DBMS > Amazon Redshift vs. AnzoGraph DB vs. Apache IoTDB vs. Datomic vs. RocksDB

System Properties Comparison Amazon Redshift vs. AnzoGraph DB vs. Apache IoTDB vs. Datomic vs. RocksDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

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

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.29
Rank	#303	Overall
	#25	Graph DBMS
	#14	RDF stores

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Website

aws.amazon.com/redshift

cambridgesemantics.com/anzograph

iotdb.apache.org

www.datomic.com

rocksdb.org

Technical documentation

docs.aws.amazon.com/redshift

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

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

docs.datomic.com

github.com/facebook/rocksdb/wiki

Developer

Amazon (based on PostgreSQL)

Cambridge Semantics

Apache Software Foundation

Cognitect

Facebook, Inc.

Initial release

2012

2018

2012

2013

Current release

2.3, January 2021

1.1.0, April 2023

1.0.7075, December 2023

9.2.1, May 2024

License

Commercial or Open Source

commercial

free trial version available

Open Source

Apache Version 2.0

commercial

limited edition free

Open Source

BSD

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

Java, Clojure

C++

Server operating systems

hosted

Linux

All OS with a Java VM (>= 1.8)

All OS with a Java VM

Linux

Data scheme

yes

Schema-free and OWL/RDFS-schema support

yes

schema-free

Typing

predefined data types such as float or date

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.

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SPARQL and SPARQL* as primary query language. Cypher preview.

SQL-like query language

APIs and other access methods

JDBC
ODBC

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

JDBC
Native API

RESTful HTTP API

C++ API
Java API

Supported programming languages

All languages supporting JDBC/ODBC

C++
Java
Python

C
C#
C++
Go
Java
Python
Scala

Clojure
Java

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions and aggregates

yes

Transaction Functions

Triggers

yes

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic sharding

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

none

But extensive use of caching in the application peers

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication in MPP-Cluster

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

none

But extensive use of caching in the application peers

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency in MPP-Cluster

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

not needed in graphs

Transaction concepts

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

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

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

yes

recommended only for testing and development

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users and roles

yes

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.