How to Implement Data Quality Monitoring with Great Expectations

Modern data engineering pipelines are only as dependable as the data passing through them. Silent data corruption—such as schema drift, unexpected NULL spikes, or distribution shifts—often bypasses basic infrastructure monitoring, breaking downstream dashboards and invalidating machine learning models before anyone notices. To prevent these failures, engineering teams must establish continuous, automated guardrails. Great Expectations (GX) has emerged as the premier open-source framework for this exact challenge, allowing teams to test, document, and profile data pipelines programmatically.

To successfully implement data quality monitoring across production environments, teams need more than ad-hoc unit tests. They need an end-to-end framework integrated directly into ingestion and transformation layers on cloud data warehouses and lakehouses like Snowflake, Databricks, AWS, and Azure.

Understanding the Great Expectations Architecture

Great Expectations structures data quality around human-readable, declarative assertions known as Expectations. Rather than writing brittle custom SQL assertions or manual verification scripts, GX provides a standardized metadata engine built around five core primitives:

• Data Context: The operational container for your deployment. It manages project configuration, links to metadata stores, and keeps credentials secure across environments.
• Datasources & Data Assets: Abstractions that connect GX to execution engines (Pandas, Spark, or SQL databases like Snowflake and Redshift) and identify specific tables, queries, or file partitions.
• Expectation Suites: Bundles of individual assertions (e.g., expect_column_values_to_not_be_null, expect_column_quantile_values_to_be_between) applied to a given data asset.
• Validation Results & Checkpoints: The execution engine. Checkpoints run an Expectation Suite against designated batches of data, evaluate pass/fail criteria, and trigger automated downstream actions.
• Data Docs: Clean, automatically generated HTML reports that turn validation results into accessible documentation for technical and business stakeholders alike.

When organizations partner with Izan Technologies to architect data platforms, we often implement Great Expectations because it cleanly separates validation logic from transformation code, maintaining data reliability across distributed engineering teams.

Great Expectations vs. Alternative Quality Tools

Choosing the right tool to implement data quality depends on your architecture, compute footprint, and organizational maturity.

While dbt tests are ideal for SQL-only warehouse models, Great Expectations excels in hybrid architectures where data moves from landing storage (such as AWS S3 or Azure ADLS) through distributed processing in Databricks and into Snowflake.

Step-by-Step: How to Implement Data Quality Monitoring

Executing continuous data quality monitoring requires a disciplined rollout. Below is an engineering blueprint for establishing Great Expectations within an automated production pipeline.

Step 1: Initialize the Data Context Programmatically

In production CI/CD workflows, avoid interactive CLI setups. Instead, initialize and configure your Data Context via code or infrastructure-as-code, storing the configuration in cloud storage (such as an S3 bucket or Azure Blob container).

import great_expectations as gx

# Initialize a fluent data context backed by cloud storage
context = gx.get_context(project_config_dir="s3://my-company-gx-store/great_expectations/")

Step 2: Connect Datasources and Assets

Define the execution engine and register your target datasets. Using the modern Fluent API, you can bind directly to a Snowflake instance:

datasource = context.sources.add_snowflake(
    name="snowflake_analytics",
    connection_string="snowflake://<user>:<password>@<account>/ANALYTICS_DB/PUBLIC?warehouse=COMPUTE_WH&role=DATA_ENGINEER",
)

# Define an asset targeting your core orders table
data_asset = datasource.add_table_asset(
    name="fact_orders", 
    table_name="FACT_ORDERS"
)

Step 3: Define Declarative Expectation Suites

Create an Expectation Suite that balances structural integrity with statistical distribution metrics:

suite = context.add_expectation_suite(expectation_suite_name="fact_orders_production_suite")

# Schema & Integrity Checks
suite.add_expectation(
    gx.expectations.ExpectColumnValuesToNotBeNull(column="order_id")
)
suite.add_expectation(
    gx.expectations.ExpectColumnValuesToBeUnique(column="order_id")
)

# Business Logic Checks
suite.add_expectation(
    gx.expectations.ExpectColumnValuesToBeBetween(
        column="order_amount", 
        min_value=0.01, 
        max_value=50000.00
    )
)

# Categorical Checks
suite.add_expectation(
    gx.expectations.ExpectColumnValuesToBeInSet(
        column="order_status",
        value_set=["PENDING", "PROCESSED", "SHIPPED", "CANCELLED", "REFUNDED"]
    )
)

Step 4: Configure Checkpoints and Automated Actions

Checkpoints combine data retrieval, validation execution, and notifications into a single reproducible run. By attaching Actions, you automate operational notifications (e.g., Slack or PagerDuty) and publish updated Data Docs to an internal static hosting site (such as an authenticated S3 bucket behind CloudFront).

checkpoint = context.add_or_update_checkpoint(
    name="daily_fact_orders_checkpoint",
    validations=[
        {
            "batch_request": data_asset.build_batch_request(),
            "expectation_suite_name": "fact_orders_production_suite",
        }
    ],
    action_list=[
        {
            "name": "store_validation_result",
            "action": {"class_name": "StoreValidationResultAction"},
        },
        {
            "name": "update_data_docs",
            "action": {"class_name": "UpdateDataDocsAction"},
        },
        {
            "name": "send_slack_alert",
            "action": {
                "class_name": "SlackNotificationAction",
                "slack_webhook": "${SLACK_WEBHOOK_URL}",
                "notify_on": "failure",
            },
        },
    ],
)

Step 5: Orchestrate Within Your Pipeline

Run the checkpoint as a non-negotiable step in your orchestrator (Airflow, Prefect, or Databricks Workflows). If the checkpoint fails:

1. Stop downstream transformation models to prevent propagating bad data.
2. Route the quarantined records to a dead-letter queue (DLQ) for analysis.
3. Alert on-call engineers via the configured webhook.

# Airflow PythonOperator execution
result = checkpoint.run()
if not result["success"]:
    raise ValueError("Data quality validation failed. Halting downstream jobs.")

Production Best Practices for Data Quality Monitoring

To avoid the operational pitfalls of data testing, high-performing engineering teams adopt several core practices:

1. Guard Against Alert Fatigue

Do not set strict thresholds on highly volatile metrics right away. If an assertion fires every Tuesday due to regular business cycles, engineers will quickly ignore notifications. Use statistical expectations (like expect_column_mean_to_be_between) with historical baseline tolerances before moving to hard limits.

2. Shift Left: Validate at Ingestion

Validating data inside the cloud data warehouse is standard, but catching issues before compute-heavy transformation runs saves both warehouse costs and processing time. Run lightweight checks on raw JSON or Parquet batches in landing zones using PySpark or DuckDB before triggering warehouse ingestion.

3. Treat Expectations as Code

Store Expectation Suites in version control alongside your transformation code. Enforce peer review on suite changes through standard pull requests to ensure business logic remains aligned with product specifications.

4. Separate Critical Failures from Warnings

Not all failures require halting the pipeline. Configure multi-tiered suites:

• Tier 1 (Hard Stop): Primary key uniqueness, missing partition keys, non-null constraints.
• Tier 2 (Warning Only): Mild distribution drift, unexpected new values in low-priority categorical columns.

How Izan Technologies Helps

Designing a reliable data platform requires aligning scalable infrastructure with robust data observability. At Izan Technologies, we design and deploy enterprise-grade data engineering, machine learning, and data quality architectures across AWS, Azure, Snowflake, and Databricks.

Whether you need to remediate broken ingestion flows, eliminate silent pipeline failures, or automate compliance validation, our team delivers production-ready systems tailored to your stack. Crucially, our engagements are built for long-term independence: our clients own 100% of the IP we deliver, with zero vendor lock-in or proprietary licensing.

Ready to secure your data pipelines against downstream failures? Try our Smart AI DataStudio to explore intelligent data workflows, or talk to an expert to review your data quality strategy today.