DBMS > BigObject vs. Microsoft Azure Data Explorer vs. Splunk

System Properties Comparison BigObject vs. Microsoft Azure Data Explorer vs. Splunk

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

Fully managed big data interactive analytics platform

Analytics Platform for Big Data

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

column oriented

Search engine

Secondary database models

Document store

If a column is of type dynamic docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types/dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store

this is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine

support for complex search expressions docs.microsoft.com/en-us/azure/kusto/query/parseoperator FTS, Geospatial docs.microsoft.com/en-us/azure/kusto/query/geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS

see docs.microsoft.com/en-us/azure/data-explorer/time-series-analysis

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

Score	0.14
Rank	#332	Overall
	#147	Relational DBMS

Score	2.94
Rank	#87	Overall
	#48	Relational DBMS

Score	78.87
Rank	#15	Overall
	#2	Search engines

Website

bigobject.io

azure.microsoft.com/services/data-explorer

www.splunk.com

Technical documentation

docs.bigobject.io

docs.microsoft.com/en-us/azure/data-explorer

docs.splunk.com/Documentation/Splunk

Developer

BigObject, Inc.

Microsoft

Splunk Inc.

Initial release

2015

2019

2003

Current release

cloud service with continuous releases

License

Commercial or Open Source

commercial

free community edition available

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.

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

hosted

Linux
OS X
Solaris
Windows

Data scheme

yes

Fixed schema with schema-less datatypes (dynamic)

yes

Typing

predefined data types such as float or date

yes

bool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/en-us/azure/kusto/query/scalar-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

all fields are automatically indexed

yes

SQL

Support of SQL

SQL-like DML and DDL statements

Kusto Query Language (KQL), SQL subset

Splunk Search Processing Language for search commands

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API

HTTP REST

Supported programming languages

.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R

C#
Java
JavaScript
PHP
Python
Ruby

Server-side scripts

Stored procedures

Lua

Yes, possible languages: KQL, Python, R

yes

Triggers

yes

see docs.microsoft.com/en-us/azure/kusto/management/updatepolicy

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Spark connector (open source): github.com/Azure/azure-kusto-spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency
Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

Transaction concepts

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

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

Read/write lock on objects (tables, trees)

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

Azure Active Directory Authentication

Access rights for users and roles

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

Enterprise Search Engines almost double their popularity in the last 12 months
2 July 2014, Paul Andlinger

show all

Recent citations in the news

Universal Destinations & Experiences proactively improves guest satisfaction with Azure Data Explorer
14 February 2025, Microsoft

Log and Telemetry Analytics Performance Benchmark
16 August 2022, Gigaom

Azure Data Explorer: Log and telemetry analytics benchmark
16 August 2022, Microsoft

Microsoft announces general availability of Azure Data Explorer and Azure Data Lake Storage Gen2
7 February 2019, VentureBeat

Azure Data Explorer Supports Native Ingestion from Amazon S3
7 September 2022, InfoQ.com

provided by Google News

Share this page