ScyllaDB Features

Functions when integrated with multiple forms of external applications.

Allows admisistrators to manage and organize storage, networks, and datacenters.

Monitors performance and statistics related to memory, caches and connections.

Alerts users when unauthorized parties or malware is detected allowing administrators to remedy the issue.

Secures data at rest and in transit from unauthorized access.

Allows administrators to organize components of the database to maintain instance efficiency.

Allows the creating and relinquishing of database access credentials.

Documents access and alterations of the database for analytics and reporting.

Expands functionality while maintaining balanced loads. Serves growing demand without reducing functionality.

The abillity to restore files to its original loacation or alternative sources on-demand.

Stores historical records of information for reverting or reference.

Helps users navigate databases by labeling and organizing datasets.

Implements auto horizontal data partitioning that allows storing data on more than one node to scale out.

Restores a database to a correct (consistent) state in the event of a failure.

Copy data across multiple servers through master-slave, peer-to-peer replication architecture etc.

Data masking provides an additional layer of access control that can be applied to tables and views.

Verifies identity of database users.

Allows users to gain statistically valid insights from their data while protecting the privacy of individuals.

Provides predefined system roles, privileges, and user-defined roles to users.

Allows integration with external security mechanisms like Kerberos, LDAP authentication etc.

Provides an audit log to track access and operations performed on databases for regulatory compliance.

Provides encryption capability for all the data at rest using encryption keys.

Functionality that provides encryption without the loss information between the related data items.

Helps in improving the availability of data.

Uses machine learning algorithms to gain actionable insights.

Stores data as key-value pairs where key is a unique identifier.

Supports multiple data types like lists, sets, hashes (similar to map), sorted sets etc.

Stores frequently-used data in system memory quickly.

Provides support to handle structured, semi-structured, and unstructured data with equal effect.

Available on multiple operating systems like Linux, Windows, MacOS etc.

Supports various types of data like String, Boolean and integer

Supports database on various operating systems

Aids in supporting multiple models of database

Functionality provided to store data in column families

Stores data across multiple servers.

Feature that aids in data partitioning resulting into scaling of data

Makes informed choices based on available data and objectives