DBMS > LevelDB vs. Splice Machine vs. TempoIQ vs. Teradata

System Properties Comparison LevelDB vs. Splice Machine vs. TempoIQ vs. Teradata

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Editorial information provided by DB-Engines

Name

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

Description

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

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

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

A hybrid cloud data analytics software platform (Teradata Vantage)

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	2.35
Rank	#111	Overall
	#19	Key-value stores

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Website

github.com/google/leveldb

splicemachine.com

tempoiq.com (offline)

www.teradata.com

Technical documentation

github.com/google/leveldb/blob/main/doc/index.md

splicemachine.com/how-it-works

docs.teradata.com

Developer

Google

Splice Machine

TempoIQ

Teradata

Initial release

2011

2014

2012

1984

Current release

1.23, February 2021

3.1, March 2021

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

Open Source

BSD

Open Source

AGPL 3.0, 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

C++

Java

Server operating systems

Illumos
Linux
OS X
Windows

Linux
OS X
Solaris
Windows

hosted
Linux

Data scheme

schema-free

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.

yes

Secondary indexes

yes

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

SQL

Support of SQL

yes

SQL 2016 + extensions

APIs and other access methods

JDBC
Native Spark Datasource
ODBC

HTTP API

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

Supported programming languages

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

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

C#
Java
JavaScript

Node.js
Python
Ruby

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

Server-side scripts

Stored procedures

yes

Java

yes

UDFs, stored procedures, table functions in parallel

Triggers

yes

Realtime Alerts

yes

Partitioning methods

Methods for storing different data on different nodes

none

Shared Nothhing Auto-Sharding, Columnar Partitioning

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

none

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

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

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, groups and roles according to SQL-standard

simple authentication-based access control

fine grained access rights according to SQL-standard

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More resources

DB-Engines blog posts

Teradata is the most popular data warehouse DBMS
2 April 2013, Paul Andlinger

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