Data is only as useful as it is trustworthy. In modern analytics and AI stacks-where data moves fast, comes from many sources, and feeds high‑impact decisions-“we’ll clean it later” turns into broken dashboards, unreliable models, and expensive rework.

That’s where Great Expectations (GX) fits. It’s a popular open‑source data validation and data quality testing framework that helps teams define what “good data” looks like and automatically validate it as data flows through pipelines. Instead of relying on ad‑hoc checks or manual SQL spot checks, GX encourages a clear, repeatable practice: treat data quality like code-versioned, reviewed, and run on every deployment.

Why Data Quality Must Start With the First Pipeline (Not the 50th)

When teams wait to implement validation until the system is “mature,” data problems become harder to diagnose because:

• More downstream consumers rely on the data (dashboards, ML features, reverse ETL tools).
• Data transformations get layered and opaque.
• Ownership becomes unclear (“Is this a source issue, an ingestion issue, or a modeling issue?”).
• Fixes require backfills, stakeholder coordination, and reprocessing costs.

Starting early doesn’t mean building a perfect data quality monitoring program on day one. It means creating a minimum viable data contract for your first pipeline-and continuously improving it.

What Is Great Expectations (and Why Teams Use It)

Great Expectations is a data quality framework that lets you:

• Define checks (called Expectations) such as:
• Column values are not null
• Values fall within a range
• Categories belong to an allowed set
• Uniqueness constraints
• Regex patterns for IDs/emails
• Group checks into Expectation Suites
• Run validations and produce human‑readable reports (Data Docs in GX terminology)
• Integrate validations into pipelines (batch jobs, orchestration tools, CI/CD)

The appeal is straightforward: it makes data quality checks explicit, testable, and repeatable, instead of tribal knowledge living in notebooks and Slack threads.

The Core Mindset: Data Quality as Product Quality

If you’ve ever shipped software, you already understand the idea: you don’t just “hope” the app works-you run tests. Data deserves the same treatment.

A strong Great Expectations practice helps you:

• Catch issues close to the source
• Prevent silent failures (the worst kind)
• Define shared rules across engineering, analytics, and AI teams
• Create documentation that both technical and non‑technical stakeholders can interpret

Common Data Quality Failures (and the Expectations That Catch Them)

Here are frequent real‑world issues and the corresponding expectation patterns that prevent them:

1) Missing or Sparse Data

Symptom: A pipeline “succeeds” but half the rows are missing key values.

Expectations:

• Not‑null checks on critical columns
• Minimum completeness percentage (e.g., 99% not null)

2) Schema Drift

Symptom: A vendor adds a column, renames one, or changes data types.

Expectations:

• Expected column set
• Expected types (where supported)
• Alerts when unexpected columns appear

3) Duplicates That Break Aggregations

Symptom: A join duplicates orders and inflates revenue.

Expectations:

• Primary key uniqueness
• Composite uniqueness (order_id + line_item_id)

4) Out-of-Range Values

Symptom: Negative prices, impossible timestamps, unrealistic quantities.

Expectations:

• Numeric ranges (e.g., quantity >= 0)
• Date bounds (e.g., created_at <= now)

5) Invalid Categories and “New” Labels

Symptom: “CA” becomes “California,” or new product categories appear unannounced.

Expectations:

• Allowed value sets
• Warning‑level checks for “new but acceptable” categories

A Practical Blueprint: Implementing Great Expectations in Your First Pipeline

You don’t need hundreds of checks to start. You need the right checks.

Step 1: Identify the “Critical Few” Tables

Start with the datasets that:

• Drive executive dashboards
• Feed ML features
• Power customer‑facing experiences
• Integrate with finance/billing systems

Even one table is enough to begin.

Step 2: Define a Minimum Expectation Suite

A strong starter suite often includes:

• Schema expectations
• Required columns exist
• Completeness expectations
• Key fields not null (IDs, timestamps, foreign keys)
• Uniqueness expectations
• Primary keys unique
• Validity expectations
• Ranges, allowed sets, regex patterns
• Volume expectations
• Row counts within expected bounds (helps catch partial loads)

Think of it like a seatbelt: you’re not building a race car yet-you’re preventing the most dangerous failures.

Step 3: Decide What “Failure” Means

Not every quality issue should stop the pipeline. Create severity levels:

• Hard fail (block deploy / block downstream):
• Missing primary keys
• Massive volume drop
• Schema mismatch that breaks transforms
• Soft fail (warn + notify):
• Slight completeness degradation
• New category values
• Minor range anomalies

This prevents alert fatigue while still surfacing important signals.

Step 4: Integrate Into Orchestration (Where It Matters)

Great Expectations validations are most useful when they run:

• After ingestion (catch source issues early)
• After transformations (catch modeling issues)
• Before serving (protect dashboards and ML)

If you’re using orchestrators like Airflow or Dagster, validations can run as dedicated tasks/ops that produce artifacts (Data Docs, validation results) and alerts. If you’re deciding how to combine transformation and orchestration responsibilities, see dbt vs Airflow: data transformation vs pipeline orchestration.

Step 5: Make Results Visible

Validation results should not live only in logs. Ensure:

• Reports are stored and accessible
• Failures notify the right channel (Slack/email/pager)
• Owners are clear (who fixes what)

Visibility is what transforms data testing from a “nice idea” into an operational practice.

Concrete Example: A GX Expectation Suite + Running It in Airflow or Dagster

Below is a practical “first pipeline” example you can adapt. It shows:

1) a minimal GX Expectation Suite for an orders dataset

2) a simple validation function you can call from Airflow or Dagster

> Notes:

> - This example uses a Pandas DataFrame batch for readability (great for first implementations and many warehouse extracts).

> - In production, you can point GX at your warehouse/lake (e.g., via SQLAlchemy, Spark) using the same expectation concepts.

1) Define an Expectation Suite (GX 1.0-style example)

`python

gx_orders_suite.py

import great_expectations as gx

from great_expectations.expectations.expectation_configuration import (

ExpectationConfiguration,

)

def build_orders_suite() -> gx.ExpectationSuite:

suite = gx.ExpectationSuite(name="orders_quality")

suite.add_expectation_configuration(

ExpectationConfiguration(

expectation_type="expect_column_values_to_not_be_null",

kwargs={"column": "order_id"},

)

)

suite.add_expectation_configuration(

ExpectationConfiguration(

expectation_type="expect_column_values_to_be_unique",

kwargs={"column": "order_id"},

)

)

suite.add_expectation_configuration(

ExpectationConfiguration(

expectation_type="expect_column_values_to_not_be_null",

kwargs={"column": "created_at"},

)

)

suite.add_expectation_configuration(

ExpectationConfiguration(

expectation_type="expect_column_values_to_be_in_set",

kwargs={

"column": "status",

"value_set": ["pending", "paid", "shipped", "cancelled", "refunded"],

},

)

)

suite.add_expectation_configuration(

ExpectationConfiguration(

expectation_type="expect_column_values_to_be_between",

kwargs={"column": "total_amount", "min_value": 0},

)

)

return suite

`

2) Run validation (and fail the pipeline on hard failures)

`python

validate_orders.py

import pandas as pd

import great_expectations as gx

from gx_orders_suite import build_orders_suite

def validate_orders_df(df: pd.DataFrame) -> dict:

suite = build_orders_suite()

Create a GX Validator directly from a DataFrame

validator = gx.from_pandas(df)

validator.expectation_suite = suite

result = validator.validate() # returns a JSON-serializable dict-like result

if not result["success"]:

In orchestrators, raising an exception is the simplest "hard fail"

raise ValueError("Data quality validation failed for orders dataset.")

return result

`

3) Call it from Airflow (PythonOperator)

`python

dags/orders_pipeline.py

from airflow import DAG

from airflow.operators.python import PythonOperator

from datetime import datetime

import pandas as pd

from validate_orders import validate_orders_df

def extract_orders() -> pd.DataFrame:

Replace with your real extract (warehouse query, S3 read, API pull, etc.)

return pd.DataFrame([

{"order_id": "A1", "created_at": "2026-01-01", "status": "paid", "total_amount": 25.0},

{"order_id": "A2", "created_at": "2026-01-02", "status": "shipped", "total_amount": 10.0},

])

def run_validation():

df = extract_orders()

validate_orders_df(df)

with DAG(

dag_id="orders_with_data_quality_checks",

start_date=datetime(2026, 1, 1),

schedule="@daily",

catchup=False,

) as dag:

validate_task = PythonOperator(

task_id="validate_orders_data_quality",

python_callable=run_validation,

)

`

4) Call it from Dagster (asset/op)

`python

dagster_project/assets.py

import pandas as pd

from dagster import asset

from validate_orders import validate_orders_df

@asset

def orders():

df = pd.DataFrame([

{"order_id": "A1", "created_at": "2026-01-01", "status": "paid", "total_amount": 25.0},

{"order_id": "A2", "created_at": "2026-01-02", "status": "shipped", "total_amount": 10.0},

])

Hard-fail the run if validation fails

validate_orders_df(df)

return df

`

This pattern scales cleanly: keep your expectation suites versioned in the repo, run them as first‑class tasks in your ETL/ELT pipeline, and publish results where the team can see them.

Data Quality That Scales: Tips for Long-Term Success

Treat Expectation Suites as Versioned Assets

Store expectations alongside pipeline code and review them like any change:

• Pull requests
• Code review
• Release notes

This prevents “mystery rules” and helps new team members onboard quickly.

Start With Business Rules, Not Just Technical Rules

Not-null checks are helpful, but the strongest suites include rules like:

• “Refund amount cannot exceed original order amount”
• “A paid invoice must have a payment timestamp”
• “Trial users should not have renewal charges”

These are the rules that protect the business and reduce stakeholder escalations.

Use Sampling Strategically

Validating everything, everywhere can be expensive. Many teams:

• Run lightweight checks on every batch
• Run deeper profiling checks daily/weekly
• Use sampling for high-volume tables

Use Quality Metrics as KPIs

Instead of thinking only in terms of pass/fail, track:

• Completeness trends
• Duplicate rates
• Freshness SLAs
• Distribution drift

This turns data quality from reactive debugging into proactive improvement. For a deeper look at operationalizing trust with metadata and documentation, see automating documentation and auditing with dbt and DataHub.

Example: A Simple “First Pipeline” Expectation Strategy

Imagine you ingest orders into your warehouse.

A practical starter suite might include:

• order_id must exist and be unique
• created_at must not be null and must be within a reasonable window
• status must be in {pending, paid, shipped, cancelled, refunded}
• total_amount must be >= 0
• Row count must be within 70–130% of the trailing 7-day average (to catch sudden drops/spikes)

This doesn’t guarantee perfection-but it dramatically reduces the chance of shipping broken data downstream.

FAQ: Great Expectations and Data Quality in Pipelines

1) What is Great Expectations used for?

Great Expectations is used for data quality validation. Teams define rules (“expectations”) about what valid data should look like-then automatically test datasets during ingestion, transformation, or before serving to dashboards and ML systems.

2) When should I add data quality checks to my pipeline?

As early as possible-ideally in your first production pipeline. Starting small (core not-null, uniqueness, schema, and volume checks) prevents silent failures and sets a scalable foundation.

3) What are the most important data quality checks to start with?

Most teams see the fastest value from:

• Not-null checks for key columns
• Uniqueness for primary keys
• Schema/required column checks
• Range and allowed-value checks
• Row count bounds (volume anomaly detection)

4) Should data quality failures stop the pipeline?

It depends on severity. A common approach is:

• Hard fail for issues that make data unusable or misleading (missing IDs, broken schema)
• Soft fail (warn/notify) for issues that are informative but not catastrophic (new categories, mild completeness drift)

5) How do I prevent alert fatigue with Great Expectations?

Use:

• Severity tiers (warn vs fail)
• Routing (notify the right owner/team)
• Aggregated reporting (daily summary for non-critical checks)
• Stable thresholds (avoid overly sensitive rules)

The goal is actionable alerts, not noise.

6) Is Great Expectations only for data warehouses?

No. You can run data validation on ingestion layers, data lakes, warehouse tables, transformation outputs, and even data frames-wherever you can access the dataset to run checks.

7) How do expectation suites evolve over time?

They typically mature in layers:

1. Basic structural checks (schema, nulls, uniqueness)
2. Business rule checks (billing logic, lifecycle states)
3. Distribution and drift checks (statistical monitoring)
4. Automated governance (ownership, SLAs, quality dashboards)

Treat expectation suites like code: version them, review them, and update them as the business evolves.

8) What’s the difference between data validation and data monitoring?

• Validation checks whether a dataset meets defined rules at a point in time (pass/fail + metrics).
• Monitoring tracks trends over time (freshness, drift, anomaly patterns) and alerts when things degrade.

Great Expectations is often used as a validation foundation that feeds a broader data reliability strategy.

9) Can Great Expectations help with AI/ML data quality?

Yes. ML pipelines benefit heavily from:

• Feature completeness checks
• Range constraints (prevent outliers from poisoning training)
• Category consistency
• Drift detection patterns (often implemented via additional metrics and thresholds)

High-quality training data reduces model instability and improves reproducibility. If you’re tackling noisy operational telemetry, consider AI models for classifying logs and events in data pipelines.

Wrap-Up: Your Next Steps for Trustworthy Pipelines

If you want to build trust in analytics and AI outputs, start by making data quality checks part of the pipeline-not a cleanup project later. The most effective rollout is incremental:

1) pick one high‑value table,

2) ship a small Great Expectations suite (schema + nulls + uniqueness + basic validity),

3) run it in orchestration (Airflow/Dagster) where failures are visible,

4) expand into business rules and trend-based monitoring as usage grows.

That’s how teams move from “hoping the data is right” to reliable, testable data validation in production.