dlt (data load tool)
Overview
dlt (data load tool) is an open-source Python library that makes building data pipelines simple and maintainable. Created by dltHub in 2022, dlt takes a code-first approach to data ingestion, allowing you to build reliable, scalable pipelines with just Python functions.
What is dlt?
dlt is a Python library for building data pipelines that handles the heavy lifting of data extraction, schema management, incremental loading, and data quality.
Core Philosophy:
- Code over config: Write Python, not YAML
- Automatic schema inference: dlt discovers your data structure
- Incremental by default: Only load new/changed data
- Developer-friendly: Pythonic API, works with your existing tools
- Production-ready: Built-in observability, error handling, retries
What dlt Does:
What dlt handles automatically:
- ✅ Schema inference and evolution
- ✅ Incremental loading (only new data)
- ✅ Data type conversion
- ✅ Normalization and flattening
- ✅ Error handling and retries
- ✅ State management
- ✅ Data quality checks
Why Use dlt?
The Problem dlt Solves
Before dlt:
With dlt:
Key Benefits
- Rapid Development: Build pipelines in minutes, not days
- Automatic Schema Management: Schema inference and evolution
- Incremental Loading: Built-in state tracking
- Pythonic: Familiar Python syntax, integrates with existing code
- Flexible: Works with APIs, databases, files, custom sources
- Production-Ready: Observability, retries, error handling included
When to Use dlt
✅ Perfect For:
- Building custom data pipelines from APIs
- Extracting data from databases
- Loading data from files (JSON, CSV, Parquet)
- Incremental data synchronization
- Rapid prototyping of data pipelines
- Python-first data teams
- ELT workflows (extract, load, transform with dbt)
❌ Not Ideal For:
- Real-time streaming (use Kafka, Flink)
- Orchestration (use Airflow, Prefect, Dagster)
- No-code requirements (use Fivetran, Airbyte)
- Pure SQL transformations (use dbt)
dlt vs Alternatives
| Feature | dlt | Fivetran | Airbyte | Custom Scripts |
|---|---|---|---|---|
| Cost | Free (OSS) | $$$ | Free (OSS) | Free |
| Setup Time | Minutes | Minutes | Hours | Days |
| Flexibility | High | Low | Medium | Highest |
| Code-First | ✓ | ✗ | ✗ | ✓ |
| Maintenance | Low | None | Medium | High |
| Custom Sources | Easy | Hard | Medium | Easy |
| Best For | Python teams | Non-technical | Self-host | Complex logic |
Core Concepts
1. Sources
Source: A Python function that yields data.
Key Points:
- Sources are Python generators or iterators
- Can yield dicts, lists, or Pandas DataFrames
- dlt handles pagination, retries, state automatically
2. Resources
Resource: Individual data entities within a source (tables).
Write Dispositions:
replace: Truncate and reload (full refresh)append: Insert all records (duplicates possible)merge: Upsert based on primary key (deduplicate)
3. Pipelines
Pipeline: Manages the flow from source to destination.
4. Destinations
Destination: Where dlt loads your data.
Supported Destinations:
- Warehouses: Snowflake, BigQuery, Redshift, Synapse
- Databases: PostgreSQL, DuckDB, MotherDuck
- Lakes: Databricks, Filesystem (Parquet)
- Others: ClickHouse, Weaviate (vector DB)
Configuration:
5. Schema
Schema: Automatically inferred from your data, but customizable.
Automatic Inference:
Custom Schema:
Schema Evolution:
6. Incremental Loading
Incremental Loading: Only load new/changed data.
Incremental Strategies:
- Append: Track last seen value (timestamp, ID)
- Merge: Upsert based on primary key + incremental key
- Custom: Implement your own state management
7. Transformations
Transformations: Modify data before loading.
Use dbt for complex transformations:
Architecture
How dlt Works
Pipeline Execution
-
Extract Phase:
- Call source functions
- Yield data (streaming, not all in memory)
- Track state for incremental loading
-
Normalize Phase:
- Infer schema from data
- Convert types
- Flatten nested structures
- Handle schema evolution
-
Load Phase:
- Create/update tables
- Batch insert data
- Handle retries and errors
- Update state
State Management
Common Patterns
REST API Integration
Database Replication
File Processing
Custom Source with Pagination
Integration with Modern Data Stack
dlt + dbt
Perfect combination: dlt for EL (extract, load), dbt for T (transform)
Why this works:
- dlt: Gets data into warehouse quickly
- dbt: Transforms data with SQL (version-controlled, tested)
- Separation of concerns
dlt + Airflow
Orchestrate dlt pipelines with Airflow:
dlt + Dagster
Verified Sources
dlt provides 50+ pre-built, verified sources:
Popular Verified Sources:
- SaaS: GitHub, Slack, Notion, Asana, Zendesk
- Databases: PostgreSQL, MySQL, MongoDB
- Marketing: Google Analytics, Facebook Ads, Google Ads
- CRM: Salesforce, HubSpot, Pipedrive
- Ecommerce: Shopify, Stripe
- Data: REST API (generic), SQL Database (generic)
When Not to Use dlt
- Real-time streaming: Use Kafka, Flink, Spark Streaming
- Orchestration: dlt doesn't schedule; use Airflow, Prefect, Dagster
- No-code required: Use Fivetran, Airbyte UI
- Complex transformations: Use dbt (or dlt + dbt)
- Managed connectors preferred: Use Fivetran (less maintenance)
Getting Help
- Documentation: https://dlthub.com/docs/
- GitHub: https://github.com/dlt-hub/dlt
- Slack: https://dlthub.com/community
- Discord: Active community for Q&A
- Stack Overflow: Tag
dlt
Ready to build pipelines with dlt? Check out:
- Getting Started Guide - Install dlt and run your first pipeline
- Use Cases - Real-world dlt scenarios
- Best Practices - Production patterns
- Tutorials - Hands-on dlt projects