DBMS > HBase vs. Microsoft SQL Server vs. Newts vs. PouchDB vs. Vertica

System Properties Comparison HBase vs. Microsoft SQL Server vs. Newts vs. PouchDB vs. Vertica

Editorial information provided by DB-Engines

Name

Description

Wide-column store based on Apache Hadoop and on concepts of BigTable

Microsofts flagship relational DBMS

Time Series DBMS based on Cassandra

JavaScript DBMS with an API inspired by CouchDB

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

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Spatial DBMS
Time Series DBMS

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

Score	31.25
Rank	#26	Overall
	#2	Wide column stores

Score	829.80
Rank	#3	Overall
	#3	Relational DBMS

Score	0.00
Rank	#396	Overall
	#42	Time Series DBMS

Score	2.35
Rank	#116	Overall
	#22	Document stores

Score	11.40
Rank	#43	Overall
	#27	Relational DBMS

Website

hbase.apache.org

www.microsoft.com/en-us/sql-server

opennms.github.io/newts

pouchdb.com

www.vertica.com

Technical documentation

hbase.apache.org/book.html

learn.microsoft.com/en-US/sql/sql-server

github.com/OpenNMS/newts/wiki

pouchdb.com/guides

vertica.com/documentation

Developer

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Microsoft

OpenNMS Group

Apache Software Foundation

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2008

1989

2014

2012

2005

Current release

2.3.4, January 2021

SQL Server 2022, November 2022

7.1.1, June 2019

12.0.3, January 2023

License

Commercial or Open Source

Open Source

Apache version 2

commercial

restricted free version is available

Open Source

Apache 2.0

Open Source

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++

Java

JavaScript

C++

Server operating systems

Linux
Unix
Windows

using Cygwin

Linux
Windows

Linux
OS X
Windows

server-less, requires a JavaScript environment (browser, Node.js)

Linux

Data scheme

schema-free, schema definition possible

yes

schema-free

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

options to bring your own types, AVRO

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

via views

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

SQL

Support of SQL

yes

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

APIs and other access methods

Java API
RESTful HTTP API
Thrift

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

HTTP REST
Java API

HTTP REST

only for PouchDB Server
JavaScript API

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

character-based, interactive, front-end utility

Supported programming languages

C
C#
C++
Groovy
Java
PHP
Python
Scala

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

Java

JavaScript

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

Server-side scripts

Stored procedures

yes

Coprocessors in Java

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

View functions in JavaScript

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

based on Cassandra

Sharding

with a proxy-based framework, named couchdb-lounge

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes, but depending on the SQL-Server Edition

selectable replication factor

based on Cassandra

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

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

yes

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Single row ACID (across millions of columns)

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

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 Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

fine grained access rights according to SQL-standard

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

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