DBMS > Apache Impala vs. SAP SQL Anywhere vs. Spark SQL vs. Splunk vs. SQLite

System Properties Comparison Apache Impala vs. SAP SQL Anywhere vs. Spark SQL vs. Splunk vs. SQLite

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

Spark SQL is a component on top of 'Spark Core' for structured data processing

Analytics Platform for Big Data

Widely used embeddable, in-process RDBMS

Primary database model

Relational DBMS

Search engine

Relational DBMS

Secondary database models

Document store

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	3.95
Rank	#80	Overall
	#42	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	89.10
Rank	#14	Overall
	#2	Search engines

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Website

impala.apache.org

www.sap.com/products/technology-platform/sql-anywhere.html

spark.apache.org/sql

www.splunk.com

www.sqlite.org

Technical documentation

impala.apache.org/impala-docs.html

help.sap.com/docs/SAP_SQL_Anywhere

spark.apache.org/docs/latest/sql-programming-guide.html

docs.splunk.com/Documentation/Splunk

www.sqlite.org/docs.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

SAP

formerly Sybase

Apache Software Foundation

Splunk Inc.

Dwayne Richard Hipp

Initial release

2013

1992

2014

2003

2000

Current release

4.1.0, June 2022

17, July 2015

3.5.0 ( 2.13), September 2023

3.46.0 (23 May 2024), May 2024

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

Apache 2.0

commercial

Limited free edition and free developer edition available

Open Source

Public Domain

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

Scala

Server operating systems

Linux

AIX
HP-UX
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Linux
OS X
Solaris
Windows

server-less

Data scheme

yes

dynamic column types

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

SQL

Support of SQL

SQL-like DML and DDL statements

yes

SQL-like DML and DDL statements

Splunk Search Processing Language for search commands

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC
ODBC

ADO.NET
HTTP API
JDBC
ODBC

JDBC
ODBC

HTTP REST

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Java
Python
R
Scala

C#
Java
JavaScript
PHP
Python
Ruby

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes, in C/C++, Java, .Net or Perl

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes, utilizing Spark Core

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication

Database mirroring

none

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

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

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

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

based on Apache Sentry and Kerberos

fine grained access rights according to SQL-standard

Access rights for users and roles

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