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What is DBOS?

The DBOS operating system presents a pioneering and enhanced method for creating fault-tolerant cloud applications, revolutionizing the field of cloud-native architecture. This innovative platform is the result of three years of joint open-source research and development conducted by MIT and Stanford, paving the way for a new era in cloud-native solutions. By utilizing a relational database, DBOS effectively simplifies the complex application stacks that are prevalent in today's technology landscape. It forms the foundation of DBOS Cloud, a transactional serverless platform designed to guarantee fault tolerance, observability, cyber resilience, and easy deployment for stateful TypeScript applications. Built on a distributed database management system, the services provided by the operating system offer integrated transactional and fault-tolerant state management, thereby reducing complexity by removing the need for containers, cluster management, or intricate workflow orchestration. This approach ensures exceptional scalability, impressive performance, and reliable availability, with the added benefit of storing metrics, logs, and traces in SQL-accessible tables for efficient data management. Furthermore, the architecture is designed to minimize potential cyber attack vulnerabilities, includes mechanisms for self-detection of cyber threats, and bolsters overall cyber resilience, establishing it as a strong candidate for contemporary cloud applications. Consequently, the DBOS operating system marks a remarkable advancement in the simplification of cloud application development while maintaining high standards of security and reliability, ultimately fostering innovation in the cloud computing space.

What is Apache Flume?

Flume serves as a powerful service tailored for the reliable, accessible, and efficient collection, aggregation, and transfer of large volumes of log data across distributed systems. Its design is both simple and flexible, relying on streaming data flows that provide robustness and fault tolerance through multiple reliability and recovery strategies. The system features a straightforward and extensible data model, making it well-suited for online analytical applications. The Apache Flume team is thrilled to announce the launch of Flume 1.8.0, which significantly boosts its capacity to handle extensive streaming event data effortlessly. This latest version promises enhanced performance and improved efficiency in the management of data flows, ultimately benefiting users in their data handling processes. Furthermore, this update reinforces Flume's commitment to evolving in response to the growing demands of data management in modern applications.

Media

Media

Integrations Supported

Amazon Web Services (AWS)
Apache Kafka
Apache Phoenix
Observo AI
Onum
Slack
TypeScript
Yandex Data Proc

Integrations Supported

Amazon Web Services (AWS)
Apache Kafka
Apache Phoenix
Observo AI
Onum
Slack
TypeScript
Yandex Data Proc

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

DBOS

Company Location

United States

Company Website

www.dbos.dev/

Company Facts

Organization Name

Apache Software Foundation

Date Founded

1999

Company Location

United States

Company Website

flume.apache.org

Categories and Features

Serverless

API Proxy
Application Integration
Data Stores
Developer Tooling
Orchestration
Reporting / Analytics
Serverless Computing
Storage

Categories and Features

Data Warehouse

Ad hoc Query
Analytics
Data Integration
Data Migration
Data Quality Control
ETL - Extract / Transfer / Load
In-Memory Processing
Match & Merge

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