Reliable Batch Data ETL Pipelines
Orchestrate complex batch extraction, transformation, and load workflows with dependency management.
Dagster
Data orchestration platform for developing, producing, and observing data pipelines
By Elementl
Data Orchestration
Orchestration
Data Pipelines
Workflow Management
Product Overview
Dagster is a modern data orchestration platform designed to enable teams to develop, schedule, and monitor reliable data pipelines. It facilitates building complex data workflows with strong typing, versioning, and robust testing capabilities. The platform integrates with diverse data tools and offers visibility into pipeline runs for collaboration and troubleshooting.
Dagster centralizes data workflow orchestration to help teams manage data pipelines with confidence and reliability. It provides rich metadata handling, configurable schedules and sensors, and orchestrates tasks across various environments and compute backends. Dagster’s APIs and UI allow users to easily debug, monitor, and evolve data processes to meet complex analytical and operational needs. With support for multi-step orchestrations and integrations across the data ecosystem, it empowers data engineers to maintain data quality and drive data-driven business outcomes.
Headquarters and Est. In
San Francisco, United States — Est. 2018
No. of Employees
51-200
Customer Demography
Global
Customer Domains
Technology
Finance
Retail
Healthcare
Use Case Deep Dive
Interactive analysis dashboard - explore detailed performance insights for key business scenarios
Event-Driven Data Processing
Trigger data pipelines automatically in response to data availability or system events.
Integrated Data Quality Checks
Embed validation within pipelines to maintain data integrity.
Multi-Cloud Pipeline Deployment
Deploy pipelines to run seamlessly across hybrid cloud environments.
Root Cause Analysis with Metadata and Logs
Simplify debugging of pipeline failures with consolidated metadata and logs.
Parameterizing Pipelines for Multiple Environments
Manage pipeline configurations for dev, staging, and production easily.
Backfill Management for Historical Data Reprocessing
Run pipelines retroactively to correct or fill missing data.
Pipeline Execution Parallelism Optimization
Optimize run times by executing independent tasks concurrently.
Integration with CI/CD for Pipeline Lifecycle Automation
Automate deployment and testing of pipeline code changes with CI/CD integrations.
Automated Incident Alerts and Notifications
Send real-time alerts for pipeline issues through multiple channels.
Use Case Details
Integration Details
Feature Details
Key Features
Explore the core capabilities that make Dagster stand out.
Pipeline Construction
Design complex data pipelines with modular components and strong typing.
Scheduler and Sensor Support
Manage automated pipeline execution schedules and event-driven triggers.
Rich Metadata Tracking
Capture and inspect detailed metadata and statistics throughout pipeline execution.
Type Checking and Validation
Utilize a robust type system for data flowing through pipelines.
Integrated Testing Framework
Test pipeline components locally with built-in framework support.
Monitoring and Alerts
Real-time monitoring of pipeline executions with customizable alerts.
Multi-Environment Deployment
Deploy and run pipelines across various compute backends and environments.
Versioned Data Lineage
Track data versions and lineage throughout pipeline runs.
GraphQL and Python APIs
Programmatically interact with Dagster using APIs.
Resource and Configuration Management
Define resources and configurations for pipeline components.
Task Parallelization and Concurrency
Execute independent pipeline tasks concurrently for performance.
Backfills and Historical Runs
Re-run pipeline segments for historical or missed data processing.
User Interface and Visualization
Web-based UI to visualize pipelines, runs, and logs.
Pluggable Executors
Support for different execution backends via executor plugins.
Integrations with Data Tools
Connect with external systems for data storage, compute, and orchestration.
Backpressure and Retry Policies
Configure retries and backoff for failed tasks to improve pipeline resilience.
Multi-Tenancy and Access Control
Manage user roles and permissions to secure access to pipelines and data.
Event-Driven Orchestration
Trigger pipelines based on external system events or file arrivals.
Integration with Workflow Orchestration Tools
Operate in tandem with tools like Apache Airflow for complex workflow management.
Logging and Audit Trails
Maintain detailed logs and history of pipeline operations for auditing.
Extensive SDK and CLI Tools
Robust tooling ecosystem for development and operations.
Support for Polyglot Pipelines
Integrate pipeline steps written in different languages or runtimes.
Stateful Pipeline Execution
Maintain execution state to support incremental data processing.
Community and Enterprise Support
Access to community resources or enterprise-grade support and features.
Contextual Integrations
Not just "integrates with" – here's the specific value each integration delivers:
Snowflake
Delivers: Cloud data warehousing platform integration for running data pipelines.
Amazon S3
Delivers: Integration with AWS S3 for data storage and event-driven pipelines.
Apache Airflow
Delivers: Orchestration tool integration for workflow management.
DBT
Delivers: Integration with DBT for version-controlled data transformation orchestration.
Slack
Delivers: Integration for alert notifications and team communication.
GitHub
Delivers: Version control and CI/CD integration for pipeline code management.
Resources
Latest insights, guides, and templates to accelerate your decisions.
Downloads
Downloads coming soon
Resources and templates will be available soon
Case Studies
How a Retailer Advanced Analytics with Dagster
Scaling Data Pipelines at a Financial Institution
Platform Updates
Platform updates coming soon
Latest updates and improvements will be shown here
Videos
Watch Dagster in action.
Dagster Overview and Demo
Building Data Pipelines with Dagster
Pricing & Plans
Open Source
Free
Cloud
Usage-based
Enterprise
Custom
Frequently Asked Questions
Common questions about Dagster:
Dagster is used for building, scheduling, and monitoring reliable data pipelines and workflows with strong typing and metadata tracking.
Yes, Dagster supports sensors that trigger pipelines based on external events, enabling event-driven data orchestration.
Dagster supports running pipelines locally, in the cloud, on Kubernetes, and integrates with other orchestration tools like Airflow.
Yes, it offers rich metadata tracking, logs, UI visualization, and integrated testing frameworks to simplify pipeline debugging.
Yes, Dagster is open-source with an active community and also provides enterprise editions with enhanced features.
Implementation Partners
Partners listed for Dagster and trusted teams available for implementation support.
No implementation partners are listed for this profile yet.
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