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Average Ratings 0 Ratings

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ease
features
design
support

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Write a Review

Description

Dagster is 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. It is the platform of choice data teams responsible for the development, production, and observation of data assets. With Dagster, you can focus on running tasks, or you can identify the key assets you need to create using a declarative approach. Embrace CI/CD best practices from the get-go: build reusable components, spot data quality issues, and flag bugs early.

Description

Managed Service for Apache Airflow is a cloud-based workflow orchestration service that simplifies the creation and management of complex data pipelines. Built on the open-source Apache Airflow framework, it allows users to define workflows using Python-based DAGs. The platform is fully managed, removing the need to provision or maintain infrastructure, which helps teams focus on pipeline development and execution. It integrates with a wide range of Google Cloud services, including BigQuery, Dataflow, Cloud Storage, and Managed Service for Apache Spark. The service supports hybrid and multi-cloud environments, enabling organizations to orchestrate workflows across different platforms. It offers advanced monitoring and troubleshooting tools, including visual workflow representations and logs. New features such as DAG versioning and improved scheduling enhance reliability and control. The platform also supports CI/CD pipelines and DevOps automation use cases. Its open-source foundation ensures flexibility and avoids vendor lock-in. Overall, it provides a powerful and scalable solution for managing data workflows and automation processes.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

APERIO DataWise Yes 
Apache Airflow Yes 
Google Cloud Platform Yes 
Amazon Web Services (AWS) Yes 
Azure Databricks Yes 
Azure Kubernetes Service (AKS) Yes 
Control-M No 
Dask Yes 
DataHub Yes 
Google Cloud AI Infrastructure No 
Google Cloud Datastore No 
Google Cloud Pub/Sub No 
Great Expectations Yes 
Jupyter Notebook Yes 
Microsoft Azure Yes 
MySQL Yes 
PagerDuty Yes 
PostgreSQL Yes 
Slack Yes 
Snowflake Yes 

Integrations

APERIO DataWise Yes 
Apache Airflow Yes 
Google Cloud Platform Yes 
Amazon Web Services (AWS) No 
Azure Databricks No 
Azure Kubernetes Service (AKS) No 
Control-M Yes 
Dask No 
DataHub No 
Google Cloud AI Infrastructure Yes 
Google Cloud Datastore Yes 
Google Cloud Pub/Sub Yes 
Great Expectations No 
Jupyter Notebook No 
Microsoft Azure No 
MySQL No 
PagerDuty No 
PostgreSQL No 
Slack No 
Snowflake No 

Pricing Details

$0
Pricing starts at $0.04/min for Serverless and $0.03/min for Hybrid, and tapers down from there based on usage.
Free Trial Yes 
Free Version Yes 

Pricing Details

$0.074 per vCPU hour
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) Yes 
Online Support Yes 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Dagster Labs

Founded

2019

Country

United States

Website

dagster.io

Vendor Details

Company Name

Google

Founded

1998

Country

United States

Website

cloud.google.com/products/managed-service-for-apache-airflow

Product Features

Data Fabric

Data Access Management No 
Data Analytics Yes 
Data Collaboration Yes 
Data Lineage Tools Yes 
Data Networking / Connecting Yes 
Metadata Functionality Yes 
No Data Redundancy No 
Persistent Data Management No 

Data Management

Customer Data No 
Data Analysis Yes 
Data Capture No 
Data Integration Yes 
Data Migration Yes 
Data Quality Control No 
Data Security No 
Information Governance No 
Master Data Management No 
Match & Merge Yes 

ETL

Data Analysis Yes 
Data Filtering Yes 
Data Quality Control No 
Job Scheduling Yes 
Match & Merge No 
Metadata Management No 
Non-Relational Transformations No 
Version Control No 

Machine Learning

Deep Learning No 
ML Algorithm Library No 
Model Training Yes 
Natural Language Processing (NLP) No 
Predictive Modeling Yes 
Statistical / Mathematical Tools Yes 
Templates No 
Visualization No 

Product Features

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