DBMS > Hive vs. IBM Db2 Event Store vs. Transwarp Hippo vs. WakandaDB

System Properties Comparison Hive vs. IBM Db2 Event Store vs. Transwarp Hippo vs. WakandaDB

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Editorial information provided by DB-Engines

Name

Description

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Distributed Event Store optimized for Internet of Things use cases

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

Event Store
Time Series DBMS

Vector DBMS

Object oriented DBMS

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

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	0.00
Rank	#383	Overall
	#13	Vector DBMS

Score	0.03
Rank	#364	Overall
	#17	Object oriented DBMS

Website

hive.apache.org

www.ibm.com/products/db2-event-store

www.transwarp.cn/en/subproduct/hippo

wakanda.github.io

Technical documentation

cwiki.apache.org/confluence/display/Hive/Home

www.ibm.com/docs/en/db2-event-store

wakanda.github.io/doc

Developer

Apache Software Foundation

initially developed by Facebook

IBM

Wakanda SAS

Initial release

2012

2017

2023

2012

Current release

3.1.3, April 2022

2.0

1.0, May 2023

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

free developer edition available

commercial

Open Source

AGPLv3, extended commercial license available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C and C++

C++

C++, JavaScript

Server operating systems

All OS with a Java VM

Linux

Linux, macOS, Windows for the developer addition

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

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

through the embedded Spark runtime

APIs and other access methods

JDBC
ODBC
Thrift

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

RESTful HTTP API

Supported programming languages

C++
Java
PHP
Python

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

C++
Java
Python

JavaScript

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Active-active shard replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

yes

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

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

Access rights for users, groups and roles

fine grained access rights according to SQL-standard

Role based access control and fine grained access rights

yes

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More resources

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