{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"dlt-data-load-tool","slug":"dlt-data-load-tool","name":"dlt (data load tool)","type":"repo","url":"https://github.com/dlt-hub/dlt","page_url":"https://unfragile.ai/dlt-data-load-tool","categories":["data-pipelines"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"dlt-data-load-tool__cap_0","uri":"capability://automation.workflow.declarative.pipeline.orchestration.with.extract.normalize.load.sequencing","name":"declarative pipeline orchestration with extract-normalize-load sequencing","description":"dlt provides a Pipeline class that acts as a central orchestrator managing the complete ETL lifecycle through three sequential stages: extract (data ingestion), normalize (schema inference and transformation), and load (destination writing). The Pipeline class holds runtime context, manages state persistence, and sequences stage execution with built-in retry logic and error handling. Configuration resolution uses a decorator-based system (@with_config) that binds pipeline parameters to config files and environment variables, enabling environment-agnostic pipeline definitions.","intents":["I want to define a data pipeline once and run it across dev, staging, and production without code changes","I need to orchestrate multi-stage data workflows with automatic state management and recovery","I want to build pipelines that handle partial failures and resume from checkpoints"],"best_for":["data engineers building production ETL workflows","teams migrating from Airflow DAGs to Python-native pipeline definitions","organizations needing environment-agnostic pipeline code"],"limitations":["Pipeline state stored locally by default — requires external state store for distributed execution","Sequential stage execution means no built-in parallelization across extract/normalize/load phases","Configuration resolution adds complexity when managing secrets across multiple environments"],"requires":["Python 3.9+","dlt library installed via pip","destination credentials configured via config files or environment variables"],"input_types":["configuration dictionaries","source decorators","resource generators"],"output_types":["loaded datasets in destination","pipeline state metadata","execution logs and telemetry"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dlt-data-load-tool__cap_1","uri":"capability://data.processing.analysis.automatic.schema.inference.and.evolution.with.type.system","name":"automatic schema inference and evolution with type system","description":"dlt automatically infers schemas from source data during extraction using a built-in type system that maps Python types to destination-specific SQL types. The schema architecture supports evolution — new columns are detected and added automatically, and type changes are tracked. Schema inference happens during the normalize stage, which parses extracted data and generates table definitions without requiring manual schema specification. The type inference system handles nested structures, nullable fields, and precision constraints, with destination-specific type mapping (e.g., BigQuery TIMESTAMP vs Snowflake TIMESTAMP_NTZ).","intents":["I want to load data from an API without manually defining table schemas","I need schemas to evolve automatically when source data structure changes","I want type safety and validation without writing schema definitions"],"best_for":["rapid prototyping teams loading from unstructured sources","data engineers managing evolving data sources","teams avoiding manual schema maintenance overhead"],"limitations":["Automatic inference may infer overly permissive types (e.g., string instead of int) for sparse data","Schema evolution can create unexpected columns if source data is inconsistent","Destination-specific type mappings may lose precision (e.g., Python Decimal → float in some destinations)"],"requires":["Python 3.9+","source data in structured format (JSON, CSV, database rows)","destination that supports schema creation (SQL databases, BigQuery, Snowflake, etc.)"],"input_types":["JSON objects","database rows","CSV records","Python dictionaries"],"output_types":["SQL table definitions","schema metadata with column types","destination-specific DDL statements"],"categories":["data-processing-analysis","schema-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dlt-data-load-tool__cap_10","uri":"capability://automation.workflow.cli.based.pipeline.management.and.deployment","name":"cli-based pipeline management and deployment","description":"dlt provides a command-line interface for initializing pipelines, managing pipeline state, and deploying to cloud platforms. The CLI supports commands for creating new pipelines (dlt init), running pipelines (dlt run), inspecting state (dlt state), and deploying to Airflow or cloud functions. The init command scaffolds pipeline code with source templates, reducing boilerplate. The CLI integrates with the configuration system, allowing environment-specific deployments without code changes. Deployment commands generate Airflow DAGs or cloud function definitions from pipeline code, enabling serverless execution.","intents":["I want to quickly scaffold a new pipeline with source templates","I need to deploy pipelines to Airflow or cloud functions without manual configuration","I want to inspect and manage pipeline state from the command line"],"best_for":["data engineers building pipelines quickly","teams deploying to Airflow or cloud platforms","organizations standardizing on dlt for pipeline development"],"limitations":["CLI scaffolding generates boilerplate but requires customization for complex pipelines","Deployment to Airflow requires Airflow installation and configuration","Cloud function deployment is limited to specific cloud providers"],"requires":["Python 3.9+","dlt CLI installed (pip install dlt)","Airflow or cloud function environment for deployment (optional)"],"input_types":["pipeline name and source type","configuration parameters","deployment target (Airflow, cloud function)"],"output_types":["scaffolded pipeline code","Airflow DAG definitions","cloud function deployment packages"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dlt-data-load-tool__cap_11","uri":"capability://data.processing.analysis.verified.sources.library.with.pre.built.connectors","name":"verified sources library with pre-built connectors","description":"dlt provides a library of verified sources (pre-built connectors) for popular SaaS platforms (Stripe, Salesforce, HubSpot, GitHub, etc.) and databases. These sources encapsulate API integration logic, pagination handling, authentication, and schema definitions, reducing development time for common data sources. Verified sources are maintained by the dlt community and tested against source APIs, ensuring reliability. Developers can use verified sources directly or customize them for specific needs. The sources are published in a central registry and can be discovered via the CLI or documentation.","intents":["I want to load data from Stripe, Salesforce, or other SaaS platforms without building connectors from scratch","I need reliable, tested connectors that are maintained by the community","I want to customize verified sources for specific business logic"],"best_for":["teams integrating popular SaaS platforms","organizations avoiding custom connector development","data engineers building data platforms quickly"],"limitations":["Verified sources may not support all API features — customization required for advanced use cases","Source maintenance depends on community contributions — some sources may lag behind API changes","Customization of verified sources can be complex if source code is not well-documented"],"requires":["Python 3.9+","API credentials for the source platform","dlt library with verified sources installed"],"input_types":["API credentials","configuration parameters","customization code (optional)"],"output_types":["extracted data from SaaS platform","normalized tables","loaded data in destination"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dlt-data-load-tool__cap_12","uri":"capability://automation.workflow.tracing.and.telemetry.with.execution.observability","name":"tracing and telemetry with execution observability","description":"dlt provides built-in tracing and telemetry that captures pipeline execution metrics, logs, and errors. The system tracks execution time, data volumes, schema changes, and load statistics, providing visibility into pipeline performance and health. Telemetry is sent to dlt's cloud platform for centralized monitoring and alerting (optional). The tracing system integrates with Python's logging module, allowing custom log handlers and log level configuration. Execution metadata is stored in the pipeline's state, enabling historical analysis of pipeline runs.","intents":["I want to monitor pipeline execution metrics and identify performance bottlenecks","I need to track data volumes and schema changes across pipeline runs","I want centralized alerting for pipeline failures"],"best_for":["data engineers monitoring production pipelines","teams debugging pipeline performance issues","organizations needing pipeline observability"],"limitations":["Telemetry is sent to dlt's cloud platform by default — requires opt-out for privacy","Custom metrics require manual instrumentation","Alerting is limited to dlt's cloud platform — integration with other monitoring tools requires custom code"],"requires":["Python 3.9+","dlt library with telemetry enabled (default)","dlt cloud account for centralized monitoring (optional)"],"input_types":["pipeline execution events","logging output","performance metrics"],"output_types":["execution logs","performance metrics","telemetry data","alerts (via dlt cloud)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dlt-data-load-tool__cap_13","uri":"capability://data.processing.analysis.vector.database.loading.with.embedding.support","name":"vector database loading with embedding support","description":"dlt supports loading data to vector databases (Weaviate, Qdrant, Pinecone, LanceDB) with automatic embedding generation and storage. The system can generate embeddings from text fields using OpenAI, Hugging Face, or other embedding models, and store them alongside original data in vector databases. Vector database destinations handle schema mapping, embedding storage, and similarity search configuration. This enables building RAG (retrieval-augmented generation) systems and semantic search applications directly from dlt pipelines.","intents":["I want to load data to a vector database with automatic embedding generation","I need to build RAG systems with semantic search capabilities","I want to store embeddings alongside original data for hybrid search"],"best_for":["teams building RAG and semantic search applications","data engineers loading data to vector databases","organizations implementing AI-powered search"],"limitations":["Embedding generation adds latency and cost — requires careful batching and caching","Vector database schema mapping is limited to simple field types","Embedding model selection requires domain expertise — wrong model can degrade search quality"],"requires":["Python 3.9+","vector database credentials (Weaviate, Qdrant, Pinecone, LanceDB, etc.)","embedding model API key (OpenAI, Hugging Face, etc.) or local model"],"input_types":["text data for embedding","structured data with text fields","embedding model configuration"],"output_types":["embeddings in vector database","original data with embedding references","vector database indexes"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dlt-data-load-tool__cap_2","uri":"capability://data.processing.analysis.incremental.loading.with.state.based.change.tracking","name":"incremental loading with state-based change tracking","description":"dlt provides an Incremental class that tracks state across pipeline runs to load only new or modified data from sources. The system stores state (e.g., last_updated timestamp, max_id) in the pipeline's state store and uses it to filter source data on subsequent runs. State is persisted after each successful load and can be restored from the destination if local state is lost. The incremental loading mechanism integrates with the pipe system, allowing transformers to access state and apply filtering logic. This enables efficient loading of large datasets by avoiding full re-extraction on each run.","intents":["I want to load only new records from an API since the last run without re-fetching everything","I need to track the last update timestamp and resume from there on pipeline failure","I want to implement CDC (change data capture) patterns without external tools"],"best_for":["teams loading from append-only or timestamp-based sources","data engineers optimizing pipeline runtime by avoiding full re-extraction","organizations with large datasets requiring incremental updates"],"limitations":["Requires source to support filtering by timestamp or ID — not all APIs support this","State corruption or loss can cause duplicate records or missed data","Incremental state is pipeline-specific — sharing state across pipelines requires custom logic"],"requires":["Python 3.9+","source that supports filtering by timestamp, ID, or cursor","state store (local filesystem or destination database)"],"input_types":["timestamp or ID-based filter parameters","source data with ordering or modification tracking"],"output_types":["filtered dataset containing only new/modified records","updated state metadata (last_updated, max_id, etc.)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dlt-data-load-tool__cap_3","uri":"capability://data.processing.analysis.rest.api.integration.with.built.in.pagination.and.retry.handling","name":"rest api integration with built-in pagination and retry handling","description":"dlt provides a REST API source that handles common API patterns including pagination (offset, cursor, page-based), authentication (API keys, OAuth, basic auth), and retry logic with exponential backoff. The REST API integration uses a declarative configuration approach where developers specify endpoint URLs, pagination parameters, and authentication details, and dlt automatically handles pagination state, rate limiting, and transient failures. The system supports nested resource extraction (e.g., fetching related records from multiple endpoints) through the pipe system, enabling complex multi-endpoint data collection in a single pipeline.","intents":["I want to load data from a REST API without manually handling pagination and retries","I need to extract related data from multiple API endpoints in a single pipeline","I want to handle API rate limiting and transient failures automatically"],"best_for":["data engineers integrating SaaS APIs (Stripe, Salesforce, HubSpot, etc.)","teams avoiding manual pagination and retry logic","organizations needing reliable API data extraction with minimal code"],"limitations":["Pagination support is limited to common patterns — custom pagination schemes require custom code","Authentication must be configured upfront — dynamic token refresh requires custom logic","Rate limiting is handled via backoff but not via token bucket or quota management"],"requires":["Python 3.9+","API endpoint URL and authentication credentials","API that returns JSON or supports JSON parsing"],"input_types":["API endpoint URL","authentication credentials (API key, OAuth token, etc.)","pagination configuration (offset, cursor, page size)"],"output_types":["extracted JSON records","nested resource data","pagination state for incremental loading"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dlt-data-load-tool__cap_4","uri":"capability://data.processing.analysis.sql.database.source.extraction.with.table.discovery.and.filtering","name":"sql database source extraction with table discovery and filtering","description":"dlt provides a SQL database source that connects to relational databases (PostgreSQL, MySQL, SQL Server, etc.) and automatically discovers tables, columns, and relationships. The system supports table filtering (include/exclude patterns), column selection, and incremental loading based on modification timestamps or primary keys. The SQL source integrates with the pipe system to enable transformations on extracted data before loading. Database connections are managed through SQLAlchemy, supporting a wide range of database engines with consistent configuration and credential management.","intents":["I want to replicate tables from a production database to a data warehouse without manual schema definition","I need to extract only specific tables or columns from a large database","I want to implement incremental syncs based on modification timestamps or primary keys"],"best_for":["data engineers replicating databases to data warehouses","teams building data lakes from operational databases","organizations needing automated database-to-cloud data movement"],"limitations":["Requires network access to source database — may not work with private databases without VPN","Large table extraction can be slow without proper indexing on filter columns","Incremental loading requires modification tracking (e.g., updated_at column) — not all tables have this"],"requires":["Python 3.9+","SQLAlchemy-compatible database driver (psycopg2, pymysql, pyodbc, etc.)","database connection credentials and network access"],"input_types":["database connection string","table names or patterns","column selection criteria","filter conditions"],"output_types":["extracted table data as JSON records","inferred schema from database metadata","incremental state (last_modified timestamp, max_id)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dlt-data-load-tool__cap_5","uri":"capability://data.processing.analysis.pipe.system.with.transformer.based.data.transformation","name":"pipe system with transformer-based data transformation","description":"dlt's pipe system provides a composable data transformation framework where transformers are generator functions that receive data from upstream sources or pipes and yield transformed records. Transformers integrate with the extraction stage, enabling in-flight transformations before data reaches the normalize stage. The pipe system supports chaining multiple transformers, accessing pipeline state and context, and implementing custom business logic (filtering, enrichment, aggregation). Transformers are executed within the extraction stage using a pool runner that can parallelize transformer execution across multiple workers.","intents":["I want to filter, enrich, or aggregate data during extraction without separate transformation jobs","I need to apply custom business logic to API responses before loading","I want to parallelize data transformations across multiple workers"],"best_for":["data engineers implementing complex extraction logic","teams avoiding separate transformation layers (dbt, Spark)","organizations needing real-time data enrichment during extraction"],"limitations":["Transformers are limited to in-memory operations — not suitable for large aggregations","Parallel transformer execution requires careful state management to avoid race conditions","Debugging transformer chains can be complex due to generator-based execution model"],"requires":["Python 3.9+","understanding of Python generators and async patterns","source data in structured format (JSON, database rows, etc.)"],"input_types":["generator functions yielding records","source data from API, database, or file","pipeline state and context"],"output_types":["transformed records","filtered or enriched data","aggregated results"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dlt-data-load-tool__cap_6","uri":"capability://data.processing.analysis.multi.destination.loading.with.write.disposition.strategies","name":"multi-destination loading with write disposition strategies","description":"dlt supports loading data to multiple destinations (PostgreSQL, BigQuery, Snowflake, Databricks, DuckDB, Athena, ClickHouse, vector databases) with configurable write dispositions that control how data is written: replace (truncate and reload), append (insert new records), or merge (upsert based on primary keys). The load stage uses destination-specific job clients that generate and execute DDL/DML statements optimized for each destination. Write dispositions are applied at the table level, enabling different strategies for different tables in the same pipeline. The system handles schema creation, data type mapping, and destination-specific optimizations (e.g., BigQuery clustering, Snowflake clustering).","intents":["I want to load data to multiple destinations (data warehouse, data lake, vector DB) in a single pipeline","I need to implement upsert logic without writing custom merge statements","I want destination-specific optimizations (clustering, partitioning) without manual configuration"],"best_for":["data engineers managing multi-destination data pipelines","teams building data platforms with heterogeneous storage","organizations needing flexible write strategies per table"],"limitations":["Write disposition merge requires primary key definition — not all tables have natural keys","Destination-specific optimizations may not be available for all destinations","Large batch loads can be slow without destination-specific tuning (e.g., BigQuery load jobs)"],"requires":["Python 3.9+","destination credentials and connection parameters","destination-specific Python driver (psycopg2, google-cloud-bigquery, snowflake-connector, etc.)"],"input_types":["extracted and normalized data","write disposition configuration (replace, append, merge)","primary key definitions for merge operations"],"output_types":["loaded data in destination tables","load metadata (row counts, execution time)","destination-specific artifacts (BigQuery load jobs, Snowflake tasks)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dlt-data-load-tool__cap_7","uri":"capability://data.processing.analysis.data.normalization.with.nested.structure.flattening","name":"data normalization with nested structure flattening","description":"dlt's normalize stage transforms extracted data (often nested JSON) into flat, relational tables with automatic handling of nested objects and arrays. The normalization process infers schemas from data, creates parent-child relationships for nested structures, and generates normalized table definitions. The system handles deeply nested structures by creating separate tables for nested arrays and linking them via foreign keys. Normalization happens automatically after extraction and before loading, eliminating manual data flattening logic. The normalize stage is configurable, allowing control over table naming, column naming, and nesting depth.","intents":["I want to load nested JSON from APIs into flat relational tables without manual flattening","I need to handle deeply nested structures with automatic parent-child relationship creation","I want configurable normalization rules for table and column naming"],"best_for":["data engineers loading semi-structured data from APIs","teams avoiding manual JSON flattening logic","organizations needing relational representations of nested data"],"limitations":["Automatic flattening can create many tables for deeply nested structures, complicating queries","Normalization rules are global — different normalization strategies for different tables require custom code","Foreign key relationships are inferred but not enforced by default"],"requires":["Python 3.9+","extracted data in JSON or dictionary format","destination that supports multiple related tables"],"input_types":["nested JSON objects","Python dictionaries with nested structures","API responses with arrays and objects"],"output_types":["flat relational tables","parent-child relationships via foreign keys","normalized schema definitions"],"categories":["data-processing-analysis","schema-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dlt-data-load-tool__cap_8","uri":"capability://automation.workflow.configuration.and.secrets.management.with.environment.resolution","name":"configuration and secrets management with environment resolution","description":"dlt provides a configuration system that resolves pipeline parameters from multiple sources (config files, environment variables, Python code) with a clear precedence order. Secrets are managed separately from configuration, supporting secure storage in environment variables, .dlt/secrets.toml files, or external secret managers. The configuration system uses a decorator-based approach (@with_config) that binds function parameters to configuration specs, enabling environment-agnostic code. Configuration is organized into sections (PIPELINES, SOURCES, DESTINATIONS) and supports nested configuration for complex settings. The system validates configuration at runtime and provides clear error messages for missing or invalid settings.","intents":["I want to define pipelines that work across dev, staging, and production without code changes","I need to manage secrets securely without hardcoding credentials in code","I want configuration to be resolved from environment variables, files, or code with clear precedence"],"best_for":["teams deploying pipelines across multiple environments","organizations with strict secrets management requirements","data engineers avoiding hardcoded credentials and environment-specific code"],"limitations":["Configuration precedence can be confusing when settings are defined in multiple places","Secrets in .dlt/secrets.toml files are not encrypted — requires careful file permissions","External secret manager integration requires custom code"],"requires":["Python 3.9+",".dlt/config.toml or .dlt/secrets.toml files (optional)","environment variables for secrets (optional)"],"input_types":["configuration dictionaries","environment variables","TOML configuration files"],"output_types":["resolved configuration values","validated secrets","configuration metadata"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dlt-data-load-tool__cap_9","uri":"capability://automation.workflow.pipeline.state.persistence.and.recovery.with.destination.restoration","name":"pipeline state persistence and recovery with destination restoration","description":"dlt automatically persists pipeline state after each successful load, storing metadata like last_updated timestamps, max_ids, and execution history. State can be restored from the local filesystem or from the destination database if local state is lost, enabling recovery from failures without manual intervention. The state system integrates with incremental loading, allowing pipelines to resume from the last successful checkpoint. State is stored in a .dlt directory and can be synced to the destination for distributed execution. The system provides state inspection and manipulation commands for debugging and recovery.","intents":["I want pipelines to automatically resume from the last successful run after failures","I need to recover pipeline state from the destination if local state is lost","I want to inspect and manipulate pipeline state for debugging"],"best_for":["teams running pipelines in unreliable environments","organizations needing resilient data pipelines with automatic recovery","data engineers debugging pipeline failures"],"limitations":["State stored locally is not shared across distributed workers — requires external state store for horizontal scaling","State restoration from destination adds latency on first run","State corruption can cause duplicate records or missed data — requires manual recovery"],"requires":["Python 3.9+",".dlt directory with write permissions","destination database for state restoration (optional)"],"input_types":["pipeline execution metadata","incremental loading state","destination state snapshots"],"output_types":["persisted state files","state metadata in destination","recovery checkpoints"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dlt-data-load-tool__headline","uri":"capability://data.processing.analysis.data.loading.tool.for.building.data.pipelines","name":"data loading tool for building data pipelines","description":"dlt is an open-source Python library designed for simplifying the process of loading data from various sources into structured datasets, featuring automatic schema inference and incremental loading.","intents":["best data loading tool","data loading tool for building pipelines","open-source library for data ingestion","Python library for data pipelines","data loading solutions for APIs and databases"],"best_for":["developers needing efficient data pipelines","data engineers working with multiple data sources"],"limitations":[],"requires":["Python environment"],"input_types":["APIs","databases","files"],"output_types":["structured datasets"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","dlt library installed via pip","destination credentials configured via config files or environment variables","source data in structured format (JSON, CSV, database rows)","destination that supports schema creation (SQL databases, BigQuery, Snowflake, etc.)","dlt CLI installed (pip install dlt)","Airflow or cloud function environment for deployment (optional)","API credentials for the source platform","dlt library with verified sources installed","dlt library with telemetry enabled (default)"],"failure_modes":["Pipeline state stored locally by default — requires external state store for distributed execution","Sequential stage execution means no built-in parallelization across extract/normalize/load phases","Configuration resolution adds complexity when managing secrets across multiple environments","Automatic inference may infer overly permissive types (e.g., string instead of int) for sparse data","Schema evolution can create unexpected columns if source data is inconsistent","Destination-specific type mappings may lose precision (e.g., Python Decimal → float in some destinations)","CLI scaffolding generates boilerplate but requires customization for complex pipelines","Deployment to Airflow requires Airflow installation and configuration","Cloud function deployment is limited to specific cloud providers","Verified sources may not support all API features — customization required for advanced use cases","builder identity is not verified yet","no observed match outcomes 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