DBMS > Amazon DynamoDB vs. HBase vs. Hive vs. Interbase vs. SurrealDB

System Properties Comparison Amazon DynamoDB vs. HBase vs. Hive vs. Interbase vs. SurrealDB

Editorial information provided by DB-Engines

Name

Description

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

Wide-column store based on Apache Hadoop and on concepts of BigTable

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Light-weight proven RDBMS

originally from Borland

A fully ACID transactional, developer-friendly, multi-model DBMS

Primary database model

Document store
Key-value store

Wide column store

Relational DBMS

Document store
Graph DBMS

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

Score	74.45
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	27.97
Rank	#26	Overall
	#2	Wide column stores

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	4.08
Rank	#75	Overall
	#41	Relational DBMS

Score	1.02
Rank	#190	Overall
	#33	Document stores
	#18	Graph DBMS

Website

aws.amazon.com/dynamodb

hbase.apache.org

hive.apache.org

www.embarcadero.com/products/interbase

surrealdb.com

Technical documentation

docs.aws.amazon.com/dynamodb

hbase.apache.org/book.html

cwiki.apache.org/confluence/display/Hive/Home

docs.embarcadero.com/products/interbase

surrealdb.com/docs

Developer

Amazon

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Apache Software Foundation

initially developed by Facebook

Embarcadero

SurrealDB Ltd

Initial release

2012

2008

2012

1984

2022

Current release

2.3.4, January 2021

3.1.3, April 2022

InterBase 2020, December 2019

v1.5.0, May 2024

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

Apache version 2

Open Source

Apache Version 2

commercial

Open Source

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Rust

Server operating systems

hosted

Linux
Unix
Windows

using Cygwin

All OS with a Java VM

Android
iOS
Linux
OS X
Windows

Linux
macOS
Windows

Data scheme

schema-free

schema-free, schema definition possible

yes

schema-free

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

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.

export as XML data possible

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

SQL-like query language

APIs and other access methods

RESTful HTTP API

Java API
RESTful HTTP API
Thrift

JDBC
ODBC
Thrift

ADO.NET
JDBC
ODBC

GraphQL
RESTful HTTP API
WebSocket

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

C
C#
C++
Groovy
Java
PHP
Python
Scala

C++
Java
PHP
Python

.Net
C
C++
Delphi
Java
Object Pascal
PHP
Ruby

Deno
Go
JavaScript (Node.js)
Rust

Server-side scripts

Stored procedures

yes

Coprocessors in Java

yes

user defined functions and integration of map-reduce

yes

Interbase procedure and trigger language

Triggers

yes

by integration with AWS Lambda

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

selectable replication factor

Interbase Change Views

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

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

Immediate Consistency or Eventual Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID across one or more tables within a single AWS account and region

Single row ACID (across millions of columns)

ACID

Concurrency

Support for concurrent manipulation of data

yes

Multiversion concurreny control

yes

Durability

Support for making data persistent

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

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

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

Access rights for users, groups and roles

fine grained access rights according to SQL-standard

yes, based on authentication and database rules

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