Microsoft SQL Server Features
Database Features (7)
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Storage
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Availability
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Stability
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Scalability
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Security
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Data Manipulation
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Query Language
Database (3)
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Real-Time Data Collection
Collects, stores, and organizes massive, unstructured data in real time
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Data Distribution
Facilitates the disseminating of collected big data throughout parallel computing clusters
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Data Lake
Creates a repository to collect and store raw data from sensors, devices, machines, files, etc.
Integrations (2)
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Hadoop Integration
Aligns processing and distribution workflows on top of Apache Hadoop
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Spark Integration
Aligns processing and distribution workflows on top of Apache Hadoop
Platform (3)
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Machine Scaling
Facilitates solution to run on and scale to a large number of machines and systems
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Data Preparation
Curates collected data for big data analytics solutions to analyze, manipulate, and model
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Spark Integration
Aligns processing and distribution workflows on top of Apache Hadoop
Processing (2)
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Cloud Processing
Moves big data collection and processing to the cloud
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Workload Processing
Processes batch, real-time, and streaming data workloads in singular, multi-tenant, or cloud systems
Maintenance (3)
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Data Migration
Allows data movement from one database to another.
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Backup and Recovery
Provides data backup and recovery functionality to protect and restore a database.
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Multi-User Environment
Allows users to access and work on data concurrently, supporting several views of the data.
Management (5)
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Data dictionary
Stores the database metadata, that is the definitions of data elements, types, relationships etc.
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Data Replication
Creates a copy of the database to maintain consistency and integrity.
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Query Language
Allows users to create, update and retrieve data in a database.
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Data Modeling
Defines the logical design of the data before building the schemas.
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Performance Analysis
Monitors and analyzes critical database attributes like query performance, user sessions, dead lock detail, system errors etc and visualize them on a custom dashboard.
Security (6)
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Data Encryption
Encrypts and transforms data at the database from a readable state into a ciphertext of unreadable characters.
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User Access Control
Allows restricted user acess to modify depending on the access level.
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Database Locking
Prevents other users and applications from accessing data while it is being updated to avoid data loss or update.
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Access Control
Allows permissions to be granted or revoked in the database, schema or table levels.
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Encryption
Built-in native encryption with enterprise key management.
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Authentication
Provides multi-factor authentication with certificates.
Support (4)
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Text Search
Provides support for international character sets and full text search.
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Data Types
Supports multiple data types like primitive, structured, document etc.
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Languages
Supports multiple procedural programming languages like PL/PGSQL, Perl, Python etc.
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Operating Systems
Available on multiple operating systems like Linux, Windows, MacOS etc.
Performance (5)
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Disaster Recovery
Provides data recovery functionality to protect and restore data in a database.
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Data Concurrency
Allows multi-version concurrency control.
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Workload Management
Handles workloads, from single machines to data warehouses or web services with many concurrent users.
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Advanced Indexing
Allows users to quickly retrieve data through various types of indexing like B-tree, hash table etc.
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Query Optimizer
Helps interpret SQL queries and determine the fastest method of execution.
Management (4)
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Data Schema
Data is organized as a set of tables with columns and rows like a table structure.
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Query Language
Allows users to create, update and retrieve data in a database.
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ACID - Complaint
Adheres to ACID (atomicity, consistency, isolation, durability), a set of database transaction properties.
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Data Replication
Provides log-based or/and trigger-based replication.
Technology Glossary Features
View definitions of the features and discover new technology terms.
Data manipulation is the process of organizing, modifying, and transforming data to improve accuracy, usability, and analysis across systems and workflows.
Data modeling is the process of creating visual representations of information systems to better communicate the connections between data points and structures. Learn more about data modeling in this G2 guide.




