DBMS > Google BigQuery vs. GridDB vs. Infobright vs. Splunk vs. Teradata Aster

System Properties Comparison Google BigQuery vs. GridDB vs. Infobright vs. Splunk vs. Teradata Aster

Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

Description

Large scale data warehouse service with append-only tables

Scalable in-memory time series database optimized for IoT and Big Data

High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontend

Analytics Platform for Big Data

Platform for big data analytics on multistructured data sources and types

Primary database model

Secondary database models

Key-value store
Relational DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	2.09
Rank	#120	Overall
	#10	Time Series DBMS

Score	1.02
Rank	#192	Overall
	#90	Relational DBMS

Score	89.10
Rank	#14	Overall
	#2	Search engines

Website

cloud.google.com/bigquery

griddb.net

ignitetech.com/softwarelibrary/infobrightdb

www.splunk.com

Technical documentation

cloud.google.com/bigquery/docs

docs.griddb.net

docs.splunk.com/Documentation/Splunk

Developer

Google

Toshiba Corporation

Ignite Technologies Inc.; formerly InfoBright Inc.

Splunk Inc.

Teradata

Initial release

2010

2013

2005

2003

2005

Current release

5.1, August 2022

License

Commercial or Open Source

commercial

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

commercial

The open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016

commercial

Limited free edition and free developer edition 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

C++

Server operating systems

hosted

Linux

Linux
Windows

Linux
OS X
Solaris
Windows

Linux

Data scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

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

in Aster File Store

Secondary indexes

yes

Knowledge Grid Technology used instead

yes

SQL

Support of SQL

yes

SQL92, SQL-like TQL (Toshiba Query Language)

yes

Splunk Search Processing Language for search commands

yes

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

HTTP REST

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

.Net
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C#
Java
JavaScript
PHP
Python
Ruby

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

R packages

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

MapReduce

Offers an API for user-defined Map/Reduce methods

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency within container, eventual consistency across containers

Immediate Consistency

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

Transaction concepts

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

Since BigQuery is designed for querying data

ACID at container level

ACID

A 'Transaction' in Splunk has a different meaning: grouping related events into a single one for later searching

ACID

Concurrency

Support for concurrent manipulation of data

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

Google Cloud Identity & Access Management (IAM)

Access rights for users can be defined per database

fine grained access rights according to SQL-standard

exploiting MySQL or PostgreSQL frontend capabilities

Access rights for users and roles

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

GridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more

Competitive advantages

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more

Typical application scenarios

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more

Key customers

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more

Market metrics

GitHub trending repository
» more

Licensing and pricing models

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.