DBMS > EXASOL vs. Google BigQuery vs. Microsoft SQL Server vs. Solr vs. Splunk

System Properties Comparison EXASOL vs. Google BigQuery vs. Microsoft SQL Server vs. Solr vs. Splunk

Editorial information provided by DB-Engines

Name

Description

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

Large scale data warehouse service with append-only tables

Microsofts flagship relational DBMS

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

Analytics Platform for Big Data

Primary database model

Relational DBMS

Search engine

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Spatial DBMS

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

Score	2.25
Rank	#120	Overall
	#57	Relational DBMS

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	829.80
Rank	#3	Overall
	#3	Relational DBMS

Score	44.28
Rank	#24	Overall
	#3	Search engines

Score	88.71
Rank	#14	Overall
	#2	Search engines

Website

www.exasol.com

cloud.google.com/bigquery

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

solr.apache.org

www.splunk.com

Technical documentation

www.exasol.com/resources

cloud.google.com/bigquery/docs

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

solr.apache.org/resources.html

docs.splunk.com/Documentation/Splunk

Developer

Exasol

Google

Microsoft

Apache Software Foundation

Splunk Inc.

Initial release

2000

2010

1989

2006

2003

Current release

SQL Server 2022, November 2022

9.5.0, February 2024

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

Apache Version 2

commercial

Limited free edition and free developer edition available

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++

Java

Server operating systems

hosted

Linux
Windows

All OS with a Java VM

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

Linux
OS X
Solaris
Windows

Data scheme

yes

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

yes

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

yes

Solr Parallel SQL Interface

Splunk Search Processing Language for search commands

APIs and other access methods

.Net
JDBC
ODBC
WebSocket

RESTful HTTP/JSON API

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

Java API
RESTful HTTP/JSON API

HTTP REST

Supported programming languages

Java
Lua
Python
R

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

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

C#
Java
JavaScript
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in JavaScript

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

Java plugins

yes

Triggers

yes

User configurable commands triggered on index changes

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, but depending on the SQL-Server Edition

yes

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

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

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

optimistic locking

A 'Transaction' in Splunk has a different meaning: grouping related events into a single one for later searching

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 for users, groups and roles according to SQL-standard

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

yes

Access rights for users and roles

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