Migrate from Dagster to Mage Pro

Many teams start with Dagster for data orchestration—but soon encounter challenges with data engineering workflows, visual debugging, and developer productivity. Mage Pro offers a modern alternative: visual pipelines, built-in data integration, AI-assisted development, and UI-based deployment management—all within a unified platform for your data workflows.

Why migrate from Dagster to Mage Pro?

Dagster is a powerful orchestration tool focused on data assets, but it wasn’t designed for:

• Visual pipeline development and debugging
• Native data integration (ETL/ELT workflows)
• Mixing SQL and Python naturally in the same pipeline
• Streaming or event-driven data processing
• Built-in data previews and interactive development
• AI-powered pipeline development

Mage Pro is built to solve these challenges out-of-the-box, combining orchestration with data engineering capabilities in a modern, developer-friendly platform.

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Mage Pro vs Dagster: Benefits Overview

Mage Pro goes far beyond orchestration. It’s a unified platform for data integration, SQL modeling (native dbt blocks and Mage SQL blocks), AI-powered transformation, and streaming pipelines — all within a collaborative, Git-native workspace with UI-based deployment management.

Capability	Dagster	Mage Pro
Visual pipeline UI	⚠️ Code-only (Python decorators)	✅ Hybrid UI: drag-and-drop DAG builder + notebook-style coding experience
Built-in lineage	✅ Asset lineage	✅ Native, auto-generated data lineage
AI assistance	❌	✅ Generate, optimize, fix, and explain code with AI Sidekick
Multi-language support	⚠️ Python only	✅ SQL, Python, R, streaming, APIs
Data integration pipelines	⚠️ Custom Python or 3rd-party tools	✅ 200+ native connectors for databases, files, APIs
SQL block support	⚠️ Requires custom ops	✅ Native dbt blocks, or Mage SQL block, preview, and test support
Incremental modeling	⚠️ Requires custom logic	✅ Native in SQL block config and Data integration pipeline
Data previews & interactive runs	⚠️ Limited	✅ Preview data at every step, run blocks independently
Environment isolation	⚠️ Requires separate deployments	✅ Per-workspace configs, secrets, variables
Git integration	⚠️ Manual sync	✅ Git-backed version control and CI/CD, UI based Deployment App
Scheduling & triggers	✅ Cron + sensors	✅ Cron, events, file triggers, webhooks, API
Secrets management	⚠️ Requires Dagster Cloud or external config	✅ Support both built-in secret manager, or external secret manager
RBAC & SSO	⚠️ Dagster Cloud only	✅ Built-in, enterprise-ready
Observability & logging	✅ Dagster UI	✅ Native UI for logs, metrics, traces, lineage
Scalability	⚠️ Manual worker configuration	✅ Auto-scaled executors on K8s or Docker
Streaming support	❌	✅ Kafka, CDC, real-time ingestion
Team collaboration	⚠️ Limited multi-tenancy	✅ Workspaces, permissions, activity logs

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Step-by-Step Migration Instructions

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Mapping Dagster Concepts to Mage Pro

Assets & Jobs → Pipelines

Dagster:

from dagster import asset, job, op

@asset
def raw_data():
    return pd.read_csv("data.csv")

@asset
def transformed_data(raw_data):
    return raw_data.dropna()

@job
def my_etl_job():
    transformed_data(raw_data())

Mage Pro:

• Create a new pipeline in the UI
• Each asset/op becomes a block (data loader, transformer, or data exporter)
• Dependencies are defined by connecting blocks in the UI
• No need for explicit job definitions—pipeline structure defines execution

Assets & Ops → Blocks

Dagster:

@asset
def extract_data():
    return pd.read_csv("data.csv")

@op
def transform_data(context, df):
    return df.dropna()

@asset
def load_data(transformed_data):
    transformed_data.to_sql("table", engine)

Mage Pro:

• extract_data → Data loader block (Python or connector)
• transform_data → Transformer block (Python, SQL, or dbt block)
• load_data → Data exporter block (Python or connector)

Dagster Resources → Mage Blocks

Dagster Resource Type	Mage Pro Equivalent
S3Resource, GCSResource	Data loader/exporter blocks with native S3/GCS connectors
PostgresResource, SnowflakeResource	Data loader/exporter blocks with database connectors
DockerResource, K8sResource	Executor configuration in pipeline settings
Config resources	Workspace variables or pipeline variables

Partitions & Materializations

Dagster:

from dagster import DailyPartitionsDefinition, asset

daily_partitions = DailyPartitionsDefinition(start_date="2024-01-01")

@asset(partitions_def=daily_partitions)
def partitioned_asset(context):
    partition_date = context.partition_key
    # Process data for specific partition
    ...

Mage Pro:

• Use pipeline variables or dynamic blocks for partition logic
• Configure incremental processing in SQL blocks or Python blocks
• Use backfill triggers for historical data processing
• Schedule pipelines with cron expressions for partition-based runs

Scheduling & Sensors

Dagster:

from dagster import schedule, sensor, RunRequest

@schedule(cron_schedule="0 0 * * *", job=my_etl_job)
def daily_schedule(context):
    return RunRequest()

@sensor(job=my_etl_job)
def file_sensor(context):
    if file_exists("new_data.csv"):
        return RunRequest()

Mage Pro:

• Create a schedule trigger in the pipeline UI
• Configure cron expression, interval, or event-based triggers
• Use file triggers for file-based sensors
• Use event triggers for webhook-based sensors
• Use the Deployment App to manage deployments across environments (dev, staging, prod)

Secrets & Configuration

Dagster:

from dagster import ConfigurableResource

class MyResource(ConfigurableResource):
    api_key: str

@asset
def use_resource(my_resource: MyResource):
    key = my_resource.api_key

Mage Pro:

• Use workspace variables or pipeline variables
• Access via variables dictionary in blocks
• Built-in secret manager with encryption, or integrate with external secret managers (AWS Secrets Manager, HashiCorp Vault, etc.)
• Secrets are isolated per workspace and pipeline

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Convert Dagster Code with AI Sidekick

Skip the manual rewrites — Mage Pro’s AI Sidekick can automatically convert your Dagster asset and job code into a Mage pipeline with just one prompt.

How to Use It

1. Click the “Ask AI” button in the top-right corner of the Mage Pro UI.
2. Paste your Dagster code — including @asset, @op, @job, or asset-based workflows.
3. Ask: “Convert this Dagster job to a Mage pipeline.”
4. Sidekick will:
   • Parse the asset/op structure and dependencies
   • Generate the corresponding Mage blocks (data loader, transformer, data exporter)
   • Define block dependencies and scheduling logic
   • Convert Dagster resources to Mage connectors where applicable
   • Optimize the generated code for performance and best practices
5. Review and insert the generated pipeline directly into your project.

Why Use Sidekick?

• Faster migration: turn entire jobs into Mage pipelines in seconds
• Less error-prone: Sidekick understands asset dependencies, partitions, and op types
• Code optimization: automatically optimizes generated code for performance
• Context-aware: uses your project setup and prior block structure
• Fully editable: review, tweak, and insert blocks before saving

👉 Learn more in the AI Sidekick docs.

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Tips for Migrating Complex Jobs

Handling Dagster Asset Dependencies

Dagster:

@asset
def parent_asset():
    ...

@asset
def child_asset(parent_asset):
    ...

Mage Pro:

• Dependencies are automatically inferred from block connections in the UI
• Use upstream blocks to define data flow
• Blocks with no dependencies run in parallel automatically

Dagster Partitions

Dagster:

from dagster import DailyPartitionsDefinition

daily_partitions = DailyPartitionsDefinition(start_date="2024-01-01")

@asset(partitions_def=daily_partitions)
def partitioned_asset(context):
    partition_date = context.partition_key
    ...

Mage Pro:

• Use pipeline variables to pass partition information
• Configure incremental processing in SQL blocks
• Use backfill triggers to process historical partitions
• Schedule pipelines with appropriate cron expressions

Dagster Resources & Configuration

Dagster:

from dagster import ConfigurableResource, asset

class DatabaseResource(ConfigurableResource):
    connection_string: str

@asset
def use_database(database: DatabaseResource):
    conn = database.connection_string
    ...

Mage Pro:

• Use workspace variables or pipeline variables for configuration
• Access via variables dictionary in blocks
• Use data loader/exporter blocks with native connectors for databases
• Secrets are managed through built-in or external secret managers

Dagster Retries & Error Handling

Dagster:

@asset(retry_policy=RetryPolicy(max_retries=3, delay=60))
def risky_asset():
    ...

Mage Pro:

• Configure retry settings in block or pipeline configuration
• Set retry count, delay, and backoff strategy
• Built-in error handling and alerting

Dagster Caching & Materializations

Dagster:

@asset(
    compute_kind="pandas",
    metadata={"cache": True}
)
def cached_asset():
    ...

Mage Pro:

• Use block output caching in pipeline settings
• Configure cache expiration and invalidation rules
• Preview cached outputs in the UI

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After Migration: What You Get with Mage Pro

• Unified data platform: orchestration + data integration + transformation + streaming in one tool
• Visual pipeline builder with real-time data previews
• AI-powered block generation, optimization, and debugging (via AI Sidekick)
• Git-backed version control and CI/CD with UI-based Deployment App
• Built-in access controls, audit logs, and workspace isolation
• Support for SQL, Python, R, streaming, dbt blocks, Delta Lake, Iceberg, and more
• 200+ native connectors for databases, APIs, and cloud storage
• Streaming pipelines for real-time data processing
• Flexible secrets management: built-in secret manager or integrate with external systems (AWS Secrets Manager, Vault, etc.)
• Auto-scaled executors on Kubernetes or Docker
• Transparent pricing: usage-based pricing (SaaS) or cluster/workspace license-based pricing (self-hosted)

Dagster excels at asset-based orchestration. But when you need data engineering, visual development, AI assistance, and native data integration — Mage Pro is built for modern data teams.

👉 Migrate to Mage Pro Today