{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"dlt","slug":"dlt","name":"dlt","type":"framework","url":"https://github.com/dlt-hub/dlt","page_url":"https://unfragile.ai/dlt","categories":["data-pipelines","rag-knowledge"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"dlt__cap_0","uri":"capability://data.processing.analysis.declarative.schema.inference.from.nested.json.and.structured.data","name":"declarative schema inference from nested json and structured data","description":"Automatically infers table schemas from source data by analyzing type patterns across records, handling nested objects and arrays through recursive normalization into flattened relational structures. Uses a type system that maps Python types to destination-specific SQL types, with schema evolution tracking to detect new columns or type changes across incremental loads. The schema inference engine (dlt/common/schema) maintains a canonical schema representation that guides both data normalization and destination table creation.","intents":["I want to load JSON API responses without manually defining table schemas","I need my schema to automatically adapt when the source adds new fields","I want nested objects flattened into normalized tables automatically"],"best_for":["data engineers building rapid ETL pipelines without schema design overhead","teams migrating from custom scripts to declarative data loading","developers loading from semi-structured sources (APIs, JSON files, databases)"],"limitations":["Schema inference requires at least one record to analyze; empty sources produce minimal schemas","Deeply nested structures (>5 levels) may produce verbose normalized schemas with many join tables","Type inference is probabilistic; ambiguous types (e.g., '123' as string vs integer) use heuristics that may require manual override","Schema evolution detection adds ~50-100ms per load cycle for comparison operations"],"requires":["Python 3.8+","Source data with consistent structure across records (schema inference works best with homogeneous data)"],"input_types":["JSON objects and arrays","SQL query results","REST API responses","CSV/Parquet files","Python dictionaries and dataclasses"],"output_types":["SQL table schemas","Normalized relational structure","Schema YAML/JSON representation"],"categories":["data-processing-analysis","schema-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dlt__cap_1","uri":"capability://data.processing.analysis.incremental.loading.with.state.management.and.change.tracking","name":"incremental loading with state management and change tracking","description":"Manages incremental data extraction by tracking cursor state (timestamps, IDs, offsets) across pipeline runs, enabling resumption from the last successful checkpoint without reprocessing. The state system (dlt/pipeline/state_sync.py) persists state to the destination or local filesystem, with support for multiple independent state cursors per resource. Integrates with REST API pagination and SQL WHERE clauses to fetch only new/modified records since the last run.","intents":["I want to load only new records from an API since the last successful run","I need to resume a failed pipeline without reprocessing all historical data","I want to track which records were modified and reload only those"],"best_for":["teams running scheduled pipelines (hourly, daily) that need to avoid duplicate loads","data engineers managing large datasets where full reloads are prohibitively expensive","applications with append-only or slowly-changing-dimension sources"],"limitations":["Requires source to support filtering by timestamp or ID; sources without cursor columns cannot use incremental mode","State corruption (e.g., clock skew on source system) can cause missed or duplicate records; requires manual state reset","State is per-resource; complex multi-source pipelines require coordinating state across resources","Incremental state adds ~10-20ms per pipeline run for state serialization/deserialization"],"requires":["Source with sortable/filterable cursor column (timestamp, auto-increment ID, or sequence number)","Destination with state storage capability (SQL database, filesystem, or cloud storage)"],"input_types":["REST API responses with pagination","SQL query results with WHERE clause filtering","Append-only data streams"],"output_types":["State checkpoint (JSON/YAML with cursor position)","Incremental data records (only new/modified rows)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dlt__cap_10","uri":"capability://data.processing.analysis.filesystem.destination.support.for.data.lake.and.file.based.storage","name":"filesystem destination support for data lake and file-based storage","description":"Provides destination adapters for filesystem-based storage (local filesystem, S3, GCS, Azure Blob Storage) that write normalized data as Parquet, Delta, or JSON files. The filesystem destination (dlt/destinations/filesystem.py) organizes files by table and partition, supporting both append and replace write dispositions. Integrates with cloud storage APIs (boto3, google-cloud-storage, azure-storage-blob) to enable direct writes to cloud buckets without local staging. Supports Parquet compression and partitioning strategies for efficient querying.","intents":["I want to load data into S3 as Parquet files for use with Athena or Spark","I need to create a data lake with organized table structure","I want to write data to local filesystem for testing or small-scale use"],"best_for":["teams building data lakes on cloud storage (S3, GCS, Azure)","developers using Athena, Spark, or other query engines on Parquet files","organizations with cost-sensitive workloads (filesystem storage is cheaper than data warehouses)"],"limitations":["Filesystem destinations do not support SQL queries; data must be queried with external tools (Athena, Spark, DuckDB)","Merge disposition is not supported; filesystem destinations only support append and replace","File organization is flat (one file per table per run); complex partitioning requires manual configuration","No built-in data compaction; small files accumulate over time, impacting query performance","Cloud storage latency (S3, GCS) can be 100-500ms per file write; not suitable for very high-frequency updates"],"requires":["Cloud storage account and credentials (for S3, GCS, Azure)","Write permissions to bucket/container","Optional: Parquet/Delta libraries (pyarrow, deltalake)"],"input_types":["Normalized relational records","Structured data with typed columns"],"output_types":["Parquet files","Delta Lake tables","JSON files","CSV files"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dlt__cap_11","uri":"capability://automation.workflow.tracing.and.telemetry.with.execution.visibility","name":"tracing and telemetry with execution visibility","description":"Provides built-in tracing and telemetry (dlt/common/runtime/telemetry.py) that captures pipeline execution metrics, errors, and performance data. Traces are collected at each stage (extract, normalize, load) and can be exported to external systems (OpenTelemetry, Datadog, etc.). Includes detailed logging of data volumes, execution times, and error details. Telemetry is opt-in and can be disabled for privacy-sensitive deployments.","intents":["I want to monitor pipeline performance and identify bottlenecks","I need to track how much data was extracted, normalized, and loaded","I want to debug failures by seeing detailed execution logs"],"best_for":["teams running production pipelines that need observability","developers debugging pipeline failures","organizations with SLAs that require performance monitoring"],"limitations":["Telemetry collection adds ~5-10% overhead to pipeline execution","Traces are stored locally by default; external export requires configuration","Detailed logging can produce large log files (>100MB for large pipelines); requires log rotation","Telemetry data includes source/destination names but not actual data (privacy-safe by default)","No built-in alerting; requires external monitoring system (Datadog, New Relic, etc.)"],"requires":["Optional: OpenTelemetry collector for external trace export"],"input_types":["Pipeline execution events","Stage completion metrics","Error details"],"output_types":["Execution logs","Performance metrics","Trace data (OpenTelemetry format)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dlt__cap_12","uri":"capability://automation.workflow.cli.commands.for.pipeline.management.and.deployment","name":"cli commands for pipeline management and deployment","description":"Provides command-line interface (dlt/cli) for common pipeline operations: init (create new pipeline), run (execute pipeline), deploy (push to cloud), and config (manage credentials). CLI commands are thin wrappers around Python API, enabling both programmatic and command-line usage. Supports interactive prompts for configuration and credential setup. CLI output includes progress indicators and detailed error messages.","intents":["I want to create a new pipeline from the command line","I need to run a pipeline without writing Python code","I want to deploy a pipeline to a cloud platform (Airflow, Kubernetes, etc.)"],"best_for":["data engineers who prefer CLI over Python code","teams with CI/CD pipelines that need to trigger dlt from shell scripts","developers deploying dlt to cloud platforms"],"limitations":["CLI is less flexible than Python API; complex customizations require Python code","Interactive prompts are not suitable for automated deployments; requires --non-interactive flag","Deploy command is basic; complex deployment scenarios require custom scripts","CLI does not support real-time progress streaming; output is buffered","Error messages from CLI are less detailed than Python exceptions; debugging requires checking logs"],"requires":["dlt installed via pip","Python 3.8+"],"input_types":["Command-line arguments","Interactive prompts","Configuration files"],"output_types":["Pipeline execution output","Configuration files","Deployment artifacts"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dlt__cap_13","uri":"capability://automation.workflow.airflow.integration.with.dag.generation.and.task.orchestration","name":"airflow integration with dag generation and task orchestration","description":"Provides Airflow integration (dlt/airflow) that generates Airflow DAGs from dlt pipelines, enabling orchestration through Airflow. The integration includes operators for running dlt pipelines as Airflow tasks, with automatic dependency management and error handling. Supports both dynamic DAG generation (DAGs created at runtime) and static DAG definition (DAGs defined in code). Integrates with Airflow's scheduling, monitoring, and alerting systems.","intents":["I want to run dlt pipelines as Airflow tasks","I need to orchestrate multiple dlt pipelines with dependencies","I want to use Airflow's scheduling and monitoring for dlt pipelines"],"best_for":["teams already using Airflow for orchestration","organizations with complex multi-pipeline workflows","developers who want to leverage Airflow's ecosystem (monitoring, alerting, etc.)"],"limitations":["Airflow integration adds complexity; simple pipelines may not need Airflow","DAG generation requires Airflow to be installed and configured","Dynamic DAG generation can be slow for large numbers of pipelines (>1000)","Error handling in Airflow is different from dlt; requires custom error handling logic","Monitoring is through Airflow UI, not dlt's native telemetry"],"requires":["Apache Airflow 2.0+","dlt installed in Airflow environment"],"input_types":["dlt pipeline definitions","Airflow DAG configuration"],"output_types":["Airflow DAGs","Airflow tasks","Task dependencies"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dlt__cap_2","uri":"capability://data.processing.analysis.multi.destination.data.loading.with.write.disposition.strategies","name":"multi-destination data loading with write disposition strategies","description":"Loads normalized data into 30+ destinations (Snowflake, BigQuery, Databricks, DuckDB, PostgreSQL, Redshift, Athena, ClickHouse, Pinecone, Weaviate, Qdrant, and filesystems) using a pluggable destination abstraction. Supports three write dispositions (append, replace, merge) that control how data is written: append adds new records, replace truncates and reloads, merge performs upsert-style updates based on primary keys. Each destination implements a JobClient interface that translates normalized data into destination-specific SQL/API calls.","intents":["I want to load data into Snowflake without writing custom SQL","I need to sync data to multiple destinations from a single pipeline","I want to replace a table on each run, or merge new records with existing data"],"best_for":["data teams using cloud warehouses (Snowflake, BigQuery, Databricks)","organizations with multi-destination architectures (warehouse + vector DB + data lake)","developers building ELT pipelines where transformation happens in the destination"],"limitations":["Merge disposition requires primary key definition; without it, falls back to append","Destination-specific SQL dialects may cause schema compatibility issues (e.g., DECIMAL precision differs between Snowflake and BigQuery)","Large batch loads (>1GB) may hit destination rate limits or timeout; requires manual batching configuration","Vector database destinations (Pinecone, Weaviate) require embedding vectors in source data; dlt does not generate embeddings"],"requires":["Destination credentials (API key, connection string, or service account)","Network access to destination (firewall rules, VPC peering, or public endpoints)","Destination-specific Python SDK (e.g., snowflake-connector-python, google-cloud-bigquery)"],"input_types":["Normalized relational data (tables with typed columns)","Nested JSON (flattened during normalization)","Structured records from Python generators"],"output_types":["SQL tables in data warehouse","Parquet/Delta files in data lake","Vector embeddings in vector database","CSV/JSON files in filesystem"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dlt__cap_3","uri":"capability://data.processing.analysis.rest.api.data.extraction.with.pagination.and.authentication.handling","name":"rest api data extraction with pagination and authentication handling","description":"Provides a declarative REST API source abstraction (dlt/sources/rest_client.py) that handles pagination, authentication (API keys, OAuth, basic auth), rate limiting, and response parsing. The REST client automatically detects pagination patterns (offset, cursor, link-based) and follows them until exhaustion. Integrates with the incremental loading system to support cursor-based pagination for efficient delta syncs. Supports both JSON and non-JSON responses through pluggable response processors.","intents":["I want to load data from a REST API without writing pagination logic","I need to handle API authentication and rate limiting transparently","I want to extract only new records from an API using cursor-based pagination"],"best_for":["data engineers building connectors for SaaS APIs (Stripe, Salesforce, HubSpot, etc.)","teams loading data from custom REST APIs with standard pagination patterns","developers who want to avoid writing boilerplate HTTP client code"],"limitations":["Pagination detection is heuristic-based; non-standard pagination patterns require custom pagination handlers","Rate limiting is client-side only; does not coordinate with other pipeline instances (requires external rate limiter for multi-instance deployments)","Response parsing assumes JSON; binary or streaming responses require custom processors","Large response bodies (>100MB) may cause memory issues; requires streaming/chunking configuration","OAuth token refresh is not automatic; requires manual token management or external token service"],"requires":["REST API with HTTP GET/POST endpoints","API authentication credentials (API key, OAuth token, or basic auth)","Network access to API endpoint"],"input_types":["REST API endpoint URLs","Query parameters and headers","Request body (for POST requests)"],"output_types":["JSON objects/arrays","Parsed records ready for normalization"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dlt__cap_4","uri":"capability://data.processing.analysis.sql.database.source.extraction.with.table.discovery.and.query.execution","name":"sql database source extraction with table discovery and query execution","description":"Provides a SQL database source abstraction (dlt/sources/sql_database.py) that discovers tables, executes queries, and extracts data from SQL databases (PostgreSQL, MySQL, SQL Server, Oracle, Snowflake, BigQuery, etc.). Supports table selection, column filtering, and custom SQL queries. Integrates with incremental loading to support WHERE clause filtering for delta syncs. Automatically handles connection pooling, query timeouts, and result streaming for large tables.","intents":["I want to replicate tables from a PostgreSQL database to a data warehouse","I need to extract only specific columns from large tables","I want to run a custom SQL query and load the results incrementally"],"best_for":["data engineers building database replication pipelines","teams migrating data between SQL databases","developers extracting data from operational databases for analytics"],"limitations":["Table discovery requires database-specific metadata queries; some databases (e.g., Oracle) may have permission restrictions","Large tables (>10GB) require manual partitioning or chunking; full table scans can lock source database","Custom SQL queries are not validated; syntax errors are caught at execution time","Connection pooling adds ~50-100ms overhead per query; not suitable for very high-frequency queries","Incremental filtering requires a sortable column (timestamp or ID); tables without such columns must be fully reloaded"],"requires":["SQL database with network access","Database credentials (username/password or connection string)","Database-specific Python driver (psycopg2, pymysql, pyodbc, etc.)"],"input_types":["Table names","Column lists","Custom SQL queries","WHERE clause filters"],"output_types":["Rows from SQL tables","Query results as structured records"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dlt__cap_5","uri":"capability://data.processing.analysis.data.normalization.with.recursive.flattening.and.table.generation","name":"data normalization with recursive flattening and table generation","description":"Transforms nested JSON and complex data structures into normalized relational tables through recursive flattening (dlt/normalize/normalize.py). Nested objects become separate tables with foreign key relationships, arrays are unnested into child tables, and primitive types are mapped to SQL columns. The normalization engine processes data in streaming fashion, writing normalized records to intermediate files before loading. Supports configurable flattening depth and naming conventions for generated tables.","intents":["I want to flatten a deeply nested JSON response into relational tables","I need to handle arrays in JSON by creating separate child tables","I want to customize how nested structures are named and organized"],"best_for":["data engineers loading semi-structured data (APIs, JSON files) into SQL databases","teams that need relational schemas from hierarchical sources","developers building ETL pipelines where normalization is a critical step"],"limitations":["Deeply nested structures (>10 levels) produce many small tables with complex join logic; may impact query performance","Circular references in JSON are not detected; can cause infinite recursion if not handled","Flattening decisions (which objects become tables vs columns) are heuristic-based; may not match domain semantics","Normalization adds ~100-200ms per 1000 records for file I/O and schema tracking","Column naming conventions (e.g., parent__child__field) can become verbose and hard to query"],"requires":["Structured data with consistent schema across records","Destination that supports foreign key relationships (optional but recommended)"],"input_types":["JSON objects and arrays","Nested Python dictionaries","Dataclass instances"],"output_types":["Normalized relational records","Multiple tables with foreign keys","Flattened column names with hierarchy indicators"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dlt__cap_6","uri":"capability://automation.workflow.pipeline.orchestration.with.extract.normalize.load.sequencing","name":"pipeline orchestration with extract-normalize-load sequencing","description":"Orchestrates the three-stage ETL pipeline (extract, normalize, load) through the Pipeline class (dlt/pipeline/pipeline.py), which manages execution sequencing, error handling, and state persistence. Each stage produces intermediate artifacts (extracted data files, normalized records, load jobs) that feed into the next stage. The pipeline supports both synchronous execution (blocking until completion) and asynchronous execution (returning immediately with job tracking). Includes retry logic, partial failure recovery, and detailed logging of each stage.","intents":["I want to run a complete ETL pipeline from source to destination in one call","I need to handle failures gracefully and resume from the last successful stage","I want visibility into what happened at each stage (extract, normalize, load)"],"best_for":["data engineers building production ETL pipelines","teams using dlt as the core orchestration layer (vs Airflow or Dagster)","developers who want simple, synchronous pipeline execution without external orchestrators"],"limitations":["Pipeline state is local to the machine; distributed execution across multiple workers requires external orchestration (Airflow, Kubernetes)","Retry logic is basic (exponential backoff); complex retry strategies require custom code","No built-in scheduling; requires external scheduler (cron, Airflow, etc.) to run pipelines on a schedule","Pipeline execution is blocking; long-running pipelines (>1 hour) may timeout in serverless environments","State synchronization between pipeline instances is eventual-consistent; concurrent runs may cause state conflicts"],"requires":["Python 3.8+","Source and destination configured and accessible","Sufficient disk space for intermediate files (typically 2-3x source data size)"],"input_types":["Data source (API, database, file)","Pipeline configuration (name, destination, dataset)"],"output_types":["Loaded data in destination","Pipeline state (checkpoint for resumption)","Execution logs and metrics"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dlt__cap_7","uri":"capability://safety.moderation.configuration.and.secrets.management.with.environment.based.resolution","name":"configuration and secrets management with environment-based resolution","description":"Manages pipeline configuration (source credentials, destination settings, dataset names) through a hierarchical resolution system (dlt/common/configuration) that checks environment variables, .dlt/secrets.toml files, and Python code in that order. Supports typed configuration specs with validation, enabling IDE autocomplete and early error detection. Secrets are encrypted at rest in .dlt/secrets.toml and never logged. Configuration can be overridden per-pipeline or per-run through function parameters.","intents":["I want to manage API keys and database credentials without hardcoding them","I need different configurations for dev, staging, and production environments","I want to share pipeline code without exposing secrets"],"best_for":["teams deploying pipelines across multiple environments","developers who need to manage secrets securely","organizations with strict credential management policies"],"limitations":["Secrets are encrypted with a local key; key rotation requires re-encrypting all secrets","Environment variable resolution is simple string matching; no support for complex variable interpolation","Configuration validation happens at runtime; invalid configs are caught when pipeline runs, not at definition time","Secrets file (.dlt/secrets.toml) must be manually backed up; no built-in secret rotation or expiration","No support for external secret managers (AWS Secrets Manager, HashiCorp Vault); requires custom integration"],"requires":[".dlt/secrets.toml file in project root (auto-created on first run)","Environment variables for CI/CD deployments (optional but recommended)"],"input_types":["Environment variables","TOML configuration files","Python function parameters"],"output_types":["Resolved configuration objects","Validated credentials"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dlt__cap_8","uri":"capability://code.generation.editing.source.and.resource.abstraction.for.composable.data.extraction","name":"source and resource abstraction for composable data extraction","description":"Provides a decorator-based abstraction (dlt/extract/decorators.py) for defining reusable data sources and resources. Sources are collections of resources (e.g., a Stripe source with resources for customers, invoices, subscriptions). Resources are generator functions that yield records, with metadata (name, write disposition, primary key) attached via decorators. Sources can be composed, parameterized, and shared as Python packages. The abstraction enables code reuse and makes pipelines more readable and maintainable.","intents":["I want to create a reusable Stripe connector that can be shared across teams","I need to parameterize a source (e.g., API key, date range) without hardcoding values","I want to compose multiple sources into a single pipeline"],"best_for":["teams building internal data connectors for common SaaS platforms","developers creating reusable ETL components","organizations with multiple pipelines that share data sources"],"limitations":["Sources are Python packages; requires Python knowledge to create and maintain","Resource composition is manual; no automatic dependency resolution between resources","Parameterization is via function arguments; complex configuration requires custom logic","Source versioning is manual; no built-in compatibility checking between source versions","Testing sources requires mocking external APIs; no built-in test fixtures"],"requires":["Python 3.8+","Understanding of generators and decorators"],"input_types":["Generator functions yielding records","Metadata (table name, write disposition, primary key)"],"output_types":["Reusable source objects","Composable resource definitions"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dlt__cap_9","uri":"capability://data.processing.analysis.vector.database.destination.support.with.embedding.integration","name":"vector database destination support with embedding integration","description":"Provides destination adapters for vector databases (Pinecone, Weaviate, Qdrant, LanceDB) that load normalized data as vector embeddings. The vector destination abstraction (dlt/destinations/vector_database.py) expects source data to include embedding vectors (as float arrays) and metadata columns. Supports batch loading, upsert operations, and metadata filtering. Integrates with the write disposition system to support append and merge strategies for vector data.","intents":["I want to load embeddings into Pinecone for semantic search","I need to sync documents and their embeddings to Weaviate","I want to update embeddings in a vector database when source data changes"],"best_for":["teams building RAG (Retrieval-Augmented Generation) systems","developers creating semantic search applications","organizations using vector databases for AI/ML workloads"],"limitations":["dlt does not generate embeddings; source data must include pre-computed embedding vectors","Vector database schemas are less flexible than SQL; metadata filtering is limited to supported field types","Batch loading performance depends on vector database API rate limits; large batches may timeout","Upsert operations in vector databases are slower than SQL databases; not suitable for high-frequency updates","No support for vector similarity search; vector databases are write-only from dlt's perspective"],"requires":["Vector database account and API credentials","Source data with embedding vectors (float arrays)","Metadata columns for filtering and retrieval"],"input_types":["Normalized records with embedding vectors","Metadata columns (text, numbers, dates)"],"output_types":["Vectors stored in vector database","Metadata indexed for filtering"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dlt__headline","uri":"capability://data.processing.analysis.declarative.data.loading.framework","name":"declarative data loading framework","description":"dlt is an open-source Python library designed for declarative data loading, replacing custom ETL scripts by automatically inferring schemas and managing incremental loading across 30+ destinations including data warehouses and vector databases.","intents":["best data loading framework","declarative ETL tool for Python","data loading solution for data warehouses","open-source data pipeline framework","Python library for data ingestion"],"best_for":["data engineers","data scientists"],"limitations":[],"requires":["Python"],"input_types":["JSON","CSV","API data"],"output_types":["data warehouse","data lake","vector database"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","Source data with consistent structure across records (schema inference works best with homogeneous data)","Source with sortable/filterable cursor column (timestamp, auto-increment ID, or sequence number)","Destination with state storage capability (SQL database, filesystem, or cloud storage)","Cloud storage account and credentials (for S3, GCS, Azure)","Write permissions to bucket/container","Optional: Parquet/Delta libraries (pyarrow, deltalake)","Optional: OpenTelemetry collector for external trace export","dlt installed via pip","Apache Airflow 2.0+"],"failure_modes":["Schema inference requires at least one record to analyze; empty sources produce minimal schemas","Deeply nested structures (>5 levels) may produce verbose normalized schemas with many join tables","Type inference is probabilistic; ambiguous types (e.g., '123' as string vs integer) use heuristics that may require manual override","Schema evolution detection adds ~50-100ms per load cycle for comparison operations","Requires source to support filtering by timestamp or ID; sources without cursor columns cannot use incremental mode","State corruption (e.g., clock skew on source system) can cause missed or duplicate records; requires manual state reset","State is per-resource; complex multi-source pipelines require coordinating state across resources","Incremental state adds ~10-20ms per pipeline run for state serialization/deserialization","Filesystem destinations do not support SQL queries; data must be queried with external tools (Athena, Spark, DuckDB)","Merge disposition is not supported; filesystem destinations only support append and replace","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":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.691Z","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=dlt","compare_url":"https://unfragile.ai/compare?artifact=dlt"}},"signature":"iKbda0c+DY4Au0Ziim43oo89uFMpQttLlxPpZ9qra3SF4ToKyLR2ivhg0ZGbD2/xwuEBVM9240npq01SvhEiCg==","signedAt":"2026-06-23T16:06:15.400Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/dlt","artifact":"https://unfragile.ai/dlt","verify":"https://unfragile.ai/api/v1/verify?slug=dlt","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"}}