DBMS > Axibase vs. Google BigQuery vs. STSdb vs. Teradata vs. TimesTen

System Properties Comparison Axibase vs. Google BigQuery vs. STSdb vs. Teradata vs. TimesTen

Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on HBase with integrated rule engine and visualization

Large scale data warehouse service with append-only tables

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

A hybrid cloud data analytics software platform (Teradata Vantage)

In-Memory RDBMS compatible to Oracle

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	0.35
Rank	#282	Overall
	#25	Time Series DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.10
Rank	#357	Overall
	#51	Key-value stores

Score	44.87
Rank	#22	Overall
	#15	Relational DBMS

Score	1.36
Rank	#161	Overall
	#75	Relational DBMS

Website

axibase.com/docs/atsd/finance

cloud.google.com/bigquery

github.com/STSSoft/STSdb4

www.teradata.com

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

cloud.google.com/bigquery/docs

docs.teradata.com

docs.oracle.com/database/timesten-18.1

Developer

Axibase Corporation

Google

STS Soft SC

Teradata

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2013

2010

2011

1984

1998

Current release

15585

4.0.8, September 2015

Teradata Vantage 1.0 MU2, January 2019

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

Community Edition (single node) is free, Enterprise Edition (distributed) is paid

commercial

Open Source

GPLv2, commercial license available

commercial

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

Java

Server operating systems

Linux

hosted

Windows

hosted
Linux

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

short, integer, long, float, double, decimal, string

yes

primitive types and user defined types (classes)

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.

yes

Secondary indexes

yes

Join-index to prejoin tables, aggregate index, sparse index, hash index

yes

SQL

Support of SQL

SQL-like query language

yes

SQL 2016 + extensions

yes

APIs and other access methods

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

RESTful HTTP/JSON API

.NET Client API

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

Go
Java
PHP
Python
R
Ruby

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
Java

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

yes

UDFs, stored procedures, table functions in parallel

PL/SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Hashing

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

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

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

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

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.