DBMS > Databend vs. Ingres vs. LeanXcale vs. Teradata

System Properties Comparison Databend vs. Ingres vs. LeanXcale vs. Teradata

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Editorial information provided by DB-Engines

Name

Description

An open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexity

Well established RDBMS

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

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	0.34
Rank	#283	Overall
	#130	Relational DBMS

Score	3.80
Rank	#82	Overall
	#44	Relational DBMS

Score	0.36
Rank	#280	Overall
	#40	Key-value stores
	#129	Relational DBMS

Score	44.87
Rank	#22	Overall
	#15	Relational DBMS

Website

github.com/datafuselabs/databend
www.databend.com

www.actian.com/databases/ingres

www.leanxcale.com

www.teradata.com

Technical documentation

docs.databend.com

docs.actian.com/ingres

docs.teradata.com

Developer

Databend Labs

Actian Corporation

LeanXcale

Teradata

Initial release

2021

1974

originally developed at University Berkely in early 1970s

2015

1984

Current release

1.0.59, April 2023

11.2, May 2022

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

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

Rust

Server operating systems

hosted
Linux
macOS

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

hosted
Linux

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.

but tools for importing/exporting data from/to XML-files available

yes

Secondary indexes

yes

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

SQL

Support of SQL

yes

through Apache Derby

yes

SQL 2016 + extensions

APIs and other access methods

CLI Client
JDBC
RESTful HTTP API

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

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

Supported programming languages

Go
Java
JavaScript (Node.js)
Python
Rust

C
Java
Scala

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

Server-side scripts

Stored procedures

yes

UDFs, stored procedures, table functions in parallel

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Ingres Star to access multiple databases simultaneously

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

none

Ingres Replicator

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC

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

Users with fine-grained authorization concept, user roles

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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