DBMS > GigaSpaces vs. Microsoft SQL Server vs. Teradata vs. Vertica

System Properties Comparison GigaSpaces vs. Microsoft SQL Server vs. Teradata vs. Vertica

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Editorial information provided by DB-Engines

Name

Description

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

Microsofts flagship relational DBMS

A hybrid cloud data analytics software platform (Teradata Vantage)

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Document store
Object oriented DBMS

Values are user defined objects

Relational DBMS

Column oriented

Secondary database models

Graph DBMS
Search engine

Document store
Graph DBMS
Spatial DBMS

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

Spatial DBMS
Time Series DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.97
Rank	#192	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Score	10.68
Rank	#43	Overall
	#27	Relational DBMS

Website

www.gigaspaces.com

www.microsoft.com/en-us/sql-server

www.teradata.com

www.vertica.com

Technical documentation

docs.gigaspaces.com/latest/landing.html

learn.microsoft.com/en-US/sql/sql-server

docs.teradata.com

vertica.com/documentation

Developer

Gigaspaces Technologies

Microsoft

Teradata

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2000

1989

1984

2005

Current release

15.5, September 2020

SQL Server 2022, November 2022

Teradata Vantage 1.0 MU2, January 2019

12.0.3, January 2023

License

Commercial or Open Source

Open Source

Apache Version 2; Commercial licenses available

commercial

restricted free version is available

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, C++, .Net

C++

Server operating systems

Linux
macOS
Solaris
Windows

Linux
Windows

hosted
Linux

Linux

Data scheme

schema-free

yes

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

Typing

predefined data types such as float or date

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

XML can be used for describing objects metadata

yes

Secondary indexes

yes

Join-index to prejoin tables, aggregate index, sparse index, hash index

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

SQL-99 for query and DML statements

yes

SQL 2016 + extensions

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

.Net
C++
Java
Python
Scala

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

yes

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

yes

UDFs, stored procedures, table functions in parallel

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes, event driven architecture

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

Hashing

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

yes, but depending on the SQL-Server Edition

Multi-source replication
Source-replica replication

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Role-based access control

fine grained access rights according to SQL-standard

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

Communication and network analytics, Embedded analytics, Fraud monitoring and Risk...
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Key customers

Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
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Licensing and pricing models

Cost-based models and subscription-based models are both available. One license is...
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