DBMS > Amazon Redshift vs. Spark SQL vs. Transwarp Hippo vs. WakandaDB

System Properties Comparison Amazon Redshift vs. Spark SQL vs. Transwarp Hippo vs. WakandaDB

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

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

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

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

Relational DBMS

Vector DBMS

Object oriented DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.05
Rank	#386	Overall
	#15	Vector DBMS

Score	0.10
Rank	#356	Overall
	#16	Object oriented DBMS

Website

aws.amazon.com/redshift

spark.apache.org/sql

www.transwarp.cn/en/subproduct/hippo

wakanda.github.io

Technical documentation

docs.aws.amazon.com/redshift

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

wakanda.github.io/doc

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

Wakanda SAS

Initial release

2012

2014

2023

2012

Current release

3.5.0 ( 2.13), September 2023

1.0, May 2023

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Open Source

AGPLv3, extended commercial license available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Scala

C++

C++, JavaScript

Server operating systems

hosted

Linux
OS X
Windows

Linux
macOS

Linux
OS X
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

Vector, Numeric and 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.

Secondary indexes

restricted

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

Java
Python
R
Scala

C++
Java
Python

JavaScript

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

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

informational only, not enforced by the system

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

fine grained access rights according to SQL-standard

Role based access control and fine grained access rights

yes

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.