Dagster Integrations Index

Navigate 82+ Dagster integrations organized by Dagster’s official taxonomy. Find AI/ML tools, ETL platforms, data storage, compute services, BI tools, and monitoring integrations.

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Quick Reference by Category

Category Taxonomy

This index aligns with Dagster’s official documentation taxonomy from tags.yml:

• ai: Artificial intelligence and machine learning integrations (LLM APIs, experiment tracking)
• etl: Extract, transform, and load tools including data replication and transformation frameworks
• storage: Databases, data warehouses, object storage, and table formats
• compute: Cloud platforms, container orchestration, and distributed processing frameworks
• bi: Business intelligence and visualization platforms
• monitoring: Observability platforms and metrics systems for tracking performance
• alerting: Notification and incident management systems for pipeline alerts
• testing: Data quality validation and testing frameworks
• other: Miscellaneous integrations including DataFrame libraries

Note: Support levels (dagster-supported, community-supported) are shown inline in each integration entry.

Last verified: 2026-01-27

Finding the Right Integration

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Load data from external sources

• SaaS applications → ETL (Fivetran, Airbyte)
• Files/databases → ETL (dlt, Sling, Meltano)
• Cloud storage → Storage (S3, GCS, Azure Blob)

Transform data

• SQL transformations → ETL (dbt)
• Distributed transformations → ETL (PySpark)
• DataFrame operations → Other (Pandas, Polars)
• Large-scale processing → Compute (Spark, Dask, Ray)

Store data

• Cloud data warehouse → Storage (Snowflake, BigQuery, Redshift)
• Relational database → Storage (Postgres, MySQL)
• File/object storage → Storage (S3, GCS, Azure, LakeFS)
• Analytics database → Storage (DuckDB)
• Vector embeddings → Storage (Weaviate, Chroma, Qdrant)

Validate data quality

• Schema validation → Testing (Pandera)
• Quality checks → Testing (Great Expectations)

Run ML workloads

• LLM integration → AI (OpenAI, Anthropic, Gemini)
• Experiment tracking → AI (MLflow, W&B)
• Distributed training → Compute (Ray, Spark)

Execute computation

• Cloud compute → Compute (AWS, Azure, GCP, Databricks)
• Containers → Compute (Docker, Kubernetes)
• Distributed processing → Compute (Spark, Dask, Ray)

Monitor pipelines

• Team notifications → Alerting (Slack, MS Teams, PagerDuty)
• Metrics tracking → Monitoring (Datadog, Prometheus)
• Log aggregation → Monitoring (Papertrail)

Visualize data

• BI dashboards → BI (Looker, Tableau, PowerBI)
• Analytics platform → BI (Sigma, Hex, Evidence)

Integration Categories

AI & ML

Artificial intelligence and machine learning platforms, including LLM APIs and experiment tracking.

Key integrations:

• OpenAI – GPT models and embeddings API
• Anthropic – Claude AI models
• Gemini – Google’s multimodal AI
• MLflow – Experiment tracking and model registry
• Weights & Biases – ML experiment tracking
• NotDiamond – LLM routing and optimization

See references/ai.md for all AI/ML integrations.

ETL/ELT

Extract, transform, and load tools for data ingestion, transformation, and replication.

Key integrations:

• dbt – SQL-based transformation with automatic dependencies
• Fivetran – Automated SaaS data ingestion (component-based)
• Airbyte – Open-source ELT platform
• dlt – Python-based data loading (component-based)
• Sling – High-performance data replication (component-based)
• PySpark – Distributed data transformation
• Meltano – ELT for the modern data stack

See references/etl.md for all ETL/ELT integrations.

Storage

Data warehouses, databases, object storage, vector databases, and table formats.

Key integrations:

• Snowflake – Cloud data warehouse with IO managers
• BigQuery – Google’s serverless data warehouse
• DuckDB – In-process SQL analytics
• Postgres – Open-source relational database
• Weaviate – Vector database for AI search
• Delta Lake – ACID transactions for data lakes
• DataHub – Metadata catalog and lineage

See references/storage.md for all storage integrations.

Compute

Cloud platforms, container orchestration, and distributed processing frameworks.

Key integrations:

• AWS – Cloud compute services (Glue, EMR, Lambda)
• Databricks – Unified analytics platform
• GCP – Google Cloud compute (Dataproc, Cloud Run)
• Spark – Distributed data processing engine
• Dask – Parallel computing framework
• Docker – Container execution with Pipes
• Kubernetes – Cloud-native orchestration
• Ray – Distributed computing for ML

See references/compute.md for all compute integrations.

BI & Visualization

Business intelligence and visualization platforms for analytics and reporting.

Key integrations:

• Looker – Google’s BI platform
• Tableau – Interactive dashboards
• PowerBI – Microsoft’s BI tool
• Sigma – Cloud analytics platform
• Hex – Collaborative notebooks
• Evidence – Markdown-based BI
• Cube – Semantic layer platform

See references/bi.md for all BI integrations.

Monitoring

Observability platforms and metrics systems for tracking pipeline performance.

Key integrations:

• Datadog – Comprehensive observability platform
• Prometheus – Time-series metrics collection
• Papertrail – Centralized log management

See references/monitoring.md for all monitoring integrations.

Alerting

Notification and incident management systems for pipeline alerts.

Key integrations:

• Slack – Team messaging and alerts
• PagerDuty – Incident management for on-call
• MS Teams – Microsoft Teams notifications
• Twilio – SMS and voice notifications
• Apprise – Universal notification platform
• DingTalk – Team communication for Asian markets

See references/alerting.md for all alerting integrations.

Testing

Data quality validation and testing frameworks for ensuring data reliability.

Key integrations:

• Great Expectations – Data validation with expectations
• Pandera – Statistical data validation for DataFrames

See references/testing.md for all testing integrations.

Other

Miscellaneous integrations including DataFrame libraries and utility tools.

Key integrations:

• Pandas – In-memory DataFrame library
• Polars – Fast DataFrame library with columnar storage

See references/other.md for other integrations.

References

Integration details are organized in the following files:

• AI & ML: references/ai.md – AI and ML platforms, LLM APIs, experiment tracking
• ETL/ELT: references/etl.md – Data ingestion, transformation, and replication tools
• Storage: references/storage.md – Warehouses, databases, object storage, vector DBs
• Compute: references/compute.md – Cloud platforms, containers, distributed processing
• BI & Visualization: references/bi.md – Business intelligence and analytics platforms
• Monitoring: references/monitoring.md – Observability and metrics systems
• Alerting: references/alerting.md – Notifications and incident management
• Testing: references/testing.md – Data quality and validation frameworks
• Other: references/other.md – DataFrame libraries and miscellaneous tools

Using Integrations

Most Dagster integrations follow a common pattern:

1. Install the package:

   pip install dagster-<integration>
   

2. Import and configure a resource:

   from dagster_<integration> import <Integration>Resource
   
   resource = <Integration>Resource(
       config_param=dg.EnvVar("ENV_VAR")
   )
   

3. Use in your assets:

   @dg.asset
   def my_asset(integration: <Integration>Resource):
       # Use the integration
       pass
   

For component-based integrations (dbt, Fivetran, dlt, Sling), see the specific reference files for scaffolding and configuration patterns.