Great Expectations

This great expectations data quality covers features, architecture, pricing, and how it compares to alternatives.

Great Expectations is an open-source data quality and validation framework that lets data teams define, execute, and document expectations about their data using Python. In this Great Expectations review, we examine how the framework's "expectations as code" approach compares to alternatives like Soda, dbt tests, and Monte Carlo for ensuring data reliability.

Overview

Great Expectations (commonly abbreviated as GX) was created in 2018 and has become the standard open-source framework for data validation. The project has over 9,000 GitHub stars and is used by data teams at companies of all sizes. In 2024, the team launched GX Cloud, a managed SaaS platform that provides a UI-driven experience on top of the open-source framework.

The core philosophy is that data quality should be defined as code, version-controlled alongside data pipelines, and executed automatically. Expectations are human-readable assertions about data — "I expect this column to have no null values" or "I expect the mean of this column to be between 50 and 100." When expectations fail, GX generates detailed Data Docs — HTML reports showing exactly what went wrong, with sample failing rows.

Key Features and Architecture

Expectations Library

GX ships with 300+ built-in expectations covering null checks, uniqueness, value ranges, regex patterns, statistical distributions, column types, row counts, and cross-table comparisons. Custom expectations can be written in Python for domain-specific validation logic. Expectations are grouped into Expectation Suites that define the complete quality contract for a dataset.

Data Docs

When validations run, GX automatically generates rich HTML documentation showing results for every expectation — pass/fail status, observed values, sample failing rows, and historical trends. Data Docs serve as both validation reports and living documentation of data quality standards, shareable with stakeholders who don't write code.

Checkpoint System

Checkpoints orchestrate validation runs by connecting Expectation Suites to data sources and triggering actions on results (send Slack alerts, update a database, fail a pipeline). Checkpoints integrate into CI/CD pipelines and orchestrators like Airflow, Dagster, and Prefect to gate data pipeline progression on quality checks.

Multi-Backend Support

GX connects to data wherever it lives: Pandas DataFrames, Spark DataFrames, SQL databases (PostgreSQL, MySQL, BigQuery, Snowflake, Redshift, Databricks, Trino), and file formats (CSV, Parquet, JSON). The same expectations work across backends, so quality checks defined for a Pandas prototype work unchanged when data moves to Snowflake.

GX Cloud

The managed SaaS platform adds a visual UI for creating and managing expectations without writing Python, team collaboration features, scheduled validation runs, and centralized results dashboards. GX Cloud is designed to make data quality accessible to analysts and stakeholders beyond the data engineering team.

Profiling

The automated profiler analyzes a dataset and generates a starter set of expectations based on observed data patterns — column types, value distributions, null rates, and uniqueness. This accelerates the initial setup by providing a baseline that teams can refine rather than writing every expectation from scratch.

Ideal Use Cases

Data Pipeline Quality Gates

Data engineering teams insert GX checkpoints into Airflow DAGs or Dagster jobs to validate data at each pipeline stage. If expectations fail, the pipeline halts before bad data propagates to downstream tables, dashboards, or ML models. This is GX's most common use case.

Regulatory Compliance Validation

Organizations subject to SOX, HIPAA, or GDPR use GX to codify data quality rules as auditable expectations. The Data Docs provide evidence that quality checks ran and passed, supporting compliance documentation requirements.

Data Migration Testing

Teams migrating data between systems (on-premises to cloud, legacy warehouse to Snowflake) use GX to validate that migrated data matches source data. Expectations defined against the source system are run against the target to catch discrepancies.

ML Feature Validation

ML teams validate feature data before model training and inference to catch data drift, missing values, and distribution shifts that could degrade model performance. GX expectations serve as guardrails that prevent models from training on corrupted data.

Pricing and Licensing

Great Expectations open-source is free under the Apache 2.0 license. GX Cloud offers managed capabilities:

Self-hosted GX has minimal infrastructure requirements — it's a Python library that runs wherever your data pipelines run. No separate servers or databases needed. For comparison, Soda Cloud starts at ~$400/month, Monte Carlo starts at ~$30,000/year, and Anomalo pricing is enterprise-only.

Pros and Cons

Pros

• 300+ built-in expectations — comprehensive validation library covering nulls, ranges, distributions, regex, cross-table checks, and more
• Data Docs — auto-generated HTML reports provide clear, shareable evidence of data quality for technical and non-technical stakeholders
• Multi-backend support — same expectations work across Pandas, Spark, PostgreSQL, Snowflake, BigQuery, Redshift, and Databricks
• Pipeline integration — native integration with Airflow, Dagster, Prefect, and CI/CD systems for automated quality gates
• Open-source (Apache 2.0) — no licensing costs, full source code, 9,000+ GitHub stars, active community
• Expectations as code — version-controlled, reviewable, and testable quality definitions alongside pipeline code

Cons

• Steep learning curve — Data Contexts, Datasources, Expectation Suites, Checkpoints, and Batch Requests create a complex configuration hierarchy
• Configuration-heavy — YAML-based configuration can become verbose and difficult to manage for large numbers of datasets and expectations
• No built-in anomaly detection — GX validates against explicit rules; it doesn't automatically detect unexpected patterns like Monte Carlo or Anomalo
• Python-only — requires Python knowledge to set up and customize; no native support for SQL-only teams without GX Cloud
• Breaking changes between versions — the v0.x to v1.0 migration required significant refactoring for existing users

Alternatives and How It Compares

Soda

Soda offers both open-source (Soda Core) and commercial (Soda Cloud, ~$400/month) data quality solutions. Soda uses a YAML-based "checks" syntax that's simpler than GX's configuration hierarchy. Soda Cloud provides a UI, anomaly detection, and incident management. Soda is easier to get started with; GX offers more flexibility and a larger expectations library for complex validation scenarios.

dbt Tests

dbt includes built-in data tests (not_null, unique, accepted_values, relationships) and supports custom SQL tests. For teams already using dbt, adding tests to models is the simplest path to basic data quality. dbt tests are less comprehensive than GX expectations — no statistical checks, profiling, or Data Docs — but require zero additional tooling.

Monte Carlo

Monte Carlo (~$30,000/year) is a commercial data observability platform that automatically monitors data for freshness, volume, schema changes, and distribution anomalies without requiring explicit rule definitions. Monte Carlo complements rather than replaces GX: Monte Carlo catches unknown unknowns through ML-based anomaly detection, while GX validates known quality rules. Many teams use both.

Elementary

Elementary is an open-source data observability tool built for dbt users. It runs as a dbt package, collecting test results and generating monitoring dashboards. Elementary is simpler than GX but tightly coupled to dbt. For dbt-centric teams wanting basic observability without a separate tool, Elementary is a lightweight alternative.

Anomalo

Anomalo is a commercial data quality platform that uses ML to automatically detect anomalies without manual rule configuration. It's positioned as "data quality without the rules" — the opposite of GX's explicit expectations approach. Anomalo is easier to deploy but less customizable. Enterprise pricing only.