DBMS > Atos Standard Common Repository vs. EventStoreDB vs. eXtremeDB vs. Google BigQuery vs. Sqrrl

System Properties Comparison Atos Standard Common Repository vs. EventStoreDB vs. eXtremeDB vs. Google BigQuery vs. Sqrrl

Editorial information provided by DB-Engines

Name

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

Sqrrl has been acquired by Amazon and became a part of Amazon Web Services. It has been removed from the DB-Engines ranking.

Description

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

Industrial-strength, open-source database solution built from the ground up for event sourcing.

Natively in-memory DBMS with options for persistency, high-availability and clustering

Large scale data warehouse service with append-only tables

Adaptable, secure NoSQL built on Apache Accumulo

Primary database model

Document store
Key-value store

Event Store

Relational DBMS
Time Series DBMS

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

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

Score	1.10
Rank	#179	Overall
	#1	Event Stores

Score	0.74
Rank	#223	Overall
	#103	Relational DBMS
	#18	Time Series DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Website

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

www.eventstore.com

www.mcobject.com

cloud.google.com/bigquery

sqrrl.com

Technical documentation

developers.eventstore.com

www.mcobject.com/docs/extremedb.htm

cloud.google.com/bigquery/docs

Developer

Atos Convergence Creators

Event Store Limited

McObject

Google

Amazon

originally Sqrrl Data, Inc.

Initial release

2016

2012

2001

2010

2012

Current release

1703

21.2, February 2021

8.2, 2021

License

Commercial or Open Source

commercial

Open Source

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

Java

C and C++

Java

Server operating systems

Linux

Linux
Windows

AIX
HP-UX
Linux
macOS
Solaris
Windows

hosted

Linux

Data scheme

Schema and schema-less with LDAP views

yes

schema-free

Typing

predefined data types such as float or date

optional

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

support of XML interfaces available

Secondary indexes

yes

SQL

Support of SQL

yes

with the option: eXtremeSQL

yes

APIs and other access methods

LDAP

.NET Client API
JDBC
JNI
ODBC
Proprietary protocol
RESTful HTTP API

RESTful HTTP/JSON API

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

Supported programming languages

All languages with LDAP bindings

.Net
C
C#
C++
Java
Lua
Python
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Actionscript
C

using GLib
C#
C++
Cocoa
Delphi
Erlang
Go
Haskell
Java
JavaScript
OCaml
Perl
PHP
Python
Ruby
Smalltalk

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

Triggers

yes

by defining events

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

horizontal partitioning / sharding

none

Sharding

making use of Hadoop

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Active Replication Fabric™ for IoT
Multi-source replication

by means of eXtremeDB Cluster option
Source-replica replication

by means of eXtremeDB High Availability option

selectable replication factor

making use of Hadoop

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Document store kept consistent with combination of global timestamping, row-level transactions, and server-side consistency resolution.

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of specific operations

ACID

Since BigQuery is designed for querying data

Atomic updates per row, document, or graph entity

Concurrency

Support for concurrent manipulation of data

yes

Optimistic (MVCC) and pessimistic (locking) strategies available

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

LDAP bind authentication

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

Google Cloud Identity & Access Management (IAM)

Cell-level Security, Data-Centric Security, Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

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