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StackStorm VS Dagster

Compare StackStorm VS Dagster and see what are their differences

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Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

StackStorm logo StackStorm

StackStorm is a powerful open-source automation platform that wires together all of your apps...

Dagster logo Dagster

The cloud-native open source orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability.
  • StackStorm Landing page
    Landing page //
    2023-07-27
  • Dagster Landing page
    Landing page //
    2023-03-22

StackStorm features and specs

  • Open Source
    StackStorm is an open-source automation platform, which means it is cost-effective as there are no licensing fees. Users can contribute to its development and customization.
  • Event-Driven Automation
    It supports event-driven automation, allowing users to automate complex workflows that can be triggered by events from numerous sources.
  • Flexible and Extensible
    StackStorm provides a high level of flexibility and extensibility through integrations and customizable actions, allowing it to fit various automation needs.
  • Active Community
    There is an active community supporting StackStorm, which can be beneficial for troubleshooting, sharing best practices, and seeking advice from other users.
  • Self-Service Automation
    With its GUI and command-line tools, StackStorm enables self-service automation, empowering users to initiate and manage automation tasks without deep technical expertise.

Possible disadvantages of StackStorm

  • Complex Setup
    The initial setup and configuration of StackStorm can be complex and time-consuming, requiring a good understanding of the system and its dependencies.
  • Steeper Learning Curve
    Users may experience a steep learning curve due to the wide range of features and the requirement for understanding underlying technologies like Python and YAML.
  • Dependency Management
    Managing dependencies and ensuring compatibility between different integrations and components can be challenging and require continual maintenance.
  • Resource Intensive
    Running StackStorm can be resource-intensive, which might require additional infrastructure or cloud resources, especially for large-scale deployments.
  • Limited Support
    While there is a community for support, professional support options may be limited compared to proprietary solutions, which might be a concern for enterprises needing guaranteed, reliable support.

Dagster features and specs

  • Modular Design
    Dagster's modular architecture allows users to build reusable components, known as Solids and Dagsters, which promote organized and maintainable code.
  • Type Safety
    Dagster offers strong type safety, enabling users to define input and output types for all computations, reducing runtime errors and improving code reliability.
  • Integrated Scheduler
    Dagster includes a built-in scheduler, allowing for seamless workflow automation and easy management of recurring data processing jobs.
  • Rich Metadata
    Dagster provides extensive metadata for tracking the flow and results of data jobs, aiding in debugging and improving transparency in pipeline execution.
  • Interoperability
    The platform supports integrations with various tools, including Pandas, Spark, and dbt, enhancing its capability to work across different data ecosystems.
  • User Interface
    Dagster features a sophisticated web-based UI for visualizing pipelines and monitoring job runs, which enhances user experience and accessibility.

Possible disadvantages of Dagster

  • Learning Curve
    New users may find the framework's concepts and structure complex, leading to a steeper learning curve compared to simpler orchestration tools.
  • Limited Community Support
    Compared to more established tools, Dagster's community is smaller, potentially leading to less available third-party resources or slower responses to issues.
  • Integration Complexity
    While Dagster offers many integrations, configuring them can be complex and sometimes requires a deep understanding of both Dagster and the external tools.
  • Evolving Platform
    Being a relatively newer platform, Dagster is still evolving, which might lead to breaking changes or instability as it matures.

StackStorm videos

+ Add

StackStorm 101 (v0.12)

More videos:

  • Review - Recovery from OpenStack Failures using Nagios and StackStorm

Dagster videos

+ Add

Airflow Vs. Dagster: The Full Breakdown!

More videos:

  • Review - Dagster Data Orchestration 10 min walkthrough
  • Review - Apache Airflow vs. Dagster

Category Popularity

0-100% (relative to StackStorm and Dagster)

StackStorm

Dagster

Automation
100 100%
Automation
0% 0
Utilities
0 0%
Utilities
100% 100
Workflow Automation
100 100%
Workflow Automation
0% 0
Data Integration
0 0%
Data Integration
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare StackStorm and Dagster

StackStorm Reviews

We have no reviews of StackStorm yet.
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Dagster Reviews

5 Airflow Alternatives for Data Orchestration
Dagster is an open-source data orchestration system that allows users to define their data assets as Python functions. Once defined, Dagster manages and executes these functions based on a user-defined schedule or in response to specific events. Dagster can be used at every stage of the data development lifecycle, from local development and unit testing to integration...
Source: www.kdnuggets.com
Top 8 Apache Airflow Alternatives in 2024
Unlike Airflow, which supports any production environment, Dagster concentrates on cloud services and supports modern data stacks. Being cloud-native and container-native, this solution makes the scheduling and execution processes easier. Dagster was created with such specific goals in mind: designing ETL data pipelines, implementing machine learning curves, and managing...
Source: blog.skyvia.com
10 Best Airflow Alternatives for 2024
Dagster is a Machine Learning, Analytics, and ETL Data Orchestrator. Since it handles the basic function of scheduling, effectively ordering, and monitoring computations, Dagster can be used as an alternative or replacement for Airflow (and other classic workflow engines).
Source: hevodata.com

Social recommendations and mentions

Based on our record, Dagster seems to be more popular. It has been mentiond 6 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

StackStorm mentions (0)

We have not tracked any mentions of StackStorm yet. Tracking of StackStorm recommendations started around Mar 2021.

Dagster mentions (6)

  • Automating Data Quality Checks: A Practical Guide Using Dagster and Great Expectations
    At my organization, which collects large volumes of public web data, we’ve developed a robust system for automated data quality checks using two powerful open-source tools: Dagster and Great Expectations. These tools are the cornerstone of our approach to data quality management, allowing us to efficiently validate and monitor our data pipelines at scale. - Source: dev.to / 11 months ago
  • Data Orchestration Tool Analysis: Airflow, Dagster, Flyte
    Data orchestration tools are key for managing data pipelines in modern workflows. When it comes to tools, Apache Airflow, Dagster, and Flyte are popular tools serving this need, but they serve different purposes and follow different philosophies. Choosing the right tool for your requirements is essential for scalability and efficiency. In this blog, I will compare Apache Airflow, Dagster, and Flyte, exploring... - Source: dev.to / over 1 year ago
  • Data Engineering with DLT and REST
    This article demonstrates how to work with near real-time and historical data using the dlt package. Whether you need to scale data access across the enterprise or provide historical data for post-event analysis, you can use the same framework to provide customer data. In a future article, I'll demonstrate how to use dlt with a workflow orchestrator such as Apache Airflow or Dagster.``. - Source: dev.to / almost 2 years ago
  • How I've implemented the Medallion architecture using Apache Spark and Apache Hdoop
    Instead of the custom orchestrator I used, a proper orchestration tool should replace it like Apache Airflow, Dagster, ..., etc. - Source: dev.to / about 2 years ago
  • AI Strategy Guide: How to Scale AI Across Your Business
    Level 1 of MLOps is when you've put each lifecycle stage and their intefaces in an automated pipeline. The pipeline could be a python or bash script, or it could be a directed acyclic graph run by some orchestration framework like Airflow, dagster or one of the cloud-provider offerings. AI- or data-specific platforms like MLflow, ClearML and dvc also feature pipeline capabilities. - Source: dev.to / over 2 years ago
View more

What are some alternatives?

When comparing StackStorm and Dagster, you can also consider the following products

Apache Airflow - Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.

RunDeck - RunDeck is an open source automation service with a web console, command line tools and a WebAPI.

Prefect.io - Prefect offers modern workflow orchestration tools for building, observing & reacting to data pipelines efficiently.

Alloy Automation - Launch SaaS integrations faster

Luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs.

ifttt - IFTTT puts the internet to work for you. Create simple connections between the products you use every day.

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