DBMS > Apache IoTDB vs. Atos Standard Common Repository vs. EXASOL vs. FeatureBase vs. Google BigQuery

System Properties Comparison Apache IoTDB vs. Atos Standard Common Repository vs. EXASOL vs. FeatureBase vs. Google BigQuery

Editorial information provided by DB-Engines

Name

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

Description

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

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

Primary database model

Time Series DBMS

Document store
Key-value store

Relational DBMS

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

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

Website

iotdb.apache.org

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

www.exasol.com

www.featurebase.com

cloud.google.com/bigquery

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

www.exasol.com/resources

docs.featurebase.com

cloud.google.com/bigquery/docs

Developer

Apache Software Foundation

Atos Convergence Creators

Exasol

Molecula and Pilosa Open Source Contributors

Google

Initial release

2018

2016

2000

2017

2010

Current release

1.1.0, April 2023

1703

2022, May 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

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

Server operating systems

All OS with a Java VM (>= 1.8)

Linux

Linux
macOS

hosted

Data scheme

yes

Schema and schema-less with LDAP views

yes

Typing

predefined data types such as float or date

yes

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

SQL-like query language

yes

SQL queries

yes

APIs and other access methods

JDBC
Native API

LDAP

.Net
JDBC
ODBC
WebSocket

gRPC
JDBC
Kafka Connector
ODBC

RESTful HTTP/JSON API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

All languages with LDAP bindings

Java
Lua
Python
R

Java
Python

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

Sharding

cell division

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

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

yes

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)

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