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What is ScyllaDB?

ScyllaDB is an exemplary database solution tailored for applications that require exceptional performance and low latency, specifically addressing the needs of data-heavy operations. It enables teams to leverage the increasing processing power of contemporary infrastructures, effectively eliminating barriers to scaling as data volumes grow. Unlike traditional database systems, ScyllaDB is a distributed NoSQL database that ensures complete compatibility with both Apache Cassandra and Amazon DynamoDB, while also featuring innovative architectural advancements that enhance user experience at significantly lower costs. More than 400 pioneering companies, such as Disney+ Hotstar, Expedia, FireEye, Discord, Zillow, Starbucks, Comcast, and Samsung, depend on ScyllaDB to meet their complex database challenges. In addition to its robust capabilities, ScyllaDB is available in multiple formats, including a free open-source edition, a fully-supported enterprise version, and a managed database-as-a-service (DBaaS) that operates across various cloud platforms, providing flexibility to suit a wide array of user requirements. This adaptability not only positions ScyllaDB as a leading choice but also encourages organizations to enhance their database performance and efficiency in an increasingly data-driven landscape.

What is Quasar AI?

Quasar is an advanced analytics infrastructure platform built to handle high-cardinality numerical data at scale for AI-driven systems. It is designed to process data from sources such as sensors, telemetry streams, financial trades, and large-scale simulations. Traditional data architectures often rely on a combination of warehouses, pipelines, and data lakes, which introduce latency, high costs, and operational complexity. Quasar replaces this fragmented approach with a unified distributed system optimized for continuous data ingestion and analysis. The platform features specialized numerical compression, enabling efficient storage and faster processing of massive datasets. Its deterministic query execution ensures reliable and consistent analytics outcomes. Quasar also supports distributed clustering, allowing it to scale seamlessly under sustained data pressure. By eliminating multi-stage pipelines, it reduces latency and simplifies infrastructure management. The platform offers predictable performance and stable costs through its flat pricing model. It is particularly valuable for industries such as manufacturing, finance, and scientific simulations that generate large volumes of numerical data. Quasar enables real-time insights and high-resolution analytics without compromising performance. Overall, it empowers organizations to build scalable, efficient, and cost-effective data infrastructure for modern AI and analytics workloads.

Media

Media

Integrations Supported

Apache Kafka
Apache Pulsar
Datadog
Depot
Elestio
Grafana Cloud
Hackolade
Prometheus
Wireshark

Integrations Supported

Apache Cordova
Microsoft Excel
RadSystems
SQL
TimeXtender

API Availability

Has API

API Availability

Pricing Information

Pricing not provided
Free Version
Free Trial Offered?

Pricing Information

Pricing not provided

Supported Platforms

SaaS
Linux

Supported Platforms

SaaS
Windows
Mac

Customer Service / Support

24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub

Company Facts

Organization Name

ScyllaDB

Date Founded

2014

Company Location

United States

Company Website

www.scylladb.com/product/

Company Facts

Organization Name

QuasarDB

Date Founded

2008

Company Location

France

Company Website

quasar.ai/

Categories and Features

Database

Backup and Recovery
Data Migration
Monitoring
NOSQL

Distributed Databases

Not specified

In-Memory Databases

Not specified

Key-Value Databases

Not specified

NoSQL Database

Auto-sharding
Automatic Database Replication
Data Model Flexibility
Deployment Flexibility
Dynamic Schemas
Integrated Caching
Multi-Model
Performance Management
Security Management

OLAP Databases

Not specified

Categories and Features

AI Infrastructure

Not specified

Database

Data Replication
Data Security
Database Conversion
Mobile Access
NOSQL
Performance Analysis
Queries

In-Memory Databases

Not specified

Query Engines

Not specified

Time Series Databases

Not specified

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