DBMS > Amazon DynamoDB vs. EXASOL vs. GridDB vs. Spark SQL vs. TempoIQ

System Properties Comparison Amazon DynamoDB vs. EXASOL vs. GridDB vs. Spark SQL vs. TempoIQ

Editorial information provided by DB-Engines

Name

TempoIQ seems to be decommissioned. It will be removed from the DB-Engines ranking.

Description

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

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

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

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

Scalable analytics DBMS for sensor data, provided as a service (SaaS)

Primary database model

Document store
Key-value store

Secondary database models

Key-value store
Relational DBMS

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

Score	74.07
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	1.99
Rank	#124	Overall
	#58	Relational DBMS

Score	1.95
Rank	#128	Overall
	#10	Time Series DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

aws.amazon.com/dynamodb

www.exasol.com

griddb.net

spark.apache.org/sql

tempoiq.com (offline)

Technical documentation

docs.aws.amazon.com/dynamodb

www.exasol.com/resources

docs.griddb.net

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

Developer

Amazon

Exasol

Toshiba Corporation

Apache Software Foundation

TempoIQ

Initial release

2012

2000

2013

2014

2012

Current release

5.1, August 2022

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

Open Source

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

Open Source

Apache 2.0

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++

Scala

Server operating systems

hosted

Linux

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

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.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL92, SQL-like TQL (Toshiba Query Language)

SQL-like DML and DDL statements

APIs and other access methods

RESTful HTTP API

.Net
JDBC
ODBC
WebSocket

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

JDBC
ODBC

HTTP API

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

Java
Lua
Python
R

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

Java
Python
R
Scala

C#
Java
JavaScript

Node.js
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

Triggers

yes

by integration with AWS Lambda

yes

Realtime Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

Hadoop integration

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

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Immediate consistency within container, eventual consistency across containers

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

ACID

ACID at container level

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

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

Access rights for users can be defined per database

simple authentication-based access control

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.