DBMS > Amazon SimpleDB vs. Apache Impala vs. BigchainDB vs. Google BigQuery vs. Realm

System Properties Comparison Amazon SimpleDB vs. Apache Impala vs. BigchainDB vs. Google BigQuery vs. Realm

Editorial information provided by DB-Engines

Name

Description

Hosted simple database service by Amazon, with the data stored in the Amazon Cloud.

There is an unrelated product called SimpleDB developed by Edward Sciore

Analytic DBMS for Hadoop

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

Large scale data warehouse service with append-only tables

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

Primary database model

Secondary database models

Document store

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

Score	1.88
Rank	#133	Overall
	#23	Key-value stores

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	0.85
Rank	#208	Overall
	#35	Document stores

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	7.41
Rank	#52	Overall
	#8	Document stores

Website

aws.amazon.com/simpledb

impala.apache.org

www.bigchaindb.com

cloud.google.com/bigquery

realm.io

Technical documentation

docs.aws.amazon.com/simpledb

impala.apache.org/impala-docs.html

bigchaindb.readthedocs.io/en/latest

cloud.google.com/bigquery/docs

realm.io/docs

Developer

Amazon

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Google

Realm, acquired by MongoDB in May 2019

Initial release

2007

2013

2016

2010

2014

Current release

4.1.0, June 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

AGPL v3

commercial

Open Source

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++

Python

Server operating systems

hosted

Linux

hosted

Android
Backend: server-less
iOS
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

All columns are indexed automatically

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

RESTful HTTP API

JDBC
ODBC

CLI Client
RESTful HTTP API

RESTful HTTP/JSON API

Supported programming languages

.Net
C
C++
Erlang
Java
PHP
Python
Ruby
Scala

All languages supporting JDBC/ODBC

Go
Haskell
Java
JavaScript
Python
Ruby

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
Java

with Android only
Objective-C
React Native
Swift

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

in JavaScript

runs within the applications so server-side scripts are unnecessary

Triggers

yes

Change Listeners

Partitioning methods

Methods for storing different data on different nodes

none

Sharding must be implemented in the application

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Concurrent data updates can be detected by the application

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes,with MongoDB ord RethinkDB

yes

In-memory capabilities

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

yes

In-Memory realm

User concepts

Access control

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

yes

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

Google Cloud Identity & Access Management (IAM)

yes

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