DBMS > Apache Drill vs. JaguarDB vs. Netezza vs. Snowflake vs. Solr

System Properties Comparison Apache Drill vs. JaguarDB vs. Netezza vs. Snowflake vs. Solr

Editorial information provided by DB-Engines

Name

Description

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

Performant, highly scalable DBMS for AI and IoT applications

Data warehouse and analytics appliance part of IBM PureSystems

Cloud-based data warehousing service for structured and semi-structured data

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

Primary database model

Document store
Relational DBMS

Key-value store
Vector DBMS

Relational DBMS

Search engine

Secondary database models

Spatial DBMS

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

Score	2.28
Rank	#119	Overall
	#23	Document stores
	#56	Relational DBMS

Score	0.09
Rank	#356	Overall
	#53	Key-value stores
	#13	Vector DBMS

Score	10.18
Rank	#46	Overall
	#29	Relational DBMS

Score	123.20
Rank	#9	Overall
	#6	Relational DBMS

Score	44.28
Rank	#24	Overall
	#3	Search engines

Website

drill.apache.org

www.jaguardb.com

www.ibm.com/products/netezza

www.snowflake.com

solr.apache.org

Technical documentation

drill.apache.org/docs

www.jaguardb.com/support.html

docs.snowflake.net/manuals/index.html

solr.apache.org/resources.html

Developer

Apache Software Foundation

DataJaguar, Inc.

IBM

Snowflake Computing Inc.

Apache Software Foundation

Initial release

2012

2015

2000

2014

2006

Current release

1.20.3, January 2023

3.3 July 2023

9.5.0, February 2024

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

GPL V3.0

commercial

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

the server part. Clients available in other languages

Java

Server operating systems

Linux
OS X
Windows

Linux

included in appliance

hosted

All OS with a Java VM

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

Data scheme

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

yes

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

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

SQL

Support of SQL

SQL SELECT statement is SQL:2003 compliant

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

yes

Solr Parallel SQL Interface

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC

JDBC
ODBC
OLE DB

CLI Client
JDBC
ODBC

Java API
RESTful HTTP/JSON API

Supported programming languages

C++

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

C
C++
Fortran
Java
Lua
Perl
Python
R

JavaScript (Node.js)
Python

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

Server-side scripts

Stored procedures

user defined functions

yes

user defined functions

Java plugins

Triggers

similar concept for controling cloud resources

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

Depending on the underlying data source

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

Depending on the underlying data source

yes

User concepts

Access control

Depending on the underlying data source

rights management via user accounts

Users with fine-grained authorization concept

Users with fine-grained authorization concept, user roles and pluggable authentication

yes

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