DBMS > Couchbase vs. Microsoft Azure Synapse Analytics vs. Netezza vs. Solr vs. Sqrrl

System Properties Comparison Couchbase vs. Microsoft Azure Synapse Analytics vs. Netezza vs. Solr vs. Sqrrl

Editorial information provided by DB-Engines

Name

Sqrrl has been acquired by Amazon and became a part of Amazon Web Services. It has been removed from the DB-Engines ranking.

Description

A distributed document store with integrated cache, a powerful search engine, in-built operational and analytical capabilities, and an embedded mobile database

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

Data warehouse and analytics appliance part of IBM PureSystems

A widely used distributed, scalable search engine based on Apache Lucene

Adaptable, secure NoSQL built on Apache Accumulo

Primary database model

Document store

Relational DBMS

Search engine

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Key-value store

originating from the former Membase product and supporting the Memcached protocol
Spatial DBMS

using the Geocouch extension
Search engine
Time Series DBMS
Vector DBMS

Spatial DBMS

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

Score	16.59
Rank	#36	Overall
	#5	Document stores

Score	19.93
Rank	#31	Overall
	#19	Relational DBMS

Score	8.59
Rank	#45	Overall
	#29	Relational DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

www.couchbase.com

azure.microsoft.com/services/synapse-analytics

www.ibm.com/products/netezza

solr.apache.org

sqrrl.com

Technical documentation

docs.couchbase.com

docs.microsoft.com/azure/synapse-analytics

solr.apache.org/resources.html

Developer

Couchbase, Inc.

Microsoft

IBM

Apache Software Foundation

Amazon

originally Sqrrl Data, Inc.

Initial release

2011

2016

2000

2006

2012

Current release

Server: 7.2, June 2023; Mobile: 3.1, March 2022; Couchbase Capella (DBaaS), June 2023

9.6.1, May 2024

License

Commercial or Open Source

Open Source

Business Source License (BSL 1.1); Commercial licenses also available

commercial

Open Source

Apache Version 2

commercial

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

C, C++, Go and Erlang

C++

Java

Server operating systems

Linux
OS X
Windows

hosted

Linux

included in appliance

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Linux

Data scheme

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

schema-free

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

All search fields are automatically indexed

yes

SQL

Support of SQL

SQL++, extends ANSI SQL to JSON for operational, transactional, and analytic use cases

yes

Solr Parallel SQL Interface

APIs and other access methods

CLI Client
HTTP REST
Kafka Connector
Native language bindings for CRUD, Query, Search and Analytics APIs
Spark Connector
Spring Data

ADO.NET
JDBC
ODBC

JDBC
ODBC
OLE DB

Java API
RESTful HTTP/JSON API

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

Supported programming languages

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

C#
Java
PHP

C
C++
Fortran
Java
Lua
Perl
Python
R

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Actionscript
C

using GLib
C#
C++
Cocoa
Delphi
Erlang
Go
Haskell
Java
JavaScript
OCaml
Perl
PHP
Python
Ruby
Smalltalk

Server-side scripts

Stored procedures

Functions and timers in JavaScript and UDFs in Java, Python, SQL++

Transact SQL

yes

Java plugins

Triggers

yes

via the TAP protocol

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Automatic Sharding

Sharding, horizontal partitioning

Sharding

making use of Hadoop

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

including cross data center replication
Source-replica replication

yes

Source-replica replication

yes

selectable replication factor

making use of Hadoop

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Immediate Consistency

Eventual Consistency

Immediate Consistency

Document store kept consistent with combination of global timestamping, row-level transactions, and server-side consistency resolution.

Foreign keys

Referential integrity

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

Transaction concepts

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

ACID

optimistic locking

Atomic updates per row, document, or graph entity

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

Ephemeral buckets

yes

User concepts

Access control

User and Administrator separation with password-based and LDAP integrated Authentication. Role-base access control.

yes

Users with fine-grained authorization concept

yes

Cell-level Security, Data-Centric Security, Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)

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