DBMS > Amazon DynamoDB vs. Google BigQuery vs. Interbase vs. Spark SQL vs. Transbase

System Properties Comparison Amazon DynamoDB vs. Google BigQuery vs. Interbase vs. Spark SQL vs. Transbase

Editorial information provided by DB-Engines

Name

Description

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

Large scale data warehouse service with append-only tables

Light-weight proven RDBMS

originally from Borland

Spark SQL is a component on top of 'Spark Core' for structured data processing

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Document store
Key-value store

Relational 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	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	4.08
Rank	#75	Overall
	#41	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.17
Rank	#334	Overall
	#148	Relational DBMS

Website

aws.amazon.com/dynamodb

cloud.google.com/bigquery

www.embarcadero.com/products/interbase

spark.apache.org/sql

www.transaction.de/en/products/transbase.html

Technical documentation

docs.aws.amazon.com/dynamodb

cloud.google.com/bigquery/docs

docs.embarcadero.com/products/interbase

spark.apache.org/docs/latest/sql-programming-guide.html

www.transaction.de/en/products/transbase/features.html

Developer

Amazon

Google

Embarcadero

Apache Software Foundation

Transaction Software GmbH

Initial release

2012

2010

1984

2014

1987

Current release

InterBase 2020, December 2019

3.5.0 ( 2.13), September 2023

Transbase 8.3, 2022

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

Open Source

Apache 2.0

commercial

free development license

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

Scala

C and C++

Server operating systems

hosted

Android
iOS
Linux
OS X
Windows

Linux
OS X
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

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.

export as XML data possible

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

RESTful HTTP API

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Java
Python
R
Scala

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Interbase procedure and trigger language

yes

Triggers

yes

by integration with AWS Lambda

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Interbase Change Views

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

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

Since BigQuery is designed for querying data

ACID

yes

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

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

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.