DBMS > Amazon DocumentDB vs. Apache Impala vs. Google BigQuery vs. IBM Cloudant vs. JanusGraph

System Properties Comparison Amazon DocumentDB vs. Apache Impala vs. Google BigQuery vs. IBM Cloudant vs. JanusGraph

Editorial information provided by DB-Engines

Name

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

Analytic DBMS for Hadoop

Large scale data warehouse service with append-only tables

Database as a Service offering based on Apache CouchDB

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

Primary database model

Secondary database models

Document store

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

Score	1.91
Rank	#132	Overall
	#24	Document stores

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	2.68
Rank	#106	Overall
	#20	Document stores

Score	1.94
Rank	#129	Overall
	#12	Graph DBMS

Website

aws.amazon.com/documentdb

impala.apache.org

cloud.google.com/bigquery

www.ibm.com/products/cloudant

janusgraph.org

Technical documentation

aws.amazon.com/documentdb/resources

impala.apache.org/impala-docs.html

cloud.google.com/bigquery/docs

cloud.ibm.com/docs/Cloudant

docs.janusgraph.org

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Google

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

Linux Foundation; originally developed as Titan by Aurelius

Initial release

2019

2013

2010

2017

Current release

4.1.0, June 2022

0.6.3, February 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

Open Source

Apache 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

C++

Erlang

Java

Server operating systems

hosted

Linux

hosted

Linux
OS X
Unix
Windows

Data scheme

schema-free

yes

schema-free

yes

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

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

JDBC
ODBC

RESTful HTTP/JSON API

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

All languages supporting JDBC/ODBC

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
Java
JavaScript
Objective-C
PHP
Ruby

Clojure
Java
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

in JavaScript

View functions (Map-Reduce) in JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Sharding

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

selectable replication factor

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

query execution via MapReduce

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Eventual Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

Relationships in graphs

Transaction concepts

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

Atomic single-document operations

Since BigQuery is designed for querying data

atomic operations within a document possible

ACID

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

yes

Durability

Support for making data persistent

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

In-memory capabilities

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

User concepts

Access control

Access rights for users and roles

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

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

Google Cloud Identity & Access Management (IAM)

Access rights for users can be defined per database

User authentification and security via Rexster Graph Server

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