DBMS > Hive vs. IBM Db2 Event Store vs. Machbase Neo vs. SpaceTime vs. Yaacomo

System Properties Comparison Hive vs. IBM Db2 Event Store vs. Machbase Neo vs. SpaceTime vs. Yaacomo

Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

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

Distributed Event Store optimized for Internet of Things use cases

TimeSeries DBMS for AIoT and BigData

SpaceTime is a spatio-temporal DBMS with a focus on performance.

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Relational DBMS

Event Store
Time Series DBMS

Time Series DBMS

Spatial DBMS

Relational DBMS

Secondary database models

Relational DBMS

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

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	0.27
Rank	#309	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	0.17
Rank	#337	Overall
	#30	Time Series DBMS

Score	0.03
Rank	#392	Overall
	#8	Spatial DBMS

Website

hive.apache.org

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

machbase.com

www.mireo.com/spacetime

yaacomo.com

Technical documentation

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

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

machbase.com/dbms

Developer

Apache Software Foundation

initially developed by Facebook

IBM

Machbase

Mireo

Q2WEB GmbH

Initial release

2012

2017

2013

2020

2009

Current release

3.1.3, April 2022

2.0

V8.0, August 2023

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

free developer edition available

commercial

free test version available

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

Java

C and C++

C++

Server operating systems

All OS with a Java VM

Linux

Linux, macOS, Windows for the developer addition

Linux
macOS
Windows

Linux

Android
Linux
Windows

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

through the embedded Spark runtime

SQL-like query language

A subset of ANSI SQL is implemented

yes

APIs and other access methods

JDBC
ODBC
Thrift

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

RESTful HTTP API

JDBC
ODBC

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
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

C#
C++
Python

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

Fixed-grid hypercubes

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Active-active shard replication

selectable replication factor

Real-time block device replication (DRBD)

Source-replica replication

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

yes

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

volatile and lookup table

yes

User concepts

Access control

Access rights for users, groups and roles

fine grained access rights according to SQL-standard

simple password-based access control

yes

fine grained access rights according to SQL-standard

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.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

Why is Hadoop not listed in the DB-Engines Ranking?
13 May 2013, Paul Andlinger

show all

Recent citations in the news

Apache Software Foundation Announces Apache Hive 4.0
30 April 2024, Datanami

Design a data mesh pattern for Amazon EMR-based data lakes using AWS Lake Formation with Hive metastore ...
10 June 2024, AWS Blog

18 Top Big Data Tools and Technologies to Know About in 2024
24 January 2024, TechTarget

ASF Unveils the Next Evolution of Big Data Processing With the Launch of Hive 4.0
2 May 2024, Datanami

Run Apache Hive workloads using Spark SQL with Amazon EMR on EKS | Amazon Web Services
18 October 2023, AWS Blog

provided by Google News

Advancements in streaming data storage, real-time analysis and machine learning
25 July 2019, ibm.com

How IBM Is Turning Db2 into an ‘AI Database’
3 June 2019, Datanami

Best cloud databases of 2022
4 October 2022, ITPro

Why a robust data management strategy is essential today | IBM HDM
19 September 2019, Express Computer

provided by Google News

Share this page