DBMS > Sequoiadb vs. Teradata vs. Transwarp Hippo

System Properties Comparison Sequoiadb vs. Teradata vs. Transwarp Hippo

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Editorial information provided by DB-Engines

Name

Description

NewSQL database with distributed OLTP and SQL

A hybrid cloud data analytics software platform (Teradata Vantage)

Cloud-native distributed Vector DBMS that supports storage, retrieval, and management of massive vector-based datasets

Primary database model

Document store
Relational DBMS

Relational DBMS

Vector 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	0.50
Rank	#258	Overall
	#41	Document stores
	#120	Relational DBMS

Score	44.87
Rank	#22	Overall
	#15	Relational DBMS

Score	0.05
Rank	#386	Overall
	#14	Vector DBMS

Website

www.sequoiadb.com

www.teradata.com

www.transwarp.cn/en/subproduct/hippo

Technical documentation

www.sequoiadb.com/en/index.php?m=Files&a=index

docs.teradata.com

Developer

Sequoiadb Ltd.

Teradata

Initial release

2013

1984

2023

Current release

Teradata Vantage 1.0 MU2, January 2019

1.0, May 2023

License

Commercial or Open Source

Open Source

Server: AGPL; Client: Apache V2

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

C++

Server operating systems

Linux

hosted
Linux

Linux
macOS

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

oid, date, timestamp, binary, regex

yes

Vector, Numeric and String

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

SQL-like query language

yes

SQL 2016 + extensions

APIs and other access methods

proprietary protocol using JSON

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

RESTful HTTP API

Supported programming languages

.Net
C++
Java
PHP
Python

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

C++
Java
Python

Server-side scripts

Stored procedures

JavaScript

yes

UDFs, stored procedures, table functions in parallel

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Hashing

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

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

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Document is locked during a transaction

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

simple password-based access control

fine grained access rights according to SQL-standard

Role based access control and fine grained access rights

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More resources

DB-Engines blog posts

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