Chapter 2
Great Expectations: Architecture and Ecosystem

Far more than a simple validation library, Great Expectations represents a modular, extensible foundation for orchestrating trust in data. This chapter peels back the layers of its architecture and ecosystem, spotlighting how its innovative abstractions enable seamless integration, observability, and collaboration across heterogeneous data landscapes. Prepare to deconstruct the moving parts, reveal their interdependencies, and discover how Great Expectations scales from isolated scripts to enterprise-wide guardianship of data quality.

2.1 Core Components and Model

Great Expectations provides a robust framework designed to enforce data quality through a modular and extensible architecture. At its foundation lie four cardinal abstractions: Expectations, Data Sources, Data Contexts, and Checkpoints. These components collectively form an interoperable ecosystem that facilitates expressive, maintainable, and testable data validation workflows within contemporary data engineering environments.

Expectations serve as declarative assertions about data characteristics. More formally, an Expectation is a parametrized predicate defining constraints on columnar, table-level, or domain-specific properties. Each Expectation object encapsulates the logic required to evaluate whether the data satisfies specified conditions, such as value ranges, uniqueness, distributional properties, or relationships between columns. Structurally, an Expectation consists of:

• Expectation type: a unique identifier (e.g., expect_column_values_to_be_between) that corresponds to a predefined validation logic.
• Parameters: a dictionary of expected values and constraints (e.g., min_value, max_value).
• Metadata: auxiliary information including human-readable descriptions, severity levels, and tags.
• Validation logic: encapsulated either as native Python functions or pluggable execution strategies integrating with various compute engines (e.g., Pandas, SQL, Spark).

Expectation evaluations produce structured validation results containing verdicts such as success, failure, or partial expectation results, enriched with diagnostics and summary statistics. These results are designed to be serialized and consumed by downstream components for reporting or automated actions.

Data Sources abstract the ingestion points and computational backends that provide data batches to validate. Each Data Source acts as an adapter layer wrapping connections and query mechanics for one or more storage platforms. The architecture supports heterogeneous sources-ranging from file-based systems (CSV, Parquet) to relational databases and distributed analytic engines. Internally, a Data Source encapsulates:

• Connection configuration: credentials, endpoints, and other parameters required to access the underlying system.
• Batching logic: mechanisms to slice datasets into manageable chunks (batches) based on time windows, partition predicates, or other domain-specific criteria.
• Execution engine bindings: connectors that translate validation queries and expectation execution logic into platform-specific commands.
• Data connectors: abstractions which map logical batch requests to physical data entities, enabling dynamic discovery of partitions or tables.

This layer ensures that Expectation evaluations remain agnostic of the data storage and processing backend, enabling portability of validation workflows across diverse environments.

Data Contexts embody the runtime environment and configuration state centralizing all validation-related artifacts. The Data Context manages lifecycle aspects, orchestrating access to Expectation Suites, Data Sources, Checkpoints, and the validation store. Internally, it maintains:

• Configuration repository: a hierarchy of YAML or JSON configuration files detailing Data Sources, Expectation Suites, and execution parameters.
• Artifact management: versioned storage and retrieval of Expectation Suites and Validation Results, facilitating reproducibility and auditability.
• Execution environment: the context for running validation jobs, assembling dependencies, and resolving resource paths.

The Data Context offers high-level APIs granting programmatic control and integration within CI/CD pipelines, orchestrators, and interactive analysis environments. It promotes separation of concerns by decoupling declarative Expectation definitions from execution and monitoring logic.

Checkpoints define executable validation pipelines binding together Expectations and data batches within an operational schedule or trigger framework. A Checkpoint configuration specifies:

• Target Expectation Suites: collections of Expectations to be executed.
• Batch requests: parameterized specifications selecting data slices from Data Sources.
• Validation actions: subsequent processing steps such as notifications, storing Validation Results, or halting pipelines upon failure.

At runtime, Checkpoints instantiate concrete validation jobs that generate comprehensive validation artifacts and enforce quality gates. The modularity of Checkpoints enables complex orchestrations where multiple Data Contexts and Data Sources coalesce, facilitating multi-tenant and multi-environment deployments.

Interaction patterns and architectural cohesion arise from the clear interfaces and roles defined among these components. The Data Context acts as the nucleus, coordinating access to Data Sources for data retrieval, referencing Expectations for quality assertions, and managing Checkpoints to trigger validation workflows. Data Sources provide data batches on demand, while Expectation Suites specify the criteria applied against those batches, producing Validation Results that feed back into the Data Context's stores. Checkpoints operationalize these configurations, generating repeatable, automated validation runs.

This architecture enables:

• Testability: Expectation Suites can be independently developed and unit tested against sample data sets, while Data Sources and Data Contexts facilitate integration testing with real or mock environments.
• Maintainability: The separation into discrete, reusable objects aligns with best practices for configuration management, versioning, and environment-specific overrides.
• Extensibility: New Expectation types, Data Sources, or execution connectors can be plugged in with minimal disruption to existing workflows.

In synthesis, these core abstractions instantiate the conceptual model enabling Great Expectations to provide a unified, declarative, and operationally robust data validation framework. Their internal structures codify domain-specific knowledge while respecting principles of modularity and separation of concerns, which is critical in modern data stack implementations.

2.2 Data Assets and Data Connectors

Great Expectations (GE) facilitates robust data validation through an abstraction layer that effectively decouples data assets from their physical storage and access mechanisms. This abstraction ensures that data validation logic remains environment-agnostic and reproducible in varied operational contexts, whether the underlying data reside in relational databases, file systems, or cloud-native storage platforms.

At the core, a Data Asset in GE represents a concrete set of data subject to validation. These data assets can be tables, files, streams, or any structured data repository. The abstraction begins by defining logical representations of these assets irrespective of their physical footprint. Internally, data assets are modeled as instances of classes deriving from the base Dataset or Batch constructs, which encapsulate the behavior and metadata necessary for validation.

Data Connectors serve as the pivotal mechanism by which GE discovers and maps data assets to in-code entities. They function as configuration-driven adapters, responsible for enumerating available data, organizing them into batches, and constructing the corresponding execution context. Great Expectations supports several Data Connector paradigms, each suited for different data source types:

• Filesystem Data Connectors: Designed to enumerate...