{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"soda","slug":"soda","name":"Soda","type":"repo","url":"https://github.com/sodadata/soda-core","page_url":"https://unfragile.ai/soda","categories":["data-pipelines","testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"soda__cap_0","uri":"capability://data.processing.analysis.sodacl.domain.specific.language.parsing.and.compilation","name":"sodacl domain-specific language parsing and compilation","description":"Parses human-readable SodaCL YAML syntax into an abstract syntax tree (AST) that represents data quality checks, then compiles these checks into executable check objects. The parser uses a configuration-driven approach where SodaCL statements are tokenized, validated against a schema, and mapped to check type implementations. This enables non-technical users to define complex data quality rules without writing SQL directly.","intents":["Define data quality checks in a human-readable format without writing SQL","Validate check syntax and catch configuration errors before execution","Support multiple check types (metric, distribution, anomaly, freshness) through a unified language","Enable version control and collaboration on data quality definitions"],"best_for":["Data engineers building reusable quality check libraries","Analytics teams collaborating on data governance standards","Organizations migrating from ad-hoc SQL quality checks to standardized frameworks"],"limitations":["SodaCL syntax is proprietary and requires learning a new DSL — not portable to other tools","Complex conditional logic across multiple columns requires nested check definitions, reducing readability","No built-in support for dynamic check generation based on schema introspection — checks must be manually defined per column"],"requires":["Python 3.8+","Valid YAML syntax in SodaCL configuration files","Understanding of SodaCL check type syntax (metric, distribution, anomaly, freshness)"],"input_types":["YAML text (SodaCL configuration)","Check definitions with column names, thresholds, and check types"],"output_types":["Compiled check objects (Check class instances)","Validation errors and warnings","Check execution plan"],"categories":["data-processing-analysis","domain-specific-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"soda__cap_1","uri":"capability://data.processing.analysis.multi.dialect.sql.query.generation.and.execution","name":"multi-dialect sql query generation and execution","description":"Converts compiled SodaCL checks into dialect-specific SQL queries (PostgreSQL, Snowflake, BigQuery, Redshift, Spark, Athena) by routing through data source-specific adapter packages. Each adapter implements a QueryExecutor that translates generic check logic into optimized SQL for that database's syntax and functions, then executes the query and returns results as structured data. This abstraction enables the same check definition to run across heterogeneous data platforms.","intents":["Execute the same data quality checks across multiple databases without rewriting SQL","Leverage database-native functions for performance optimization (e.g., BigQuery's APPROX_QUANTILES vs Postgres PERCENTILE_CONT)","Support multi-cloud data architectures with unified quality monitoring","Handle dialect-specific syntax differences transparently"],"best_for":["Organizations with multi-cloud or hybrid data architectures","Teams managing data quality across Snowflake, BigQuery, Redshift, and on-premise databases","Data platforms requiring consistent quality checks regardless of underlying SQL dialect"],"limitations":["Query performance varies significantly by dialect — no automatic query optimization across platforms","Some advanced check types (anomaly detection with Prophet) only work with Spark/Pandas, not all SQL databases","Requires separate adapter package installation per data source (soda-core-snowflake, soda-core-bigquery, etc.) — no single unified package","Custom SQL checks must be written per dialect if using database-specific functions"],"requires":["Python 3.8+","soda-core base package","Data source-specific adapter package (e.g., soda-core-snowflake, soda-core-bigquery)","Valid database connection credentials and network access","Appropriate database permissions (SELECT on target tables, CREATE TEMP TABLE for sampling)"],"input_types":["Compiled check objects with metric/distribution/anomaly definitions","Data source configuration (connection string, credentials, schema name)","Column names and data types from target tables"],"output_types":["Query results as numeric values, row counts, or sampled rows","Execution metadata (query runtime, rows scanned)","Check pass/fail evaluation"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"soda__cap_10","uri":"capability://tool.use.integration.soda.cloud.integration.with.centralized.quality.monitoring","name":"soda cloud integration with centralized quality monitoring","description":"Integrates with Soda Cloud (SaaS platform) to upload scan results, enable centralized quality dashboards, configure alerts, and manage quality governance policies. The integration uses API credentials to authenticate with Soda Cloud, uploads scan results and check definitions, and enables cross-organization quality monitoring. Supports both push-based result uploads and pull-based scan scheduling from Soda Cloud.","intents":["Centralize data quality monitoring across multiple data sources and teams","Configure quality alerts and notifications based on check failures","Build organization-wide quality dashboards and reports","Implement data governance policies and quality SLAs"],"best_for":["Enterprise organizations requiring centralized quality governance","Teams managing quality across multiple data sources and environments","Organizations implementing data quality SLAs and compliance monitoring"],"limitations":["Soda Cloud integration requires paid SaaS subscription — not available in open-source Soda Core","Scan results are uploaded to external SaaS platform — requires network connectivity and data privacy considerations","Alert configuration is managed in Soda Cloud UI, not in SodaCL — creates split configuration management","No support for on-premise Soda Cloud deployment — only SaaS offering available"],"requires":["Python 3.8+","Soda Cloud account with API credentials","Network connectivity to Soda Cloud API endpoints","soda-core installation with cloud integration package"],"input_types":["Scan results from local Soda Core execution","Soda Cloud API credentials","Check definitions and metadata"],"output_types":["Uploaded scan results in Soda Cloud","Quality dashboards and reports","Alert notifications and incident tracking"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"soda__cap_11","uri":"capability://automation.workflow.cli.based.scan.execution.with.variable.substitution.and.output.formatting","name":"cli-based scan execution with variable substitution and output formatting","description":"Provides a command-line interface for executing scans with the `soda scan` command, supporting variable substitution, output format selection, and configuration overrides. The CLI parses command-line arguments, substitutes variables into SodaCL configurations, executes scans, and formats results as JSON, YAML, or text. Supports integration with CI/CD pipelines via exit codes and structured output formats.","intents":["Execute data quality scans from command-line or CI/CD pipelines","Pass runtime variables to scans (e.g., table names, thresholds)","Generate machine-readable scan results for downstream processing","Integrate Soda into orchestration platforms (Airflow, GitHub Actions, GitLab CI)"],"best_for":["Data engineers running scans from CI/CD pipelines","Teams integrating Soda into orchestration platforms","Organizations automating data quality checks in data pipelines"],"limitations":["CLI execution is synchronous — no built-in support for async execution or background jobs","Variable substitution is simple string replacement — no support for complex templating or conditional logic","Output formatting is limited to JSON, YAML, and text — no support for custom output formats","No built-in support for scan scheduling or cron-based execution"],"requires":["Python 3.8+","soda-core installation with CLI entry point","SodaCL configuration file","Data source configuration with connection credentials"],"input_types":["Command-line arguments (scan name, configuration file, variables)","SodaCL configuration files","Data source configuration"],"output_types":["Formatted scan results (JSON, YAML, text)","Exit codes (0 for success, non-zero for failures)","Console output with check results"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"soda__cap_12","uri":"capability://code.generation.editing.custom.check.extension.framework.with.pluggable.check.types","name":"custom check extension framework with pluggable check types","description":"Enables extension of Soda with custom check types by implementing a Check base class and registering custom check implementations. The framework allows users to define custom metrics, validation logic, and result evaluation without modifying core Soda code. Custom checks are registered in the check type registry and can be used in SodaCL alongside built-in check types, enabling domain-specific quality checks tailored to specific use cases.","intents":["Implement domain-specific data quality checks not covered by built-in check types","Extend Soda with custom metrics and validation logic","Build reusable check libraries for specific data domains","Integrate custom business logic into data quality monitoring"],"best_for":["Organizations with domain-specific data quality requirements","Teams building reusable quality check libraries","Advanced users implementing custom metrics and validation logic"],"limitations":["Custom check development requires Python programming — not accessible to non-technical users","Custom checks must be registered in code — no dynamic check registration from SodaCL","No built-in support for custom check distribution or package management","Custom checks may not be compatible across Soda versions — requires maintenance during upgrades"],"requires":["Python 3.8+","Understanding of Soda Check class architecture","Ability to implement custom SQL query generation and result evaluation","soda-core source code or development environment"],"input_types":["Custom Check class implementations","Check configuration from SodaCL","Data source and query execution context"],"output_types":["Custom check results and pass/fail evaluation","Custom metrics and validation outcomes"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"soda__cap_2","uri":"capability://data.processing.analysis.metric.based.data.quality.checks.with.threshold.evaluation","name":"metric-based data quality checks with threshold evaluation","description":"Executes metric checks that compute aggregate statistics (row count, missing values, duplicate count, valid values) over entire tables or column subsets, then evaluates results against user-defined thresholds (exact values, ranges, or percentage-based). The metric check system generates SQL aggregation queries, caches results, and compares them to threshold configurations to produce pass/fail outcomes. Supports both simple numeric thresholds and complex multi-condition rules.","intents":["Monitor table freshness by checking row count changes over time","Detect data quality degradation through missing value ratios and duplicate detection","Validate data completeness and validity with column-level metric checks","Set up automated alerts when metrics fall outside acceptable ranges"],"best_for":["Data engineers building continuous data quality monitoring pipelines","Analytics teams tracking data freshness and completeness SLAs","Organizations implementing data governance with quantifiable quality metrics"],"limitations":["Metric checks operate on aggregates only — cannot detect anomalies in specific row patterns without sampling","Threshold values must be manually configured — no automatic baseline learning or anomaly detection (requires soda-scientific package for Prophet-based anomaly detection)","Missing value detection counts NULLs only, not application-specific null representations (empty strings, -1, etc.)","No built-in support for time-series trend analysis or seasonal adjustment"],"requires":["Python 3.8+","Valid table and column names in target database","Numeric threshold values or range definitions in SodaCL","Database connection with SELECT permissions on target tables"],"input_types":["SodaCL metric check definitions (missing_count, duplicate_count, row_count, valid_count, etc.)","Threshold configurations (exact values, ranges, percentages)","Optional column filters and WHERE clauses"],"output_types":["Numeric metric values (counts, percentages)","Pass/fail evaluation against thresholds","Check execution metadata (query runtime, rows evaluated)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"soda__cap_3","uri":"capability://data.processing.analysis.distribution.based.data.quality.checks.with.reference.profiles","name":"distribution-based data quality checks with reference profiles","description":"Captures and validates the statistical distribution of column values by computing frequency distributions, quantiles, and value ranges, then comparing current distributions against stored reference profiles (DRO files). The system generates SQL queries to compute distribution statistics, stores them in YAML-based distribution reference objects, and detects distribution drift when current values deviate from historical baselines. Supports both automatic reference generation and manual threshold configuration.","intents":["Detect unexpected changes in categorical value distributions (e.g., new categories appearing)","Monitor numeric column distributions for outliers and range violations","Establish data quality baselines and track drift over time","Validate that data transformations preserve expected statistical properties"],"best_for":["Data teams monitoring data quality in production pipelines","Organizations implementing data contracts with distribution-based SLAs","Analytics teams detecting data quality regressions in ETL processes"],"limitations":["Distribution reference files (DRO) must be manually created or updated — no automatic baseline learning from historical data","Distribution checks are expensive for high-cardinality columns (e.g., user IDs) because they compute full frequency distributions","Drift detection uses simple statistical tests — no support for advanced anomaly detection methods (e.g., Kolmogorov-Smirnov test)","Reference profiles are stored in YAML files — no built-in versioning or time-series tracking of distribution changes"],"requires":["Python 3.8+","SodaCL distribution check definitions","Distribution reference object (DRO) YAML files for baseline comparisons","Database connection with SELECT permissions","Sufficient memory to compute and store full distributions for target columns"],"input_types":["SodaCL distribution check definitions (valid_values, invalid_values, missing_values)","Distribution reference YAML files (DRO format)","Column names and optional WHERE clause filters"],"output_types":["Distribution statistics (frequency counts, quantiles, value ranges)","Drift detection results (new values, missing values, out-of-range values)","Updated DRO files with new reference profiles"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"soda__cap_4","uri":"capability://data.processing.analysis.anomaly.detection.using.time.series.statistical.modeling","name":"anomaly detection using time-series statistical modeling","description":"Detects anomalies in numeric metrics by fitting time-series models (Prophet from Facebook) to historical metric values and identifying deviations from expected trends. The soda-scientific package extends core Soda with anomaly check types that compute metrics over time windows, train Prophet models on historical data, and flag values that fall outside predicted confidence intervals. This enables unsupervised anomaly detection without manual threshold configuration.","intents":["Detect unexpected metric changes without manually configuring thresholds","Identify data quality anomalies that deviate from historical trends","Handle seasonal patterns in metrics (e.g., lower transaction volumes on weekends)","Reduce false positives from fixed-threshold checks by learning from historical data"],"best_for":["Data teams with mature monitoring infrastructure and historical metric data","Organizations implementing ML-based data quality monitoring","Analytics teams tracking metrics with strong seasonal or trend patterns"],"limitations":["Requires soda-scientific package (separate from soda-core) — not included in base installation","Anomaly detection only works with Spark and Pandas data sources, not all SQL databases","Requires sufficient historical data (minimum ~30 days of metric history) to train Prophet models effectively","Prophet models are computationally expensive — adds significant latency to scan execution","No support for multivariate anomaly detection (detecting anomalies based on relationships between multiple metrics)"],"requires":["Python 3.8+","soda-scientific package installation","Spark or Pandas data source (not supported for direct SQL databases)","Historical metric data spanning at least 30 days","SodaCL anomaly check definitions with metric names and confidence intervals"],"input_types":["SodaCL anomaly check definitions (metric name, confidence interval percentage)","Time-series metric data (values with timestamps)","Optional seasonality and trend parameters"],"output_types":["Anomaly detection results (flagged/normal)","Predicted value ranges and confidence intervals","Prophet model diagnostics and forecast plots"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"soda__cap_5","uri":"capability://data.processing.analysis.data.freshness.monitoring.with.timestamp.based.checks","name":"data freshness monitoring with timestamp-based checks","description":"Monitors data freshness by checking the maximum timestamp in a table and comparing it against expected update frequencies. Freshness checks query the latest timestamp value, calculate the time elapsed since the last update, and evaluate whether the data is stale based on user-defined freshness thresholds (e.g., 'data must be updated within 24 hours'). Supports both absolute timestamp columns and relative time-based freshness rules.","intents":["Monitor data pipeline SLAs by detecting stale data","Alert when data updates are delayed beyond expected schedules","Track data freshness across multiple tables in a data warehouse","Validate that ETL pipelines are completing on schedule"],"best_for":["Data engineers managing ETL pipelines with SLA requirements","Analytics teams monitoring data freshness for business-critical tables","Organizations implementing data governance with freshness metrics"],"limitations":["Freshness checks require a timestamp column in the target table — cannot detect staleness without explicit update timestamps","No support for detecting partial updates (e.g., some partitions updated but not others)","Timezone handling depends on database-specific timestamp functions — may produce inconsistent results across databases","Cannot distinguish between intentional data retention (no new data expected) and pipeline failures"],"requires":["Python 3.8+","Target table with a timestamp column (created_at, updated_at, etc.)","SodaCL freshness check definitions with column name and freshness threshold","Database connection with SELECT permissions"],"input_types":["SodaCL freshness check definitions (column name, freshness threshold in hours/days)","Timestamp column name from target table","Optional WHERE clause to filter rows"],"output_types":["Maximum timestamp value from table","Time elapsed since last update","Pass/fail evaluation against freshness threshold"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"soda__cap_6","uri":"capability://data.processing.analysis.failed.row.sampling.and.root.cause.analysis","name":"failed row sampling and root cause analysis","description":"Captures and samples rows that fail quality checks by executing targeted SQL queries that retrieve rows matching failure conditions. The sampling system uses configurable sampling strategies (random, first N rows, stratified) to retrieve representative failed rows, stores them in a sample reference object, and enables inspection of actual data values that caused check failures. This supports debugging and root cause analysis of data quality issues.","intents":["Inspect actual data values that failed quality checks","Debug data quality issues by examining failed row patterns","Collect evidence for data quality incidents and post-mortems","Validate check logic by reviewing sampled failed rows"],"best_for":["Data engineers debugging data quality issues in production","Analytics teams investigating root causes of quality failures","Organizations implementing data quality incident response workflows"],"limitations":["Sampling large tables is expensive — can add significant query latency to scans","Sample size is fixed and cannot adapt to failure rate — may miss rare failure patterns","Sampled rows are stored in memory — large samples can cause memory pressure","No built-in support for sampling across multiple tables or complex join conditions"],"requires":["Python 3.8+","SodaCL check definitions with optional sample configuration","Database connection with SELECT permissions","Sufficient memory to store sampled rows in memory"],"input_types":["Failed check definitions with WHERE clause conditions","Sample size and sampling strategy configuration","Column names to include in sample output"],"output_types":["Sampled rows as structured data (dictionaries or DataFrames)","Sample reference objects (SampleRef) with metadata","Row counts and sampling statistics"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"soda__cap_7","uri":"capability://data.processing.analysis.column.profiling.and.schema.validation","name":"column profiling and schema validation","description":"Profiles column-level data characteristics (data types, null counts, value distributions, cardinality) and validates schema consistency across scans. The profiling system executes SQL queries to compute column statistics, compares current schema against expected schemas, and detects schema drift (new columns, dropped columns, type changes). Supports both automatic profiling and explicit schema validation checks.","intents":["Detect unexpected schema changes in source tables","Monitor column-level data quality metrics (nullability, cardinality)","Validate that data transformations preserve expected column properties","Track schema evolution over time"],"best_for":["Data engineers managing schema governance in data warehouses","Analytics teams detecting breaking changes in upstream data sources","Organizations implementing data contracts with schema validation"],"limitations":["Schema validation requires explicit schema definitions in SodaCL — no automatic schema inference from data","Column profiling is expensive for tables with many columns — adds significant query latency","No support for detecting semantic schema changes (e.g., column renamed but type unchanged)","Schema drift detection is binary (pass/fail) — no support for gradual schema migration workflows"],"requires":["Python 3.8+","SodaCL schema check definitions with expected column names and types","Database connection with SELECT permissions and schema introspection capabilities"],"input_types":["SodaCL schema check definitions (column names, data types, nullability)","Target table name","Optional column filters"],"output_types":["Column statistics (data types, null counts, cardinality)","Schema validation results (missing columns, extra columns, type mismatches)","Schema drift detection results"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"soda__cap_8","uri":"capability://automation.workflow.scan.orchestration.and.check.execution.lifecycle.management","name":"scan orchestration and check execution lifecycle management","description":"Orchestrates the complete scan lifecycle from configuration loading through result reporting via the Scan class. The orchestrator manages check compilation, data source connection pooling, parallel check execution, result aggregation, and output formatting. It implements a state machine that tracks scan progress (initialized, configured, executed, evaluated, reported) and handles error recovery. Supports both synchronous CLI execution and asynchronous programmatic usage via the Python API.","intents":["Execute multiple data quality checks in a single scan operation","Manage check execution order and dependencies","Aggregate results and generate quality reports","Integrate data quality checks into data pipelines programmatically"],"best_for":["Data engineers building automated data quality pipelines","Teams integrating Soda into orchestration platforms (Airflow, dbt, Prefect)","Organizations implementing continuous data quality monitoring"],"limitations":["Scan execution is synchronous by default — no built-in support for parallel check execution across multiple data sources","Check execution order is sequential — no support for check dependencies or conditional execution","Result aggregation is in-memory — scans with thousands of checks may consume significant memory","No built-in support for distributed scan execution across multiple workers"],"requires":["Python 3.8+","SodaCL configuration file with check definitions","Data source configuration with connection credentials","Valid Python environment with soda-core installed"],"input_types":["SodaCL configuration files (YAML)","Data source configuration (connection strings, credentials)","Optional scan variables and overrides"],"output_types":["Scan results object with check outcomes","Formatted reports (JSON, YAML, text)","Exit codes for CI/CD integration"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"soda__cap_9","uri":"capability://tool.use.integration.dbt.integration.with.test.result.ingestion","name":"dbt integration with test result ingestion","description":"Integrates with dbt by ingesting dbt test results and converting them into Soda quality metrics. The integration uses the `soda ingest` CLI command to parse dbt test artifacts (manifest.json, run_results.json) and create Soda checks that track dbt test pass/fail rates over time. This enables unified quality monitoring across both dbt tests and Soda checks within a single platform.","intents":["Consolidate dbt test results into Soda Cloud for unified quality monitoring","Track dbt test pass/fail rates as quality metrics over time","Integrate dbt tests into Soda quality dashboards and alerts","Migrate from dbt-only quality monitoring to a hybrid dbt + Soda approach"],"best_for":["dbt users wanting to consolidate quality monitoring in Soda Cloud","Teams using both dbt tests and Soda checks for comprehensive quality coverage","Organizations migrating from dbt-only quality monitoring to unified platforms"],"limitations":["dbt integration requires Soda Cloud account — not available in open-source Soda Core alone","Only ingests dbt test results, not dbt model lineage or documentation","dbt test results must be manually ingested via CLI — no automatic synchronization","Cannot convert dbt tests to Soda checks — only tracks test outcomes as metrics"],"requires":["Python 3.8+","dbt project with test artifacts (manifest.json, run_results.json)","Soda Cloud account and API credentials","soda-core installation with dbt integration package"],"input_types":["dbt test artifacts (manifest.json, run_results.json)","dbt project configuration","Soda Cloud API credentials"],"output_types":["Soda quality metrics representing dbt test outcomes","Test pass/fail rates tracked over time","Integration with Soda Cloud dashboards and alerts"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"soda__headline","uri":"capability://data.processing.analysis.open.source.data.quality.testing.framework","name":"open-source data quality testing framework","description":"Soda is an open-source framework designed for data quality testing and monitoring, utilizing a human-readable language for defining checks across various data sources.","intents":["best data quality testing tool","data quality framework for SQL databases","open-source data monitoring solution","how to test data quality in pipelines","data quality checks for cloud platforms"],"best_for":["data engineers","data analysts"],"limitations":["requires SQL databases or cloud data platforms"],"requires":["SodaCL language knowledge"],"input_types":["SQL databases","cloud data sources"],"output_types":["data quality reports","anomaly detection results"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","Valid YAML syntax in SodaCL configuration files","Understanding of SodaCL check type syntax (metric, distribution, anomaly, freshness)","soda-core base package","Data source-specific adapter package (e.g., soda-core-snowflake, soda-core-bigquery)","Valid database connection credentials and network access","Appropriate database permissions (SELECT on target tables, CREATE TEMP TABLE for sampling)","Soda Cloud account with API credentials","Network connectivity to Soda Cloud API endpoints","soda-core installation with cloud integration package"],"failure_modes":["SodaCL syntax is proprietary and requires learning a new DSL — not portable to other tools","Complex conditional logic across multiple columns requires nested check definitions, reducing readability","No built-in support for dynamic check generation based on schema introspection — checks must be manually defined per column","Query performance varies significantly by dialect — no automatic query optimization across platforms","Some advanced check types (anomaly detection with Prophet) only work with Spark/Pandas, not all SQL databases","Requires separate adapter package installation per data source (soda-core-snowflake, soda-core-bigquery, etc.) — no single unified package","Custom SQL checks must be written per dialect if using database-specific functions","Soda Cloud integration requires paid SaaS subscription — not available in open-source Soda Core","Scan results are uploaded to external SaaS platform — requires network connectivity and data privacy considerations","Alert configuration is managed in Soda Cloud UI, not in SodaCL — creates split configuration management","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.49999999999999994,"match_graph":0.25,"freshness":0.52,"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":"2026-06-17T09:51:05.296Z","last_scraped_at":null,"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=soda","compare_url":"https://unfragile.ai/compare?artifact=soda"}},"signature":"tkiBxdL7S4f7RZUrTHlJonO1V5LukHJMhh2+WS/zKSkUgAL6Na4gA3mQ2X2duOqc/wDXBQyFhcPpHyyL+Y/qCQ==","signedAt":"2026-06-20T16:06:50.823Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/soda","artifact":"https://unfragile.ai/soda","verify":"https://unfragile.ai/api/v1/verify?slug=soda","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"}}