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What is Stratio?

A robust and secure data layer for businesses that provides immediate insights for both data and business teams is crucial for success. Stratio's generative AI data fabric encompasses the complete data management lifecycle, which includes data discovery, governance, utilization, and eventual disposal. In numerous organizations, data is often dispersed across various departments, with different applications used for specific functions. Utilizing the capabilities of AI, Stratio effectively identifies and accesses all your data, whether it is stored on-premises or in the cloud, ensuring appropriate data handling throughout the organization. Without the ability to visualize your data as it is generated, you risk falling behind in meeting customer demands. Traditional data infrastructures can take hours to process customer information, which can significantly impede responsiveness. In contrast, Stratio facilitates real-time access to the entirety of your data without the need for relocation, enabling prompt responses while preserving essential context. By merging operational and informational elements within a collaborative platform, organizations can enhance their capabilities to utilize instant extended AI for improved decision-making and agility. Ultimately, adopting such an integrated approach will not only help businesses stay competitive but also enable them to flourish in an increasingly data-centric environment. This shift towards a unified data strategy is essential for achieving long-term success.

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

Amazon Web Services (AWS)
Google Cloud Platform
Kubernetes
Microsoft Azure
PostgreSQL
ArangoDB
Calico Cloud
Ovvoe

Integrations Supported

Amazon Web Services (AWS)
Google Cloud Platform
Kubernetes
Microsoft Azure
PostgreSQL
Apache Airflow
Apache Spark
Coginiti
Collate
Databricks
Datadog
Microsoft Teams
MySQL
OpenMetadata
Papertrail
Twilio
pandas

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided

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

SaaS
On-Prem

Supported Platforms

SaaS
Windows
Mac
On-Prem
Linux

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

24 Hour Support
Web-Based Support

Training Options

Documentation Hub
On-Site Training

Training Options

Documentation Hub

Company Facts

Organization Name

Stratio

Date Founded

2014

Company Location

Spain

Company Website

www.stratio.com/home

Company Facts

Organization Name

Dagster Labs

Date Founded

2019

Company Location

United States

Company Website

dagster.io

Categories and Features

Data Fabric

Not specified

Data Integration

Not specified

Generative AI

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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