IBM Db2 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.

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.

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.

Policies, procedures and standards to manage and access data.

Restricts data access at a cell level, mask or hide parts of cells, and encrypt data at rest and in motion

Helps in improving the availability of data.

Uses machine learning algorithms to gain actionable insights.

Consolidates, Cleanses and Normalizes data from multiple disparate sources.

Helps save storage capacity and improves query performance.

Eliminates data inconsistency and duplications ensuring data integrity.

SQL based analytics functions like Time series, pattern matching, geospatial analytics etc.

Provides built in capabilities like machine learning algorithms, data preparation functions, model evaluation and management etc.

Allows data querying across data formats like parquet, ORC, JSON etc and analyze complex data types on HDFS

Integrates with data science workflows, Machine Learning and artificial intelligence (AI) capabilities.

Integrates with BI Tools to transform data into Actionable Insights.

Provides speed in data processing and capturing unstructured, semi-structured and streaming data.

Provides On-Premise deployment options.

Provides Cloud deployment options (private or public cloud, hybrid cloud).

Manages huge volumes of data, upscale or downscale as per demand.

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.

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.

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.

Software is able to alert the user in case of any errors.

Utilizes data to proactively identify IT issues.

Creates new or streamlines existing data models to ensure business continuity.

Automatic creation and management of documents.

The software supports a wide variety of data sources and data warehouses that require automation.

Supports a template system, making it easier to set up configuration parameters.

Automates all parts of the Data Warehouse lifecycle - warehouse design, provision data sources.

Supports real-time data ingestions and updates in the cloud or on-premises.

Software delivers insights into data in order to identify gaps, risk within the data warehouse.

Supports no-code/low code development patterns.

Allows users to generate text based on a text prompt.

Condenses long documents or text into a brief summary.

Anticipates needs and offers suggestions without prompting