DBMS > Apache Impala vs. Atos Standard Common Repository vs. Blazegraph vs. Microsoft Azure AI Search vs. VoltDB

System Properties Comparison Apache Impala vs. Atos Standard Common Repository vs. Blazegraph vs. Microsoft Azure AI Search vs. VoltDB

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Amazon has acquired Blazegraph's domain and (probably) product. It is said that Amazon Neptune is based on Blazegraph.

Description

Analytic DBMS for Hadoop

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

High-performance graph database supporting Semantic Web (RDF/SPARQL) and Graph Database (tinkerpop3, blueprints, vertex-centric) APIs with scale-out and High Availability.

Search-as-a-service for web and mobile app development

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Relational DBMS

Document store
Key-value store

Graph DBMS
RDF store

Search engine

Relational DBMS

Secondary database models

Document store

Vector DBMS

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	0.75
Rank	#219	Overall
	#19	Graph DBMS
	#8	RDF stores

Score	5.59
Rank	#63	Overall
	#7	Search engines

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Website

impala.apache.org

atos.net/en/convergence-creators/portfolio/standard-common-repository

blazegraph.com

azure.microsoft.com/en-us/services/search

www.voltdb.com

Technical documentation

impala.apache.org/impala-docs.html

wiki.blazegraph.com

learn.microsoft.com/en-us/azure/search

docs.voltdb.com

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Atos Convergence Creators

Blazegraph

Microsoft

VoltDB Inc.

Initial release

2013

2016

2006

2015

2010

Current release

4.1.0, June 2022

1703

2.1.5, March 2019

11.3, April 2022

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

extended commercial license available

commercial

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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

C++

Java

Java, C++

Server operating systems

Linux

Linux
OS X
Windows

hosted

Linux
OS X

for development

Data scheme

yes

Schema and schema-less with LDAP views

schema-free

yes

Typing

predefined data types such as float or date

yes

optional

yes

RDF literal 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

SQL-like DML and DDL statements

SPARQL is used as query language

yes

only a subset of SQL 99

APIs and other access methods

JDBC
ODBC

LDAP

Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3

RESTful HTTP API

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

All languages supporting JDBC/ODBC

All languages with LDAP bindings

.Net
C
C++
Java
JavaScript
PHP
Python
Ruby

C#
Java
JavaScript
Python

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

Implicit feature of the cloud service

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in Graphs

FOREIGN KEY constraints are not supported

Transaction concepts

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

Atomic execution of specific operations

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Snapshots and command logging

In-memory capabilities

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

yes

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

LDAP bind authentication

Security and Authentication via Web Application Container (Tomcat, Jetty)

yes

using Azure authentication

Users and roles with access to stored procedures

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More resources

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