I Finally Ditched Great Expectations After 6 Months of Midnight Profiling Jobs

After 6 months of midnight profiling jobs, I tested Pandera, dbt tests, and Soda Core. Here’s what actually worked for teams who’ve outgrown GX.

Breaking up with Great Expectations wasn’t part of my plan. The relationship started fine. Then it grew heavy, slow, and hard to reason about. Eventually, I found myself running profiling jobs at midnight, wondering how a tool designed to make data quality easier had ballooned into a platform that required its own care and feeding. Sound familiar? If you’re hunting for Great Expectations alternatives, I’ve been exactly where you are. And I’ve got the scar tissue, benchmarks, and migration logs to prove it.

Work in data engineering or data science long enough, and you eventually hit that moment when Great Expectations stops feeling empowering and starts feeling like work. This guide walks through the complexity traps, compares Pandera, dbt tests, and Soda Core with honest pros and cons, and shares performance numbers from real pipelines. There’s also a decision framework that I use when advising teams, plus guidance on replacing Great Expectations in a data pipeline without breaking production.

The Great Expectations Complexity Trap: When Enterprise-Grade Becomes Enterprise-Bloat

Great Expectations started as a promising idea: declarative tests, data contracts, clean documentation, and a friendly onboarding story. That’s the honeymoon phase.

Reality shows up later.

My first encounter with the wall happened at Airbnb while building out experiment monitoring checks. Our pipelines weren’t huge by Meta standards, but large enough that validation overhead mattered. The same issues kept appearing:

• Too much boilerplate for simple checks
• Rendered documentation that nobody read
• Runtime performance degraded on wide DataFrames
• A suite concept that looked elegant but encouraged config sprawl

After migrating several teams off Great Expectations, the pattern is unmistakable. Complexity grows faster than your data quality needs do, especially when you only need lightweight schema enforcement or a handful of statistical rules.

Ever caught yourself saying, “Great Expectations is too complex for small teams”? Trust me, you’re not imagining it.

Pandera vs. Great Expectations: The DataFrame Validation Showdown with Benchmarks

When working directly in Python notebooks or designing experimentation metrics, Pandera is my go-to. The Pandera vs. Great Expectations for DataFrames debate is less a debate and more a mismatch. One is built for DataFrames. The other is built for everything else.

Here are the key differences that keep pulling me back:

• Pandera integrates cleanly with pandas and Polars
• Validation lives in code, not YAML
• Statistical checks are easier to express
• Runtime overhead is lower because it avoids the Great Expectations abstraction layers

Here’s a tiny example for sanity-checking experiment metric tables:

Python (Pandera style):

import pandera as pa
from pandera import Column, DataFrameSchema

schema = DataFrameSchema({
    "user_id": Column(int, nullable=False),
    "metric": Column(float, checks=pa.Check.greater_than_or_equal_to(0)),
    "variant": Column(str)
})

validated = schema.validate(df)

R equivalent, since proving to engineers that statisticians speak multiple dialects still brings me joy:

library(validate)

rules <- validator(
  is.integer(user_id),
  metric >= 0,
  is.character(variant)
)

validated <- confront(df, rules)

Performance observation from one of my Meta side projects:

Informal testing on a 5-million-row DataFrame showed Pandera consistently outperforming Great Expectations for equivalent schema validations. Actual times varied depending on schema complexity, the specific checks involved, hardware, and library versions. But the pattern held: Pandera’s lighter abstraction layer translated to faster execution in my DataFrame-centric workflows.

Not a scientific study. More like a hallway conversation backed by a script on my laptop. But consistent across datasets.

Searching for the best data quality frameworks for Python developers in 2025? Pandera belongs on your shortlist.

DBT Tests vs. Great Expectations: Why Adding Another Tool Might Be Your Real Problem

Teams often try to answer the Great Expectations vs. dbt data quality tools comparison question as if these tools exist in the same space. They don’t. DBT tests work well when your data quality rules align with SQL constructs, but they’re terrible when the logic is statistical or row-level.

Tool stacking is the real issue here.

Teams often do this:

• Use dbt tests for warehouse checks
• Add Great Expectations for Python checks
• Add custom scripts for experimental logic

Pretty soon, the validation logic is spread across three languages and four repos, and nobody remembers what’s being tested.

The dbt tests vs. Great Expectations pros and cons debate always leads me back to this principle:

• When your transformation logic lives in SQL, keep your validation in SQL when possible.
• When your transformation logic lives in Python, don’t force dbt to do things it wasn’t meant for.

Pick the ecosystem that matches your team’s mental model.

Soda Core vs. Great Expectations: The Middle-Ground Option Nobody Discusses Fairly

Soda Core sits in a weird spot in the ecosystem. Teams that want YAML simplicity without the Great Expectations machinery keep asking, “Soda Core vs. Great Expectations: which is better?”

Having used Soda Core for two real pipelines, here’s my honest assessment:

Advantages:

• Faster startup time
• Less configuration clutter
• Better CLI experience
• Slightly better developer ergonomics

Drawbacks:

• Limited expressiveness for statistical rules
• Harder to embed within Python-heavy workflows
• YAML still feels like a ceremony for row-level checks

Soda Core is one of the lightweight alternatives to Great Expectations for small teams, especially analytics teams that want a middle ground between config-driven validation and something code-heavy like Pandera.

Among open-source data validation tools better than Great Expectations, Soda Core is often underrated.

Decision Framework: Matching Tools to Team Size, Stack, and Actual Validation Needs

Here’s the simple version of the decision tree used when advising teams, whether at Meta or at Bay Area meetups:

Choose Pandera if:

• You live in pandas or Polars
• You want code-first rules
• You need performance
• You want something easier than Great Expectations without losing power

Choose DBT tests if:

• Your model logic is SQL-centric
• You care about warehouse constraints
• Rules involve relationships across tables
• You want analytics engineers to own quality

Choose Soda Core if:

• You want lightweight YAML
• You want something simpler than Great Expectations
• You need basic monitors, not heavy schemas
• You don’t need Python-heavy customization

Choose nothing extra if:

• You already have validation in your transformation logic
• You can express your rules directly in SQL or code
• You’re adding tools because of FOMO rather than real quality needs

Teams often ask for data validation tools that scale better than Great Expectations. The truth, from someone who spent half a decade optimizing experiment pipelines, is that the best tool is usually the one closest to your compute engine and your developer workflow.

Right now, my stack looks like this:

• Pandera for any Python-centric validations
• dbt built-in tests for warehouse models
• Custom statistical checks in Python scripts when the logic gets hairy
• No Great Expectations anywhere

Want to know how to replace Great Expectations in a data pipeline without turning launch week into a fire drill? Here’s the simple plan:

1. Identify which expectations actually catch issues
2. Rewrite those in Pandera, dbt tests, or Soda Core
3. Remove dead expectations that never trigger
4. Gradually turn off the Great Expectations suite by suite
5. Run dual validation for one week to confirm nothing breaks

A full rewrite isn’t necessary. A thoughtful migration is.

And for those still comparing Great Expectations alternatives, the key is knowing which parts of your validation workflow actually matter. The simpler the tool, the fewer places bugs can hide.

One rule of thumb from someone who’s over-engineered more validation systems than she’d like to admit: pick the tool you’re most willing to maintain at 2 a.m. when the pipeline is red, your on-call phone is buzzing, and your only companion is a cup of tea and maybe a tabla rhythm looping in your head.