DBMS > Atos Standard Common Repository vs. Google Cloud Datastore vs. Microsoft SQL Server vs. RRDtool vs. TigerGraph

System Properties Comparison Atos Standard Common Repository vs. Google Cloud Datastore vs. Microsoft SQL Server vs. RRDtool vs. TigerGraph

Editorial information provided by DB-Engines

Name

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

Description

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

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Microsofts flagship relational DBMS

Industry standard data logging and graphing tool for time series data. RRD is an acronym for round-robin database.

The data is stored in a circular buffer, thus the system storage footprint remains constant over time.

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

Primary database model

Document store
Key-value store

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	4.49
Rank	#79	Overall
	#12	Document stores

Score	829.80
Rank	#3	Overall
	#3	Relational DBMS

Score	2.14
Rank	#124	Overall
	#10	Time Series DBMS

Score	1.83
Rank	#141	Overall
	#13	Graph DBMS

Website

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

cloud.google.com/datastore

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

oss.oetiker.ch/rrdtool

www.tigergraph.com

Technical documentation

cloud.google.com/datastore/docs

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

oss.oetiker.ch/rrdtool/doc

docs.tigergraph.com

Developer

Atos Convergence Creators

Google

Microsoft

Tobias Oetiker

Initial release

2016

2008

1989

1999

2017

Current release

1703

SQL Server 2022, November 2022

1.8.0, 2022

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

GPL V2 and FLOSS

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++

Implementations in Java (e.g. RRD4J) and C# available

C++

Server operating systems

Linux

hosted

Linux
Windows

HP-UX
Linux

Linux

Data scheme

Schema and schema-less with LDAP views

schema-free

yes

Typing

predefined data types such as float or date

optional

yes, details here

yes

Numeric data only

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

Exporting into and restoring from XML files possible

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language (GQL)

yes

SQL-like query language (GSQL)

APIs and other access methods

LDAP

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

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

in-process shared library
Pipes

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

Supported programming languages

All languages with LDAP bindings

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

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

with librrd library
C#

with a different implementation of RRDTool
Java

with a different implementation of RRDTool
JavaScript (Node.js)

with a different implementation of RRDTool
Lua
Perl
PHP

with a wrapper library
Python
Ruby

C++
Java

Server-side scripts

Stored procedures

using Google App Engine

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

yes

Triggers

yes

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication using Paxos

yes, but depending on the SQL-Server Edition

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

none

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Relationships in graphs

Transaction concepts

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

Atomic execution of specific operations

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Concurrency

Support for concurrent manipulation of data

yes

by using the rrdcached daemon

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 rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

Role-based access control

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