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What is Flyte?

Flyte is a powerful platform crafted for the automation of complex, mission-critical data and machine learning workflows on a large scale. It enhances the ease of creating concurrent, scalable, and maintainable workflows, positioning itself as a crucial instrument for data processing and machine learning tasks. Organizations such as Lyft, Spotify, and Freenome have integrated Flyte into their production environments. At Lyft, Flyte has played a pivotal role in model training and data management for over four years, becoming the preferred platform for various departments, including pricing, locations, ETA, mapping, and autonomous vehicle operations. Impressively, Flyte manages over 10,000 distinct workflows at Lyft, leading to more than 1,000,000 executions monthly, alongside 20 million tasks and 40 million container instances. Its dependability is evident in high-demand settings like those at Lyft and Spotify, among others. As a fully open-source project licensed under Apache 2.0 and supported by the Linux Foundation, it is overseen by a committee that reflects a diverse range of industries. While YAML configurations can sometimes add complexity and risk errors in machine learning and data workflows, Flyte effectively addresses these obstacles. This capability not only makes Flyte a powerful tool but also a user-friendly choice for teams aiming to optimize their data operations. Furthermore, Flyte's strong community support ensures that it continues to evolve and adapt to the needs of its users, solidifying its status in the data and machine learning landscape.

What is Dagster?

Dagster serves as a cloud-native open-source orchestrator that streamlines the entire development lifecycle by offering integrated lineage and observability features, a declarative programming model, and exceptional testability. This platform has become the preferred option for data teams tasked with the creation, deployment, and monitoring of data assets. Utilizing Dagster allows users to concentrate on executing tasks while also pinpointing essential assets to develop through a declarative methodology. By adopting CI/CD best practices from the outset, teams can construct reusable components, identify data quality problems, and detect bugs in the early stages of development, ultimately enhancing the efficiency and reliability of their workflows. Consequently, Dagster empowers teams to maintain a high standard of quality and adaptability throughout the data lifecycle.

Media

Media

Integrations Supported

Apache Spark
Dask
Databricks
Google Cloud Platform
Great Expectations
Kubernetes
MLflow
Slack
Snowflake
dbt
pandas
Hugging Face
PyTorch

Integrations Supported

Apache Spark
Dask
Databricks
Google Cloud Platform
Great Expectations
Kubernetes
MLflow
Slack
Snowflake
dbt
pandas
Airbyte
Apache Airflow
Collate
DataHub
Datadog
Tobiko
Twilio

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Version

Pricing Information

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

Supported Platforms

Mac
Linux

Supported Platforms

SaaS
Windows
Mac
On-Prem
Linux

Customer Service / Support

Web-Based Support

Customer Service / Support

24 Hour Support
Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Union.ai

Date Founded

2020

Company Location

United States

Company Website

flyte.org

Company Facts

Organization Name

Dagster Labs

Date Founded

2019

Company Location

United States

Company Website

dagster.io

Categories and Features

AI Automation

Not specified

AI/ML Model Training

Not specified

Machine Learning

Not specified

Workflow Automation

Not specified

Categories and Features

Data Fabric

Data Analytics
Data Collaboration
Data Lineage Tools
Data Networking / Connecting
Metadata Functionality

Data Management

Data Analysis
Data Integration
Data Migration
Match & Merge

Data Pipeline

Not specified

ETL

Data Analysis
Data Filtering
Job Scheduling

Machine Learning

Model Training
Predictive Modeling
Statistical / Mathematical Tools

ML Model Deployment

Not specified

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