DBMS > Google BigQuery vs. HarperDB vs. Infobright vs. SAP SQL Anywhere vs. Splunk

System Properties Comparison Google BigQuery vs. HarperDB vs. Infobright vs. SAP SQL Anywhere vs. Splunk

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontend

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

Analytics Platform for Big Data

Primary database model

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Trend Chart

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.55
Rank	#248	Overall
	#38	Document stores

Score	0.96
Rank	#194	Overall
	#91	Relational DBMS

Score	4.25
Rank	#79	Overall
	#43	Relational DBMS

Score	86.45
Rank	#14	Overall
	#2	Search engines

Website

cloud.google.com/bigquery

www.harperdb.io

ignitetech.com/softwarelibrary/infobrightdb

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

www.splunk.com

Technical documentation

cloud.google.com/bigquery/docs

docs.harperdb.io/docs

help.sap.com/docs/SAP_SQL_Anywhere

docs.splunk.com/Documentation/Splunk

Developer

Google

HarperDB

Ignite Technologies Inc.; formerly InfoBright Inc.

SAP

formerly Sybase

Splunk Inc.

Initial release

2010

2017

2005

1992

2003

Current release

3.1, August 2021

17, July 2015

License

Commercial or Open Source

commercial

free community edition available

commercial

The open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016

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

Node.js

Server operating systems

hosted

Linux
OS X

Linux
Windows

AIX
HP-UX
Linux
OS X
Solaris
Windows

Linux
OS X
Solaris
Windows

Data scheme

yes

dynamic schema

yes

Typing

predefined data types such as float or date

yes

JSON data types

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

Knowledge Grid Technology used instead

yes

SQL

Support of SQL

yes

SQL-like data manipulation statements

yes

Splunk Search Processing Language for search commands

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

ADO.NET
JDBC
ODBC

ADO.NET
HTTP API
JDBC
ODBC

HTTP REST

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

.Net
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

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

C#
Java
JavaScript
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Custom Functions

since release 3.1

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

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

A table resides as a whole on one (or more) nodes in a cluster

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

the nodes on which a table resides can be defined

Source-replica replication

Database mirroring

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Since BigQuery is designed for querying data

Atomic execution of specific operations

ACID

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

yes, using LMDB

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

Google Cloud Identity & Access Management (IAM)

Access rights for users and roles

fine grained access rights according to SQL-standard

exploiting MySQL or PostgreSQL frontend capabilities

fine grained access rights according to SQL-standard

Access rights for users and roles

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