DBMS > Altibase vs. Google BigQuery vs. HarperDB vs. Linter vs. Microsoft SQL Server

System Properties Comparison Altibase vs. Google BigQuery vs. HarperDB vs. Linter vs. Microsoft SQL Server

Editorial information provided by DB-Engines

Name

Description

An enterprise grade, high-performance RDBMS

Large scale data warehouse service with append-only tables

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

RDBMS for high security requirements

Microsofts flagship relational DBMS

Primary database model

Relational DBMS

Document store

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	1.02
Rank	#189	Overall
	#88	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.60
Rank	#244	Overall
	#38	Document stores

Score	0.12
Rank	#350	Overall
	#152	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Website

altibase.com

cloud.google.com/bigquery

www.harperdb.io

linter.ru

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

Technical documentation

github.com/ALTIBASE/Documents/tree/master/Manuals

cloud.google.com/bigquery/docs

docs.harperdb.io/docs

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

Developer

Altibase

Google

HarperDB

relex.ru

Microsoft

Initial release

1999

2010

2017

1990

1989

Current release

v7.3, 2023, August 2023

3.1, August 2021

SQL Server 2022, November 2022

License

Commercial or Open Source

commercial

Open source edition discontinued with March 2023

commercial

free community edition available

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Node.js

C and C++

C++

Server operating systems

AIX
HP-UX
Linux

hosted

Linux
OS X

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

Linux
Windows

Data scheme

yes

dynamic schema

yes

Typing

predefined data types such as float or date

yes

JSON data types

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.

yes

Secondary indexes

yes

SQL

Support of SQL

ANSI SQL-92

yes

SQL-like data manipulation statements

yes

APIs and other access methods

JDBC
ODBC
Proprietary protocol

RESTful HTTP/JSON API

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

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

Supported programming languages

C
C++
Java

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

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

Server-side scripts

Stored procedures

stored procedures and stored functions

user defined functions

in JavaScript

Custom Functions

since release 3.1

yes

proprietary syntax with the possibility to convert from PL/SQL

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

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

A table resides as a whole on one (or more) nodes in a cluster

none

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes

the nodes on which a table resides can be defined

Source-replica replication

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

Atomic execution of specific operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using LMDB

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

fine grained access rights according to SQL-standard

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Access rights for users and roles

fine grained access rights according to SQL-standard

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