Neo4j Graph Database 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.

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.

Encrypts and transforms data at the database from a readable state into a ciphertext of unreadable characters.

Allows restricted user acess to modify depending on the access level.

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.

Helps in improving the availability of data.

Uses machine learning algorithms to gain actionable insights.

Designed to store interconnected data consisting of nodes and edges.

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

Allows users to index at ingest and query endlessly across data.

Notifies specific events like document inserts, updates, replaces, deletes etc and responds in real-time.

Powerful query language that allows users to access patterns like traversals, JOINs, search, geospatial etc.

Allows data movement from one database to another.

Provides data backup and recovery functionality to protect and restore a database.

Allows users to access and work on data concurrently, supporting several views of the data.

Stores the database metadata, that is the definitions of data elements, types, relationships etc.

Creates a copy of the database to maintain consistency and integrity.

Allows users to create, update and retrieve data in a database.

Defines the logical design of the data before building the schemas.

Monitors and analyzes critical database attributes like query performance, user sessions, dead lock detail, system errors etc and visualize them on a custom dashboard.

Helps interpret SQL queries and determine the fastest method of execution

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

Allows users to connect business intelligence tools to the database.

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