DBMS > Graph Engine vs. Microsoft Azure Synapse Analytics vs. Percona Server for MongoDB vs. SQLite vs. Teradata Aster

System Properties Comparison Graph Engine vs. Microsoft Azure Synapse Analytics vs. Percona Server for MongoDB vs. SQLite vs. Teradata Aster

Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

Description

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

A drop-in replacement for MongoDB Community Edition with enterprise-grade features.

Widely used embeddable, in-process RDBMS

Platform for big data analytics on multistructured data sources and types

Primary database model

Graph DBMS
Key-value store

Relational DBMS

Document store

Relational DBMS

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

Score	0.67
Rank	#232	Overall
	#21	Graph DBMS
	#34	Key-value stores

Score	19.93
Rank	#31	Overall
	#19	Relational DBMS

Score	0.60
Rank	#246	Overall
	#39	Document stores

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Website

www.graphengine.io

azure.microsoft.com/services/synapse-analytics

www.percona.com/mongodb/software/percona-server-for-mongodb

www.sqlite.org

Technical documentation

www.graphengine.io/docs/manual

docs.microsoft.com/azure/synapse-analytics

docs.percona.com/percona-distribution-for-mongodb

www.sqlite.org/docs.html

Developer

Microsoft

Percona

Dwayne Richard Hipp

Teradata

Initial release

2010

2016

2015

2000

2005

Current release

3.4.10-2.10, November 2017

3.46.0 (23 May 2024), May 2024

License

Commercial or Open Source

Open Source

MIT License

commercial

Open Source

GPL Version 2

Open Source

Public Domain

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

.NET and C

C++

Server operating systems

.NET

hosted

Linux

server-less

Linux

Data scheme

yes

schema-free

yes

dynamic column types

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

in Aster File Store

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-92 is not fully supported

yes

APIs and other access methods

RESTful HTTP API

ADO.NET
JDBC
ODBC

proprietary protocol using JSON

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

C#
C++
F#
Visual Basic

C#
Java
PHP

Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

yes

Transact SQL

JavaScript

R packages

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding, horizontal partitioning

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

none

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

yes

Durability

Support for making data persistent

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

yes

In-memory capabilities

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

yes

via In-Memory Engine

yes

User concepts

Access control

yes

Access rights for users and roles

fine grained access rights according to SQL-standard

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