{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-great-expectations","slug":"pypi-great-expectations","name":"great-expectations","type":"repo","url":"https://greatexpectations.io","page_url":"https://unfragile.ai/pypi-great-expectations","categories":["data-analysis"],"tags":["data","science","testing","pipeline","data","quality","dataquality","validation","datavalidation"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-great-expectations__cap_0","uri":"capability://data.processing.analysis.declarative.data.quality.test.authoring.in.python","name":"declarative data quality test authoring in python","description":"Enables developers to write data quality tests as Python code using an Expectation-based DSL that encodes business logic and data contracts. Tests are expressed declaratively (e.g., 'column X must be non-null', 'values in column Y must be between 0-100') and compiled into executable validation rules that can be versioned, shared, and integrated into CI/CD pipelines. The framework abstracts away the complexity of implementing custom validation logic by providing a library of pre-built Expectation types covering common data quality patterns.","intents":["Write reusable data quality tests that reflect business requirements without custom SQL or Python validation code","Version control data quality rules alongside application code in Git","Share data contracts with non-technical stakeholders in human-readable format","Catch data quality issues early in development before they propagate downstream"],"best_for":["data engineers building data pipelines who want to shift quality testing left","teams adopting data contracts and schema-driven development","organizations standardizing data quality practices across multiple pipelines"],"limitations":["Requires Python knowledge to write and maintain tests; no low-code UI for test authoring in open-source version","Test execution performance depends on data volume and complexity of Expectation logic","Custom Expectations require Python development; not all validation patterns may be pre-built"],"requires":["Python 3.7+ runtime","Great Expectations GX Core library (open-source) or GX Cloud account","Access to data source (database, data warehouse, file system, or data lake)"],"input_types":["Python code (Expectation definitions)","Data from connected sources (SQL databases, Pandas DataFrames, Spark, Snowflake, BigQuery, etc.)"],"output_types":["Validation results (pass/fail per Expectation)","Structured JSON/YAML reports","HTML documentation and data docs"],"categories":["data-processing-analysis","testing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-great-expectations__cap_1","uri":"capability://automation.workflow.multi.stage.data.pipeline.validation.with.checkpoint.orchestration","name":"multi-stage data pipeline validation with checkpoint orchestration","description":"Provides a Checkpoint abstraction that bundles multiple Expectations and executes them at defined stages in a data pipeline (development, pre-downstream, production). Checkpoints can be triggered manually, on-schedule, or integrated into orchestration tools (Airflow, dbt, Prefect) to validate data at ingestion, transformation, and output stages. Results are collected and can trigger alerts, block downstream processing, or log to monitoring systems. The framework supports conditional validation logic and parameterized Expectations to adapt tests to different data contexts.","intents":["Validate data at multiple pipeline stages without duplicating test logic","Integrate data quality checks into existing orchestration workflows (Airflow DAGs, dbt models, Spark jobs)","Block bad data from moving downstream and alert teams when quality thresholds are breached","Track data quality metrics over time and correlate failures with pipeline changes"],"best_for":["data platform teams managing multi-stage ETL/ELT pipelines","organizations with mature data infrastructure using Airflow, dbt, or Spark","teams needing production-grade data quality monitoring with alerting"],"limitations":["Checkpoint execution adds latency to pipeline runs; no built-in optimization for large-scale distributed validation","Requires integration code to connect Checkpoints to orchestration tools; not all orchestrators have native connectors","State management and result persistence require external storage (database, cloud object store); GX Core has no built-in state backend"],"requires":["Python 3.7+ runtime","Great Expectations GX Core or GX Cloud","Data orchestration tool (Airflow, dbt, Prefect, Dagster) or custom scheduling mechanism","Persistent storage for Checkpoint definitions and validation results"],"input_types":["Checkpoint configuration (JSON/YAML)","Expectation Suite definitions","Data from pipeline stages (batch or streaming)"],"output_types":["Validation reports (structured JSON)","Alerts/notifications (email, Slack, webhooks)","Metrics and metadata for monitoring dashboards"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-great-expectations__cap_10","uri":"capability://code.generation.editing.custom.expectation.development.and.extension.framework","name":"custom expectation development and extension framework","description":"Great Expectations provides a framework for developing custom Expectations that extend the built-in library with domain-specific validation logic. Custom Expectations are implemented as Python classes that inherit from base Expectation classes and implement validation logic, rendering logic, and metadata. The framework handles execution, result collection, and integration with the standard validation pipeline. Custom Expectations can be packaged as plugins and shared across teams or published to the community. The framework supports custom Expectation validation, documentation generation, and testing utilities.","intents":["Implement domain-specific data quality rules not covered by built-in Expectations","Extend Great Expectations with custom validation logic for proprietary data formats or business logic","Package and share custom Expectations across teams or publish to the community","Integrate third-party validation libraries or custom algorithms into Great Expectations"],"best_for":["data teams with specialized validation requirements beyond built-in Expectations","organizations building data quality platforms on top of Great Expectations","teams wanting to standardize custom validation logic across multiple pipelines"],"limitations":["Custom Expectation development requires Python expertise and understanding of Great Expectations internals","Custom Expectations must be tested and maintained; no automatic compatibility with new GX versions","Performance of custom Expectations depends on implementation; poorly written code can slow validation","Documentation and examples for custom Expectation development are limited"],"requires":["Python 3.7+ runtime","Great Expectations GX Core or GX Cloud","Python development knowledge and familiarity with Great Expectations API","Testing framework (pytest) for validating custom Expectations"],"input_types":["Python code implementing custom Expectation class","Validation logic and metadata","Test cases for custom Expectation"],"output_types":["Custom Expectation class (Python module)","Validation results (pass/fail with diagnostic data)","Documentation and metadata for custom Expectation"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-great-expectations__cap_2","uri":"capability://data.processing.analysis.automated.test.generation.via.expectai","name":"automated test generation via expectai","description":"Provides an AI-assisted test generation feature (ExpectAI) that analyzes sample data and automatically generates Expectation Suites reflecting observed data patterns and statistical properties. The system infers constraints on column types, value ranges, null rates, and distributions, then suggests Expectations that encode these patterns. Generated tests can be reviewed, edited, and committed to version control. This reduces manual effort in bootstrapping data quality tests for new data sources or tables.","intents":["Quickly bootstrap data quality tests for new data sources without manual Expectation authoring","Discover implicit data contracts by analyzing historical data patterns","Generate baseline tests that can be refined and customized for specific business logic","Reduce time-to-value for teams adopting data quality practices"],"best_for":["teams onboarding new data sources and needing rapid test coverage","data governance initiatives requiring baseline quality metrics across many tables","organizations with limited data engineering resources for manual test authoring"],"limitations":["Generated tests reflect historical patterns, not business logic; require manual review and customization","May generate overly permissive tests if data contains anomalies or edge cases not representative of normal state","ExpectAI mechanism and training data are not documented; unclear how it handles different data types and distributions","Feature availability and capabilities differ between GX Core (open-source) and GX Cloud (SaaS)"],"requires":["Python 3.7+ runtime","Great Expectations GX Core or GX Cloud account","Sample data or data source connection for analysis","GX Cloud subscription may be required for ExpectAI feature (pricing tier dependent)"],"input_types":["Data sample or data source connection","Table/dataset metadata"],"output_types":["Generated Expectation Suite (JSON/YAML)","Suggested Expectations with confidence scores","Human-readable test descriptions"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-great-expectations__cap_3","uri":"capability://data.processing.analysis.structured.validation.result.reporting.and.data.docs.generation","name":"structured validation result reporting and data docs generation","description":"Executes Expectations and produces structured validation results (JSON/YAML) containing pass/fail status, failure counts, and diagnostic metadata for each Expectation. Results are aggregated into Validation Reports that can be rendered as HTML Data Docs—human-readable documentation showing data quality metrics, test results, and data lineage. Data Docs are versioned and can be hosted on static web servers or integrated into data catalogs. Results can also be exported to monitoring systems, data warehouses, or custom dashboards for real-time quality tracking.","intents":["Generate human-readable data quality reports for non-technical stakeholders","Create living documentation of data quality expectations and validation history","Export validation metrics to monitoring/observability platforms (Datadog, New Relic, Grafana)","Track data quality trends over time and correlate failures with upstream changes"],"best_for":["data teams needing to communicate quality status to business stakeholders","organizations building data catalogs or data governance platforms","teams integrating data quality metrics into observability/monitoring stacks"],"limitations":["Data Docs are static HTML; real-time dashboards require integration with external BI tools or custom development","Result storage and versioning require external persistence layer; GX Core does not include built-in result database","HTML Data Docs generation can be slow for large Expectation Suites with many validation runs","No built-in access control or role-based visibility for Data Docs in open-source version"],"requires":["Python 3.7+ runtime","Great Expectations GX Core or GX Cloud","Web server or static hosting for Data Docs (GitHub Pages, S3, etc.)","Optional: monitoring/observability platform for metric export"],"input_types":["Validation results (from Checkpoint execution)","Expectation Suite metadata","Historical validation runs"],"output_types":["Structured JSON/YAML validation reports","HTML Data Docs (static website)","Metrics for export to monitoring systems","CSV/Parquet for analysis in BI tools"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-great-expectations__cap_4","uri":"capability://data.processing.analysis.connector.based.data.source.abstraction.and.execution","name":"connector-based data source abstraction and execution","description":"Abstracts data source connectivity through a connector pattern, enabling Expectations to be executed against multiple data sources (SQL databases, Pandas DataFrames, Spark, Snowflake, BigQuery, Redshift, etc.) without changing test code. Connectors handle data fetching, query translation, and result collection. The framework supports both batch validation (full table scans) and sampling-based validation for large datasets. Connectors are extensible; custom connectors can be implemented for proprietary data systems.","intents":["Write data quality tests once and execute them against multiple data sources (dev database, production warehouse, data lake)","Validate data in-place without copying to a separate validation engine","Optimize validation performance through sampling or incremental validation strategies","Support heterogeneous data stacks with multiple database systems and data formats"],"best_for":["organizations with multi-source data architectures (on-prem databases, cloud data warehouses, data lakes)","teams needing to validate data where it lives without ETL to a central system","data platforms supporting diverse data sources and requiring unified quality checks"],"limitations":["Connector quality and feature completeness vary; some data sources may have limited Expectation support","Query translation and execution performance depends on data source capabilities; some Expectations may be slow on certain systems","Sampling-based validation reduces coverage; full-table validation can be prohibitively slow for very large datasets","Custom connector development requires Python and understanding of data source APIs"],"requires":["Python 3.7+ runtime","Great Expectations GX Core or GX Cloud","Connection credentials and network access to target data source","Supported data source (SQL database, Pandas, Spark, Snowflake, BigQuery, Redshift, etc.)","Optional: custom connector implementation for unsupported data sources"],"input_types":["Data source connection configuration (host, credentials, database name)","Table/dataset identifiers","Expectation Suite definitions"],"output_types":["Validation results (pass/fail per Expectation)","Failure samples and diagnostic data","Execution metadata (rows scanned, query time, etc.)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-great-expectations__cap_5","uri":"capability://automation.workflow.cloud.based.saas.validation.platform.with.managed.infrastructure","name":"cloud-based saas validation platform with managed infrastructure","description":"GX Cloud provides a fully-managed SaaS platform that eliminates the need to self-host and manage Great Expectations infrastructure. The platform includes a web-based UI for test authoring, a managed validation execution engine, result storage, and Data Docs hosting. Teams can set up validation in minutes without deploying Python code or managing databases. GX Cloud includes features like ExpectAI, real-time monitoring dashboards, team collaboration tools, and integrations with data orchestration platforms. Pricing tiers (Developer free, Team, Enterprise) support different team sizes and feature sets.","intents":["Set up data quality validation without managing infrastructure or writing Python code","Enable non-technical stakeholders to author and monitor data quality tests via web UI","Centralize validation results and data quality metrics across multiple pipelines and teams","Integrate data quality into existing data stacks with pre-built connectors and webhooks"],"best_for":["small teams and startups needing rapid data quality setup without DevOps overhead","organizations with non-technical data stewards who need UI-based test authoring","enterprises requiring managed infrastructure, compliance, and support SLAs","teams already using cloud data warehouses (Snowflake, BigQuery, Redshift) and wanting integrated quality monitoring"],"limitations":["SaaS pricing scales with usage; may be expensive for very large-scale validation (millions of rows, thousands of Expectations)","Data must be accessible from GX Cloud (cloud data warehouses or public APIs); on-premises data requires VPN/proxy setup","Vendor lock-in; migrating from GX Cloud to self-hosted GX Core requires exporting configurations and re-implementing integrations","Feature set and API stability may change with platform updates; no guarantee of backward compatibility","Data residency and compliance requirements may restrict use in regulated industries"],"requires":["GX Cloud account (free Developer tier or paid Team/Enterprise subscription)","Cloud data warehouse or accessible data source (Snowflake, BigQuery, Redshift, Postgres, etc.)","Web browser for UI access","Optional: API key for programmatic access and integrations"],"input_types":["Data source connection configuration (via web UI)","Expectation definitions (via UI or API)","Webhook triggers from orchestration tools"],"output_types":["Validation results (web dashboard, API, webhooks)","Alerts and notifications (email, Slack, custom webhooks)","Data Docs (hosted on GX Cloud)","Metrics for export to monitoring systems"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-great-expectations__cap_6","uri":"capability://data.processing.analysis.data.source.agnostic.expectation.suite.versioning.and.configuration.management","name":"data source-agnostic expectation suite versioning and configuration management","description":"Expectation Suites are stored as JSON/YAML configuration files that can be versioned in Git, enabling data quality tests to be treated as code. Suites are decoupled from specific data sources, allowing the same suite to be executed against different tables or databases without modification. Configuration management supports parameterization (e.g., table name, column names, thresholds) enabling test reuse across similar datasets. Suites can be organized hierarchically and shared across teams. The framework supports suite validation, merging, and conflict resolution for collaborative workflows.","intents":["Version control data quality tests alongside application code in Git","Reuse Expectation Suites across similar datasets with parameterization","Collaborate on test definitions with code review workflows (pull requests, approvals)","Track changes to data quality rules and correlate with data quality incidents"],"best_for":["teams using Git-based workflows and wanting to treat data quality as code","organizations with many similar datasets (e.g., tables with same schema in different environments)","data platforms supporting infrastructure-as-code practices"],"limitations":["Parameterization is limited; complex conditional logic requires custom Python code","No built-in conflict resolution for concurrent edits to Expectation Suites; requires manual merging","Suite validation and testing require running Expectations; no static analysis of suite correctness","Large Expectation Suites (thousands of Expectations) can be slow to load and validate"],"requires":["Python 3.7+ runtime","Great Expectations GX Core or GX Cloud","Git repository for version control","YAML/JSON editor for manual suite editing (optional; GX Cloud provides web UI)"],"input_types":["Expectation Suite definitions (JSON/YAML)","Parameterization values (environment variables, config files)","Data source connection info"],"output_types":["Versioned Expectation Suite files (JSON/YAML)","Suite metadata and documentation","Validation results per suite version"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-great-expectations__cap_7","uri":"capability://automation.workflow.integration.with.data.orchestration.platforms.and.ci.cd.pipelines","name":"integration with data orchestration platforms and ci/cd pipelines","description":"Provides native or community-supported integrations with popular data orchestration tools (Airflow, dbt, Prefect, Dagster) and CI/CD systems (GitHub Actions, GitLab CI, Jenkins). Integrations enable Checkpoints to be triggered as pipeline steps, with results blocking downstream tasks on failure or logging to pipeline metadata. GX provides Airflow operators, dbt test adapters, and webhook-based triggers for other platforms. Results can be exported to orchestration logs, monitoring systems, or custom notification channels. Integration patterns support both synchronous (blocking) and asynchronous (non-blocking) validation modes.","intents":["Embed data quality checks into Airflow DAGs, dbt projects, or other orchestration workflows","Block bad data from moving downstream by failing pipeline tasks on validation failure","Integrate data quality metrics into pipeline observability and alerting systems","Trigger validation on data changes using CI/CD webhooks or event-based mechanisms"],"best_for":["data teams using Airflow, dbt, Prefect, or Dagster for pipeline orchestration","organizations with mature CI/CD practices wanting to extend them to data quality","teams needing to correlate data quality failures with pipeline changes and deployments"],"limitations":["Integration quality and feature completeness vary by orchestration tool; some may require custom code","Validation latency adds to pipeline runtime; no built-in optimization for large-scale distributed validation","Orchestration tool-specific knowledge required to implement integrations; not all patterns are documented","Result export to external systems requires custom code or webhooks; no universal integration pattern"],"requires":["Python 3.7+ runtime","Great Expectations GX Core or GX Cloud","Data orchestration tool (Airflow, dbt, Prefect, Dagster) or CI/CD system (GitHub Actions, GitLab CI, Jenkins)","Network connectivity between orchestration tool and GX (self-hosted or cloud)","Optional: custom integration code for unsupported orchestration tools"],"input_types":["Checkpoint definitions","Orchestration tool configuration (DAG, dbt project, pipeline definition)","Validation trigger events (scheduled, on-demand, webhook)"],"output_types":["Validation results (passed to orchestration logs)","Pipeline task status (success/failure based on validation)","Alerts and notifications (email, Slack, custom webhooks)","Metrics for export to monitoring systems"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-great-expectations__cap_8","uri":"capability://automation.workflow.real.time.data.quality.monitoring.and.alerting.in.gx.cloud","name":"real-time data quality monitoring and alerting in gx cloud","description":"GX Cloud provides real-time monitoring dashboards that track validation results, data quality metrics, and trends over time. Dashboards display pass/fail rates, failure counts, and historical patterns for each Expectation and Checkpoint. Alerting rules can be configured to trigger notifications (email, Slack, webhooks) when quality thresholds are breached or validation failures occur. Alerts support conditional logic (e.g., alert only if failure rate exceeds 10%) and can be routed to different teams based on data ownership. Monitoring data is retained for historical analysis and trend detection.","intents":["Monitor data quality metrics in real-time and detect anomalies quickly","Alert teams immediately when data quality degrades or validation fails","Track data quality trends over time and identify systemic issues","Correlate data quality failures with upstream changes and incidents"],"best_for":["organizations with production data pipelines requiring real-time quality monitoring","data teams needing to respond quickly to quality issues and customer impact","enterprises with SLAs requiring data quality guarantees and incident response"],"limitations":["Real-time monitoring requires continuous validation execution; may increase infrastructure costs","Alert fatigue risk if thresholds are not tuned carefully; requires domain expertise to set appropriate baselines","Monitoring data retention is limited by GX Cloud plan; historical analysis may require export to external systems","No built-in root cause analysis; teams must investigate failures manually or integrate with external tools"],"requires":["GX Cloud Team or Enterprise subscription (not available in free Developer tier)","Continuous validation execution (scheduled or event-triggered)","Integration with notification systems (email, Slack, webhooks) for alerting"],"input_types":["Validation results from Checkpoint execution","Alert rule configuration (thresholds, conditions, notification channels)","Historical validation data"],"output_types":["Real-time monitoring dashboards (web UI)","Alerts and notifications (email, Slack, webhooks)","Metrics and trends for analysis","Incident reports and audit logs"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-great-expectations__cap_9","uri":"capability://tool.use.integration.collaborative.team.workflows.and.role.based.access.control.in.gx.cloud","name":"collaborative team workflows and role-based access control in gx cloud","description":"GX Cloud provides team collaboration features including shared Expectation Suites, collaborative test authoring, and role-based access control (RBAC). Teams can assign roles (Admin, Editor, Viewer) to control who can create, edit, or view Expectations and validation results. Audit logs track changes to Expectations and validation configurations. Workspace organization enables teams to manage multiple data sources and pipelines within a single GX Cloud account. Notifications and mentions enable team communication around data quality issues.","intents":["Enable multiple team members to collaborate on data quality test authoring and maintenance","Control access to sensitive data quality information based on team roles and responsibilities","Track changes to Expectations and validation configurations for compliance and audit purposes","Organize validation across multiple teams and data sources within a single platform"],"best_for":["organizations with multiple data teams needing to collaborate on quality standards","enterprises with compliance requirements for audit trails and access control","large organizations with complex data governance structures and team hierarchies"],"limitations":["RBAC is limited to predefined roles; no custom role definitions","Audit logs may have retention limits depending on GX Cloud plan","Collaboration features are limited to GX Cloud; self-hosted GX Core does not include team management","No built-in workflow approval process for Expectation changes; requires external tools or manual processes"],"requires":["GX Cloud Team or Enterprise subscription","Team member accounts with email addresses","Workspace configuration and role assignments"],"input_types":["Team member invitations and role assignments","Expectation Suite definitions and changes","Validation configurations and Checkpoints"],"output_types":["Audit logs and change history","Team notifications and mentions","Access control policies and role assignments","Workspace organization and metadata"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+ runtime","Great Expectations GX Core library (open-source) or GX Cloud account","Access to data source (database, data warehouse, file system, or data lake)","Great Expectations GX Core or GX Cloud","Data orchestration tool (Airflow, dbt, Prefect, Dagster) or custom scheduling mechanism","Persistent storage for Checkpoint definitions and validation results","Python development knowledge and familiarity with Great Expectations API","Testing framework (pytest) for validating custom Expectations","Great Expectations GX Core or GX Cloud account","Sample data or data source connection for analysis"],"failure_modes":["Requires Python knowledge to write and maintain tests; no low-code UI for test authoring in open-source version","Test execution performance depends on data volume and complexity of Expectation logic","Custom Expectations require Python development; not all validation patterns may be pre-built","Checkpoint execution adds latency to pipeline runs; no built-in optimization for large-scale distributed validation","Requires integration code to connect Checkpoints to orchestration tools; not all orchestrators have native connectors","State management and result persistence require external storage (database, cloud object store); GX Core has no built-in state backend","Custom Expectation development requires Python expertise and understanding of Great Expectations internals","Custom Expectations must be tested and maintained; no automatic compatibility with new GX versions","Performance of custom Expectations depends on implementation; poorly written code can slow validation","Documentation and examples for custom Expectation development are limited","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.32,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:23.204Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pypi-great-expectations","compare_url":"https://unfragile.ai/compare?artifact=pypi-great-expectations"}},"signature":"tFlNYMnPV9Vi9QA4hqTbSGu7iDk6kQpvoNvTJLO/Q42C3k5sthcCH5/83KvHImTqaRzzyqdM7nl7TlS1eFr5Dg==","signedAt":"2026-06-23T17:30:53.183Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-great-expectations","artifact":"https://unfragile.ai/pypi-great-expectations","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-great-expectations","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}