experience.
2. Amazon Deequ
Amazon Deequ is a data quality library built on Apache Spark. It was created by AWS. It is great for large datasets. Very large datasets. Big enough to make your laptop sweat.
Deequ lets you define checks on data. It can measure things like completeness, uniqueness, and value ranges. It can also find patterns and constraints from data.
That last part is interesting. Deequ can profile your data and suggest rules. It is like saying, “Hey Deequ, please sniff this dataset and tell me what looks normal.”
Why it is fun: Deequ is like a gym coach for big data. It counts. It measures. It notices when your data skips leg day.
Best for:
- Teams using Spark.
- AWS-heavy data platforms.
- Very large data jobs.
- Engineers who are comfortable with code.
Simple example: A streaming company has billions of viewing events. Deequ can check that user IDs are complete, video IDs are valid, and watch time is not negative. It can run these checks at scale.
Things to know: Deequ is more technical than some tools. It is a library, not a shiny full platform by itself. You may need to build your own reporting and alerting around it.
Still, for Spark users, Deequ is powerful. It is not tiny. It is not fluffy. It is a sturdy tool for big jobs.
3. Pandera
Pandera is a data validation tool for Python. It works especially well with pandas dataframes. It also supports other dataframe systems, including Polars and PySpark in some workflows.
If your team lives in notebooks and Python scripts, Pandera can feel natural. You define schemas for your dataframes. A schema says what columns should exist, what types they should have, and what values are allowed.
For example, you can say:
- The email column must be text.
- The signup date must be a date.
- The age column must be greater than 18.
- The score column must be between 0 and 100.
Then Pandera checks your dataframe. If something is wrong, it tells you. No drama. Just facts.
Why it is fun: Pandera is like a seatbelt for pandas. You may not notice it when things are fine. But when a crash comes, you are happy it is there.
Best for:
- Python data teams.
- Data scientists.
- Notebook users.
- Teams that validate data during analysis or model training.
Simple example: A data scientist is training a churn model. Pandera can check the training data before the model sees it. Are customer ages valid? Are subscription types allowed? Are target labels clean? Great. Train away.
Things to know: Pandera is code-first. It is not mainly a dashboard product. If you want fancy web monitoring, you may need to connect it with other tools.
But for Python folks, it is delightful. It is clear. It is flexible. It fits into regular work without making everything feel heavy.
4. TensorFlow Data Validation
TensorFlow Data Validation, often called TFDV, is a tool for checking data used in machine learning pipelines. It is part of the TensorFlow Extended ecosystem.
Machine learning data needs special care. Models are picky. They can behave strangely when data changes. A column may shift. A category may disappear. A new value may appear. Suddenly, the model is confused.
TFDV helps with this. It can create statistics for datasets. It can infer schemas. It can detect anomalies. It can compare training data and serving data.
That last point matters a lot. Your model might train on one kind of data but receive another kind in production. This is called training serving skew. It is sneaky. It is annoying. It is bad news.
Why it is fun: TFDV is like a bouncer for your model. If weird data tries to enter the club, TFDV checks the list.
Best for:
- Machine learning teams.
- TensorFlow users.
- Production ML pipelines.
- Teams that need schema and drift checks.
Simple example: A bank trains a fraud model. During training, the transaction type column has five categories. In production, a sixth category appears. TFDV can flag this before the model makes odd predictions.
Things to know: TFDV is strongest in ML workflows. It may feel too specialized for simple warehouse testing. If you just want SQL-style checks on tables, another tool may be easier.
But for model data, TFDV is a smart pick. It watches for the kind of changes that can quietly hurt predictions.
5. dbt tests with Elementary
dbt is not only a transformation tool. It also has built-in testing. You can test your models after you build them. This is very useful for analytics teams.
Basic dbt tests can check things like:
- A column is not null.
- A column is unique.
- A value is accepted.
- A relationship exists between tables.
These tests are simple but powerful. They live close to your data models. That makes them easy to maintain. If your team already uses dbt, this is a natural place to start.
Then comes Elementary. Elementary adds data observability and monitoring on top of dbt. It can help detect anomalies, track test failures, and create reports.
So dbt tests are the guardrails. Elementary is the lookout tower.
Why it is fun: dbt tests are like sticky notes on your data models. Elementary turns those sticky notes into a control room with blinking lights.
Best for:
- Analytics engineering teams.
- Companies already using dbt.
- Warehouse-first workflows.
- Teams that want tests near transformation logic.
Simple example: Your team builds a revenue model in dbt. You can test that every payment has an ID, every order maps to a customer, and every status is in an approved list. Elementary can then show failures and patterns over time.
Things to know: dbt tests are excellent for transformed data. They are less focused on raw data profiling out of the box. Elementary helps add more visibility, but the setup still depends on your dbt project.
How to choose the right platform
There is no single magic tool. Sorry. The data wizard is on vacation.
The best choice depends on how your team works. Start with simple questions:
- Where does your data live? In a warehouse, Spark, Python, or ML pipeline?
- Who writes the checks? Data engineers, analysts, or data scientists?
- Do you need dashboards? Or is code enough?
- Do you need alerts? Should failures go to Slack or email?
- How big is your data? Tiny, normal, huge, or monster huge?
Here is a simple cheat sheet:
- Choose Soda if you want friendly checks and monitoring for modern data platforms.
- Choose Deequ if you use Spark and need validation at big scale.
- Choose Pandera if you love Python and work with dataframes.
- Choose TFDV if you care about machine learning data quality.
- Choose dbt tests with Elementary if your analytics stack already runs on dbt.
What makes a good data validation rule?
A good rule is clear. It is useful. It catches real problems. It does not create noise all day.
Bad rule: “Data should be good.”
Good rule: “Order amount must be greater than or equal to zero.”
Great rule: “Order amount must be greater than or equal to zero, and the null rate must stay below 1%.”
Keep rules simple at first. Add more as you learn. Do not try to validate the entire universe on day one. That way lies madness, cold coffee, and many angry alerts.
Final thoughts
Data quality is not glamorous. It does not wear sunglasses. It does not get applause at company meetings. But it saves teams from painful mistakes.
Great Expectations is a strong option. But Soda, Deequ, Pandera, TensorFlow Data Validation, and dbt tests with Elementary are also excellent choices. Each one solves the same basic problem in a different way.
Your goal is simple. Catch bad data early. Trust your reports. Protect your models. Help your team sleep better.
Clean data is happy data. Happy data makes better decisions. And better decisions mean fewer banana peels on the dashboard floor.
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