StarTree Features

Facilitates analysis of high-volume, real-time data.

Allows user to query data through query languages like SQL.

Aligns processing and distribution workflows on top of Apache Hadoop

Aligns processing and distribution workflows on top of Apache Spark

Integrates data from multiple external databases.

Facilitates the dissemination of collected big data throughout parallel computing clusters.

Processes data and represents interpretations in a variety of graphic formats.

Strings together specific functions and datasets to automate analytics iterations.

Allows big data tool to run and record data within external applications.

Use notebooks for tasks such as creating dashboards with predefined, scheduled queries and visualizations

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.

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.

Stores data tables as columns.

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

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.

Stores frequently-used data in system memory quickly.

Provides support to store, index and query data in more than one format.

Aids in faster processing of query to obtain results.

Enables the user to track the time taken for data to be generated and available for processing

Aids in monitoring and reporting data pipeline performance.

Manages in building pipelines between two protocols.

Allows continuous data ingestion directly from the data source.

Aids the database in scaling horizontally and vertically in petabytes.

Helps store the data in fixed size segments called vectors for better management.

Enables ingesting data efficiently and securely.

Allows concurrent access to data without the use of any table locks.