DBMS > Hawkular Metrics vs. jBASE vs. SAP IQ vs. Vertica

System Properties Comparison Hawkular Metrics vs. jBASE vs. SAP IQ vs. Vertica

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Editorial information provided by DB-Engines

Name

Description

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

A robust multi-value DBMS comprising development tools and middleware

Columnar RDBMS optimized for Big Data analytics

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

column-oriented

Column oriented

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

Score	1.41
Rank	#159	Overall
	#3	Multivalue DBMS

Score	2.75
Rank	#104	Overall
	#53	Relational DBMS

Score	10.68
Rank	#43	Overall
	#27	Relational DBMS

Website

www.hawkular.org

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-jbase

www.sap.com/products/technology-platform/sybase-iq-big-data-management.html

www.vertica.com

Technical documentation

www.hawkular.org/hawkular-metrics/docs/user-guide

docs.rocketsoftware.com/bundle?labelkey=jbase_5.9

help.sap.com/docs/SAP_IQ

vertica.com/documentation

Developer

Community supported by Red Hat

Rocket Software (formerly Zumasys)

SAP, formerly Sybase

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2014

1991

1994

2005

Current release

5.7

16.1 SPS04, April 2019

12.0.3, January 2023

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C++

Server operating systems

Linux
OS X
Windows

AIX
Linux
Windows

AIX
HP-UX
Linux
Solaris
Windows

Linux

Data scheme

schema-free

yes

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

Typing

predefined data types such as float or date

yes

optional

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

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

Embedded SQL for jBASE in BASIC

yes

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

HTTP REST

JDBC
ODBC
Proprietary protocol
RESTful HTTP API
SOAP-based API

ADO.NET
JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

Go
Java
Python
Ruby

.Net
Basic
Jabbascript
Java

C
C#
C++
Java
Perl
PHP
Python
Ruby

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

yes

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

via Hawkular Alerting

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

shared disk or shared nothing architectures with SAP IQ Multiplexer

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

yes

SAP/Sybase Replication Server

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

Specific characteristics

Deploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
» more

Competitive advantages

Fast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
» more

Typical application scenarios

Communication and network analytics, Embedded analytics, Fraud monitoring and Risk...
» more

Key customers

Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» more

Licensing and pricing models

Cost-based models and subscription-based models are both available. One license is...
» more

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