Tiger Data Features

Based on 10 Tiger Data reviews.

As reported in 10 Tiger Data reviews.

Provides tools for building, deploying, or integrating source code into hosted application.

Provides tools for writing code, editing code, syntax, debugging, or utilizing frameworks.

Provides tools for sharing code, tracking progress, and shipping software.

Supports programming languages such as Java, C, or Python. Support for front-end languages such as HTML, CSS, and JavaScript.

Tests the functionality of a software application. Detects and diagnoses issues in application.

Lets developers manage database services. Provides support for different types of databases and integration methods.

Lets users analyze deployment, user, storage, and business metrics.

Provides tools or bots that scale services and data automatically or on demand.

Stores versions of application and data or incrementally backs up data.

Provides scalable cloud storage for different forms of data. Store analytics and records for reporting.

Allows users to provision networks, deliver content, balance loads, and manage traffic.

Provides virtual networks and operating systems.

Secures applications, encrypt data, and manage identities.

As reported in 15 Tiger Data reviews. 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. This feature was mentioned in 15 Tiger Data reviews.

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. 13 reviewers of Tiger Data have provided feedback on this feature.

Based on 11 Tiger Data reviews. 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. This feature was mentioned in 11 Tiger Data reviews.

Expands functionality while maintaining balanced loads. Serves growing demand without reducing functionality. This feature was mentioned in 11 Tiger Data reviews.

The abillity to restore files to its original loacation or alternative sources on-demand. 11 reviewers of Tiger Data have provided feedback on this feature.

Stores historical records of information for reverting or reference.

Helps users navigate databases by labeling and organizing datasets. This feature was mentioned in 12 Tiger Data reviews.

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.

As reported in 11 Tiger Data reviews. 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.

Prevents other users and applications from accessing data while it is being updated to avoid data loss or update.

Allows permissions to be granted or revoked in the database, schema or table levels.

Built-in native encryption with enterprise key management.

Provides multi-factor authentication with certificates.

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.

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.

Provides support for international character sets and full text search.

Supports multiple data types like primitive, structured, document etc.

Supports multiple procedural programming languages like PL/PGSQL, Perl, Python etc.

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

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

Allows multi-version concurrency control.

Handles workloads, from single machines to data warehouses or web services with many concurrent users.

Allows users to quickly retrieve data through various types of indexing like B-tree, hash table etc.

Helps interpret SQL queries and determine the fastest method of execution.

Data is organized as a set of tables with columns and rows like a table structure.

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

Adheres to ACID (atomicity, consistency, isolation, durability), a set of database transaction properties.

Provides log-based or/and trigger-based replication.

Stores data tables as columns.

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

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

Allows semantic search service by organizing data.

Allows indexing of data for search and retrieval.

The feature aids in filtering queries by metadata thus achieving accurate search.

This feature integrates vectors and metadata indexes into a single index.

Aids in faster processing of query to obtain results.

Enables faster queries and search engine-like workloads.

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.