DBMS > Apache Impala vs. Spark SQL vs. TerminusDB vs. Transbase

System Properties Comparison Apache Impala vs. Spark SQL vs. TerminusDB vs. Transbase

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

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

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Relational DBMS

Graph DBMS

Relational DBMS

Secondary database models

Document store

Document store
RDF store

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.17
Rank	#325	Overall
	#29	Graph DBMS

Score	0.11
Rank	#341	Overall
	#150	Relational DBMS

Website

impala.apache.org

spark.apache.org/sql

terminusdb.com

www.transaction.de/en/products/transbase.html

Technical documentation

impala.apache.org/impala-docs.html

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

terminusdb.github.io/terminusdb/#

www.transaction.de/en/products/transbase/features.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Apache Software Foundation

DataChemist Ltd.

Transaction Software GmbH

Initial release

2013

2014

2018

1987

Current release

4.1.0, June 2022

3.5.0 ( 2.13), September 2023

11.0.0, January 2023

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache 2.0

Open Source

GPL V3

commercial

free development license

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Scala

Prolog, Rust

C and C++

Server operating systems

Linux

Linux
OS X
Windows

Linux

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language (WOQL)

yes

APIs and other access methods

JDBC
ODBC

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

All languages supporting JDBC/ODBC

Java
Python
R
Scala

JavaScript
Python

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

Journaling Streams

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory journaling

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Role-based access control

fine grained access rights according to SQL-standard

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More resources

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