DBMS > Amazon CloudSearch vs. SAP HANA vs. Splice Machine vs. Teradata

System Properties Comparison Amazon CloudSearch vs. SAP HANA vs. Splice Machine vs. Teradata

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Editorial information provided by DB-Engines

Name

Description

A hosted search engine service by Amazon with the data stored in Amazons cloud

In-memory, column based data store. Available as appliance or cloud service

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

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Search engine

Relational DBMS

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	1.85
Rank	#137	Overall
	#12	Search engines

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Website

aws.amazon.com/cloudsearch

www.sap.com/products/hana.html

splicemachine.com

www.teradata.com

Technical documentation

docs.aws.amazon.com/cloudsearch

help.sap.com/hana

splicemachine.com/how-it-works

docs.teradata.com

Developer

Amazon

SAP

Splice Machine

Teradata

Initial release

2012

2010

2014

1984

Current release

2.0 SPS07 (April 4, 2023), April 2023

3.1, March 2021

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

commercial

Open Source

AGPL 3.0, commercial license available

commercial

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

hosted

Appliance or cloud-service

Linux
OS X
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.

yes

Secondary indexes

yes

all search fields are automatically indexed

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

HTTP API

JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

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

Supported programming languages

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

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

Server-side scripts

Stored procedures

SQLScript, R

yes

Java

yes

UDFs, stored procedures, table functions in parallel

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

automatic partitioning across Amazon Search Instance as required

yes

Shared Nothhing Auto-Sharding, Columnar Partitioning

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

yes

managed transparently by AWS

yes

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

In-memory capabilities

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

yes

User concepts

Access control

authentication via encrypted signatures

yes

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

fine grained access rights according to SQL-standard

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