DBMS > Atos Standard Common Repository vs. EXASOL vs. FeatureBase vs. Google BigQuery vs. Warp 10

System Properties Comparison Atos Standard Common Repository vs. EXASOL vs. FeatureBase vs. Google BigQuery vs. Warp 10

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

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

Real-time database platform that powers real-time analytics and machine learning applications by simultaneously executing low-latency, high-throughput, and highly concurrent workloads.

Large scale data warehouse service with append-only tables

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Document store
Key-value store

Relational DBMS

Time Series DBMS

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

Score	1.76
Rank	#139	Overall
	#62	Relational DBMS

Score	0.31
Rank	#292	Overall
	#135	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.14
Rank	#344	Overall
	#32	Time Series DBMS

Website

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

www.exasol.com

www.featurebase.com

cloud.google.com/bigquery

www.warp10.io

Technical documentation

www.exasol.com/resources

docs.featurebase.com

cloud.google.com/bigquery/docs

www.warp10.io/content/02_Getting_started

Developer

Atos Convergence Creators

Exasol

Molecula and Pilosa Open Source Contributors

Google

SenX

Initial release

2016

2000

2017

2010

2015

Current release

1703

2022, May 2022

License

Commercial or Open Source

commercial

Open Source

Apache License 2.0

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

Server operating systems

Linux

Linux
macOS

hosted

Linux
OS X
Windows

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

Secondary indexes

yes

SQL

Support of SQL

yes

SQL queries

yes

APIs and other access methods

LDAP

.Net
JDBC
ODBC
WebSocket

gRPC
JDBC
Kafka Connector
ODBC

RESTful HTTP/JSON API

HTTP API
Jupyter
WebSocket

Supported programming languages

All languages with LDAP bindings

Java
Lua
Python
R

Java
Python

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

WarpScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

none

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of specific operations

ACID

yes

Since BigQuery is designed for querying data

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using Linux fsync

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 according to SQL-standard

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

Google Cloud Identity & Access Management (IAM)

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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