DBMS > Amazon CloudSearch vs. Hyprcubd vs. SAP HANA vs. Spark SQL vs. Teradata

System Properties Comparison Amazon CloudSearch vs. Hyprcubd vs. SAP HANA vs. Spark SQL vs. Teradata

Editorial information provided by DB-Engines

Name

Hyprcubd seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

Description

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

Serverless Time Series DBMS

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

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

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Search engine

Time Series DBMS

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	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Website

aws.amazon.com/cloudsearch

hyprcubd.com (offline)

www.sap.com/products/hana.html

spark.apache.org/sql

www.teradata.com

Technical documentation

docs.aws.amazon.com/cloudsearch

help.sap.com/hana

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

docs.teradata.com

Developer

Amazon

Hyprcubd, Inc.

SAP

Apache Software Foundation

Teradata

Initial release

2012

2010

2014

1984

Current release

2.0 SPS07 (April 4, 2023), April 2023

3.5.0 ( 2.13), September 2023

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

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

Scala

Server operating systems

hosted

Appliance or cloud-service

Linux
OS X
Windows

hosted
Linux

Data scheme

yes

Typing

predefined data types such as float or date

yes

time, int, uint, float, string

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

SQL-like query language

yes

SQL-like DML and DDL statements

yes

SQL 2016 + extensions

APIs and other access methods

HTTP API

gRPC (https)

JDBC
ODBC

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

Supported programming languages

Java
Python
R
Scala

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

Server-side scripts

Stored procedures

SQLScript, R

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

yes, utilizing Spark Core

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

yes

managed transparently by AWS

yes

none

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

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

authentication via encrypted signatures

token access

yes

fine grained access rights according to SQL-standard

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