{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"great-expectations","slug":"great-expectations","name":"Great Expectations","type":"framework","url":"https://github.com/great-expectations/great_expectations","page_url":"https://unfragile.ai/great-expectations","categories":["data-pipelines","testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"great-expectations__cap_0","uri":"capability://data.processing.analysis.declarative.expectation.definition.with.fluent.api","name":"declarative expectation definition with fluent api","description":"Enables data teams to define data quality rules declaratively using a fluent Python API that chains expectation methods (e.g., expect_column_values_to_be_in_set, expect_table_row_count_to_be_between). Expectations are serialized as JSON and stored in ExpectationSuite objects, allowing version control and reuse across validation runs. The system supports 50+ built-in expectation types covering schema, distribution, and custom metrics.","intents":["Define reusable data quality rules without writing custom validation code","Document data contracts in a machine-readable format for team collaboration","Version control expectations alongside data pipeline code","Create parameterized expectations that adapt to different data sources"],"best_for":["data engineers building automated data pipelines","analytics teams establishing data governance standards","ML teams ensuring training data quality before model ingestion"],"limitations":["Custom expectations require subclassing ExpectationBase and implementing metric providers — no low-code custom rule builder","Expectation evaluation is row-by-row for some types, causing O(n) performance on large datasets without sampling","No built-in support for temporal or cross-dataset expectations (e.g., 'column X should grow by 5% week-over-week')"],"requires":["Python 3.8+","Pandas, SQLAlchemy, or Spark DataFrame as data source","DataContext initialized with configuration"],"input_types":["Pandas DataFrame","SQL query result","Spark DataFrame","CSV/Parquet files"],"output_types":["ExpectationSuite (JSON serializable)","Validation result with pass/fail status"],"categories":["data-processing-analysis","data-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"great-expectations__cap_1","uri":"capability://data.processing.analysis.multi.engine.validation.execution.with.metric.providers","name":"multi-engine validation execution with metric providers","description":"Executes expectations against data using pluggable execution engines (Pandas, SQL, Spark, Databricks) by translating expectation definitions into engine-specific queries through a Metric Provider system. Each expectation maps to metrics (e.g., column_values, table_row_count) that are computed differently per engine — SQL expectations compile to WHERE clauses, Pandas uses vectorized operations, Spark uses DataFrame API. The Validator class orchestrates metric computation and result aggregation.","intents":["Run the same expectations against data in different systems (Snowflake, PostgreSQL, Spark) without rewriting validation logic","Validate large datasets efficiently by pushing computation to the database instead of pulling data to Python","Support heterogeneous data stacks where validation must work across multiple data sources"],"best_for":["teams with multi-warehouse architectures (Snowflake + Spark + PostgreSQL)","organizations validating petabyte-scale datasets where pulling to Python is infeasible","data platforms needing engine-agnostic validation logic"],"limitations":["Custom metrics require implementing MetricProvider subclass for each engine — no automatic transpilation","SQL-based validation has ~500ms-2s overhead per expectation due to query compilation and network latency","Spark execution requires cluster availability and may not optimize for small datasets (overhead > benefit)"],"requires":["Python 3.8+","SQLAlchemy for SQL datasources (with dialect-specific drivers: psycopg2, pymysql, snowflake-sqlalchemy)","PySpark 3.0+ for Spark execution","Appropriate database credentials and network access"],"input_types":["SQL connection string","Spark DataFrame","Pandas DataFrame","Batch object (reference to data asset)"],"output_types":["ValidationResult with metrics, success flag, and result details","Structured exception report with row counts and sample failures"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"great-expectations__cap_10","uri":"capability://automation.workflow.gx.cloud.integration.with.centralized.validation.management","name":"gx cloud integration with centralized validation management","description":"Provides cloud-hosted validation management through GX Cloud, which centralizes expectations, validation runs, and data quality insights across teams. GX Cloud agents run validation checkpoints on schedule and report results to the cloud backend, enabling web-based dashboards, team collaboration, and audit trails. The cloud platform supports role-based access control, validation scheduling, and integration with data sources (Snowflake, Redshift, Databricks) without requiring local infrastructure.","intents":["Manage data quality across teams without maintaining local GX infrastructure","Schedule and monitor validation runs from a web dashboard","Collaborate on expectations and validation results with team members"],"best_for":["organizations wanting managed data quality without infrastructure overhead","teams needing web-based dashboards for data quality monitoring","enterprises requiring centralized audit trails and access control"],"limitations":["GX Cloud is a paid service — no free tier for production use","Cloud agents require network connectivity to GX Cloud backend — not suitable for air-gapped environments","Custom expectations and actions require deploying code to cloud agents — no local development workflow"],"requires":["GX Cloud account and API credentials","GX Cloud agent deployed in your infrastructure or cloud account","Network connectivity from agent to GX Cloud backend"],"input_types":["Expectations defined in GX Cloud UI or via API","Data source connections (Snowflake, Redshift, Databricks, etc.)"],"output_types":["Validation results displayed in GX Cloud dashboard","Audit logs of validation runs and configuration changes","Team collaboration on expectations and results"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"great-expectations__cap_11","uri":"capability://data.processing.analysis.custom.metric.provider.system.for.domain.specific.validation","name":"custom metric provider system for domain-specific validation","description":"Enables teams to define custom metrics by subclassing MetricProvider and implementing compute methods for each execution engine (Pandas, SQL, Spark). Custom metrics are registered with the MetricProvider registry and can be used in expectations without modifying core GX code. The system supports metric parameters (e.g., 'column_name', 'threshold') and caching of metric results to avoid redundant computation.","intents":["Define domain-specific data quality metrics (e.g., 'revenue_anomaly_score') not covered by built-in expectations","Implement custom validation logic that requires complex computation or external API calls","Reuse metric definitions across multiple expectations"],"best_for":["data teams with domain-specific quality requirements (e.g., financial data, healthcare data)","organizations implementing custom anomaly detection or statistical tests","teams integrating external quality scoring systems (e.g., data profiling tools)"],"limitations":["Custom metrics require implementing compute methods for each execution engine — no automatic transpilation","Metric caching is in-memory only — no distributed caching for Spark clusters","Debugging custom metrics is difficult because errors occur during validation execution, not metric definition"],"requires":["Python 3.8+","Understanding of MetricProvider base class and execution engine APIs","Knowledge of Pandas, SQL, and/or Spark APIs depending on target engines"],"input_types":["Data from Pandas DataFrame, SQL query, or Spark DataFrame","Metric parameters (column names, thresholds, etc.)"],"output_types":["Metric result (scalar value, list, or dictionary)","Metric metadata (data type, description)"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"great-expectations__cap_2","uri":"capability://data.processing.analysis.automated.data.profiling.with.rule.based.profiler","name":"automated data profiling with rule-based profiler","description":"Generates ExpectationSuites automatically by analyzing data distributions using the Rule-Based Profiler, which applies heuristic rules to infer expectations (e.g., 'if a column has <10 unique values, expect values to be in set'). The profiler computes statistical metrics (cardinality, nullness, data types, value ranges) and applies configurable rules to suggest expectations. Results are stored as ExpectationSuites that can be reviewed, edited, and deployed without manual definition.","intents":["Bootstrap data quality rules for new data sources without manual expectation writing","Discover data quality issues by comparing profiled distributions across time periods","Accelerate expectation creation for teams unfamiliar with data quality frameworks"],"best_for":["data teams onboarding new data sources and needing quick baseline expectations","organizations establishing data quality baselines for legacy systems","non-technical stakeholders who need data quality insights without writing validation code"],"limitations":["Profiler rules are heuristic-based and may generate false positives (e.g., suggesting cardinality bounds that are too strict)","Profiling large datasets (>1GB) requires sampling, which may miss edge cases in tail distributions","Generated expectations require manual review and tuning — no automatic feedback loop to refine rules based on validation failures"],"requires":["Python 3.8+","Data source with sufficient sample size (>100 rows recommended)","Compute resources for statistical analysis (CPU-bound for Pandas, network for SQL)"],"input_types":["Pandas DataFrame","SQL table","Spark DataFrame","Batch reference"],"output_types":["ExpectationSuite with auto-generated expectations","Profiling report with statistical summaries","Rule application log showing which rules fired"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"great-expectations__cap_3","uri":"capability://automation.workflow.checkpoint.based.validation.orchestration.with.scheduling","name":"checkpoint-based validation orchestration with scheduling","description":"Organizes validation runs into Checkpoints, which bundle a set of ExpectationSuites, data assets, and validation actions (e.g., send alert, update metadata) into a single executable unit. Checkpoints can be scheduled via Airflow, Prefect, or cron, and support conditional actions based on validation results (e.g., 'if validation fails, trigger PagerDuty alert'). The Checkpoint system stores validation history and provides a unified interface for monitoring data quality across pipelines.","intents":["Schedule recurring data quality checks at specific pipeline stages (post-ingestion, pre-ML)","Trigger downstream actions (alerts, quarantine, retry) based on validation outcomes","Track validation history and trends to identify systemic data quality issues"],"best_for":["data engineers integrating data quality into orchestration platforms (Airflow, Prefect, Dagster)","teams needing automated alerting when data quality degrades","organizations building data quality SLOs and monitoring dashboards"],"limitations":["Checkpoint configuration is YAML-based, requiring manual editing for complex conditional logic — no visual workflow builder","Action execution is synchronous, blocking the checkpoint until all actions complete (no async action support)","Scheduling requires external orchestrator (Airflow, Prefect) — no built-in scheduler for standalone deployments"],"requires":["Python 3.8+","DataContext with configured stores and data sources","Orchestration platform (Airflow, Prefect, Dagster) or cron for scheduling","Action handlers configured (e.g., email, Slack, webhook)"],"input_types":["Checkpoint YAML configuration","ExpectationSuite references","Data asset batch identifiers"],"output_types":["CheckpointResult with validation outcomes","Action execution logs","Validation history stored in metadata store"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"great-expectations__cap_4","uri":"capability://memory.knowledge.data.context.system.with.pluggable.store.backends","name":"data context system with pluggable store backends","description":"Provides a DataContext abstraction that manages configuration, expectations, validation results, and metadata through pluggable store backends (FileSystemStore, S3Store, DatabaseStore, GCSStore). The context system supports both file-based (YAML config) and cloud-based (GX Cloud) deployments, with stores handling persistence of expectations, validation results, and data docs. Stores are backend-agnostic, allowing teams to swap storage without changing application code.","intents":["Centralize data quality configuration and validation history in a single context","Store expectations and validation results in cloud storage (S3, GCS) for team collaboration","Migrate validation infrastructure from local files to cloud without code changes"],"best_for":["teams building shared data quality platforms across multiple projects","organizations requiring centralized validation history and audit trails","teams migrating from local file-based validation to cloud-native architectures"],"limitations":["FileSystemStore is not suitable for multi-user concurrent access — requires external locking or cloud store","Store configuration is verbose (requires specifying backend type, credentials, paths) — no auto-detection","Switching store backends requires re-initializing context and migrating existing validation history"],"requires":["Python 3.8+","DataContext configuration file (great_expectations.yml)","Cloud credentials if using S3/GCS stores (AWS_ACCESS_KEY_ID, GCS_PROJECT_ID, etc.)","Database connection string if using DatabaseStore"],"input_types":["YAML configuration file","Store backend credentials","Data source connection strings"],"output_types":["DataContext object with configured stores","Expectations, validation results, and metadata persisted to backend"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"great-expectations__cap_5","uri":"capability://text.generation.language.automated.data.docs.generation.with.customizable.renderers","name":"automated data docs generation with customizable renderers","description":"Generates HTML documentation of expectations, validation results, and data quality metrics using a Site Builder that composes Page Renderers for different content types (ExpectationSuite pages, validation result pages, data asset pages). Renderers transform ExpectationSuite and ValidationResult objects into HTML using Jinja2 templates, with support for custom CSS and JavaScript. Data Docs are published to FileSystem, S3, or GCS and can be embedded in data catalogs or served as standalone sites.","intents":["Auto-generate living documentation of data quality rules and validation history","Share data quality insights with non-technical stakeholders through interactive HTML reports","Embed data quality metadata into data catalogs (Collibra, Alation) via Data Docs API"],"best_for":["data teams needing to communicate data quality status to business stakeholders","organizations building data catalogs with embedded quality metrics","teams documenting data contracts for cross-functional collaboration"],"limitations":["Data Docs generation is static HTML — no real-time updates without regeneration","Custom renderers require Jinja2 template knowledge — no visual template builder","Large validation histories (>10k runs) cause slow Data Docs generation (>30s) due to HTML file count"],"requires":["Python 3.8+","DataContext with configured stores","Jinja2 for custom templates (optional)","Cloud storage credentials if publishing to S3/GCS"],"input_types":["ExpectationSuite objects","ValidationResult objects","Data asset metadata"],"output_types":["HTML site with expectation documentation","Validation result pages with pass/fail details","Data asset overview pages with quality metrics"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"great-expectations__cap_6","uri":"capability://tool.use.integration.validation.action.system.with.pluggable.handlers","name":"validation action system with pluggable handlers","description":"Executes actions (email, Slack, webhook, update metadata) based on validation outcomes through a pluggable ValidationAction system. Actions are triggered after checkpoint validation completes and receive ValidationResult objects, enabling conditional logic (e.g., 'send alert only if validation failed'). Built-in actions include EmailAction, SlackNotificationAction, UpdateDataDocsAction, and custom actions can be implemented by subclassing ValidationAction.","intents":["Send automated alerts to teams when data quality issues are detected","Update metadata systems (data catalogs, lineage tools) with validation results","Trigger remediation workflows (quarantine data, retry pipeline) based on validation failures"],"best_for":["teams needing real-time alerting for data quality issues","organizations integrating data quality into incident response workflows","teams updating data catalogs with quality metrics automatically"],"limitations":["Action execution is synchronous and blocking — if an action fails, checkpoint execution halts","No built-in retry logic for failed actions (e.g., if Slack API is down, alert is lost)","Custom actions require Python code — no low-code action builder for non-developers"],"requires":["Python 3.8+","Checkpoint configuration with action definitions","External service credentials (Slack token, email server, webhook URL)"],"input_types":["ValidationResult object","Checkpoint configuration"],"output_types":["Action execution logs","External notifications (Slack message, email, webhook POST)","Updated metadata in external systems"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"great-expectations__cap_7","uri":"capability://data.processing.analysis.batch.system.for.data.asset.versioning.and.lineage","name":"batch system for data asset versioning and lineage","description":"Organizes data into Batches, which represent immutable snapshots of data assets at specific points in time, enabling validation of specific data versions and tracking of data lineage. Batches are identified by batch_id (e.g., 'daily_2024-01-15') and store metadata (creation time, data source, asset name) in the metadata store. The Batch system integrates with DataSources to enable automatic batch discovery and supports manual batch creation for ad-hoc validation.","intents":["Validate specific data snapshots (e.g., daily partitions) rather than entire datasets","Track which data version was validated and when, enabling audit trails","Correlate validation results with data lineage to identify root causes of quality issues"],"best_for":["teams with partitioned data (daily, hourly) needing per-partition validation","organizations requiring audit trails of which data versions were validated","data platforms tracking data lineage and quality metrics together"],"limitations":["Batch discovery requires DataSource configuration — manual batch creation is verbose","Batch metadata is stored separately from validation results, requiring joins to correlate data","No built-in support for batch retention policies — old batches must be manually cleaned up"],"requires":["Python 3.8+","DataSource configured with batch discovery logic","Metadata store for persisting batch metadata"],"input_types":["Data asset reference","Batch identifier (batch_id)","Batch metadata (creation time, partition key)"],"output_types":["Batch object with metadata","Validation results linked to batch_id","Batch lineage information"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"great-expectations__cap_8","uri":"capability://automation.workflow.fluent.datasource.api.for.dynamic.data.source.configuration","name":"fluent datasource api for dynamic data source configuration","description":"Provides a fluent Python API for configuring data sources dynamically without YAML, enabling programmatic creation of SQL datasources, Pandas datasources, and Spark datasources with batch discovery rules. The API supports method chaining (e.g., datasource.add_table_asset(...).add_batch_definition(...)) and generates batch identifiers automatically based on partition keys or file paths. Datasources are stored in the DataContext and can be referenced by name in expectations and checkpoints.","intents":["Configure data sources programmatically without writing YAML configuration","Define batch discovery rules (e.g., 'partition by date') to automatically identify data versions","Support dynamic data source creation for multi-tenant or parameterized pipelines"],"best_for":["teams building data quality as code with programmatic configuration","organizations with dynamic data sources (e.g., parameterized by customer ID)","developers preferring Python APIs over YAML configuration"],"limitations":["Fluent API is Python-only — no support for other languages or declarative formats","Batch discovery rules are limited to simple patterns (date partitions, file paths) — complex discovery requires custom code","Datasource configuration is not persisted by default — requires explicit save() call to store in context"],"requires":["Python 3.8+","DataContext initialized","Database credentials or file system access for data sources"],"input_types":["SQL connection string or Pandas/Spark DataFrame","Batch discovery configuration (partition key, file pattern)"],"output_types":["Datasource object with configured assets and batch definitions","Batch identifiers generated from partition keys or file paths"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"great-expectations__cap_9","uri":"capability://memory.knowledge.validation.result.storage.and.querying.with.metadata.store","name":"validation result storage and querying with metadata store","description":"Persists validation results (pass/fail status, metrics, exception details) to a metadata store (FileSystem, S3, database) and provides query APIs to retrieve results by batch, expectation, or time range. ValidationResult objects are serialized to JSON and indexed by batch_id, expectation_suite_name, and run_id, enabling efficient retrieval of validation history. The metadata store supports filtering and aggregation queries for trend analysis and SLO monitoring.","intents":["Query validation history to identify recurring data quality issues","Track validation trends over time to measure data quality improvements","Build dashboards and alerts based on validation result aggregations"],"best_for":["teams building data quality dashboards and monitoring systems","organizations tracking data quality SLOs and KPIs","teams analyzing validation trends to identify systemic issues"],"limitations":["Metadata store query APIs are limited to simple filters — complex analytics require exporting to data warehouse","FileSystemStore is not suitable for high-volume validation result storage (>100k results) due to file system overhead","No built-in result retention policies — old results must be manually archived or deleted"],"requires":["Python 3.8+","Metadata store configured (FileSystem, S3, or database)","Sufficient storage capacity for validation result history"],"input_types":["ValidationResult objects","Query filters (batch_id, expectation_suite_name, time range)"],"output_types":["ValidationResult objects retrieved from store","Aggregated metrics (pass rate, failure count by expectation)","Validation history timeline"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"great-expectations__headline","uri":"capability://data.processing.analysis.open.source.data.quality.framework","name":"open-source data quality framework","description":"Great Expectations is an open-source data quality framework that helps teams validate, document, and profile their data through declarative expectations, ensuring data integrity before it impacts machine learning models.","intents":["best data quality framework","data validation tool for machine learning","open-source data profiling solution","automated data quality testing for pipelines","data documentation framework for 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