DBMS > Google BigQuery vs. Interbase vs. KairosDB vs. Splice Machine

System Properties Comparison Google BigQuery vs. Interbase vs. KairosDB vs. Splice Machine

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Light-weight proven RDBMS

originally from Borland

Distributed Time Series DBMS based on Cassandra or H2

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	4.08
Rank	#75	Overall
	#41	Relational DBMS

Score	0.67
Rank	#233	Overall
	#20	Time Series DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Website

cloud.google.com/bigquery

www.embarcadero.com/products/interbase

github.com/kairosdb/kairosdb

splicemachine.com

Technical documentation

cloud.google.com/bigquery/docs

docs.embarcadero.com/products/interbase

kairosdb.github.io

splicemachine.com/how-it-works

Developer

Google

Embarcadero

Splice Machine

Initial release

2010

1984

2013

2014

Current release

InterBase 2020, December 2019

1.2.2, November 2018

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

AGPL 3.0, commercial license available

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

Server operating systems

hosted

Android
iOS
Linux
OS X
Windows

Linux
OS X
Windows

Linux
OS X
Solaris
Windows

Data scheme

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.

export as XML data possible

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

Graphite protocol
HTTP REST
Telnet API

JDBC
Native Spark Datasource
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Java
JavaScript

Node.js
PHP
Python

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Interbase procedure and trigger language

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Interbase Change Views

selectable replication factor

based on Cassandra

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Multiversion concurreny control

yes

yes, multi-version concurrency control (MVCC)

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

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

simple password-based access control

Access rights for users, groups and roles according to SQL-standard

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