DBMS > Elasticsearch vs. Ignite vs. Microsoft Azure Data Explorer vs. Oracle Berkeley DB vs. Snowflake

System Properties Comparison Elasticsearch vs. Ignite vs. Microsoft Azure Data Explorer vs. Oracle Berkeley DB vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

A distributed, RESTful modern search and analytics engine based on Apache Lucene

Elasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metric

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

Fully managed big data interactive analytics platform

Widely used in-process key-value store

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

Primary database model

Search engine

Key-value store
Relational DBMS

Relational DBMS

column oriented

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Relational DBMS

Secondary database models

Document store
Spatial DBMS
Vector DBMS

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	135.35
Rank	#7	Overall
	#1	Search engines

Score	3.16
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

www.elastic.co/elasticsearch

ignite.apache.org

azure.microsoft.com/services/data-explorer

www.oracle.com/database/technologies/related/berkeleydb.html

www.snowflake.com

Technical documentation

www.elastic.co/guide/en/elasticsearch/reference/current/index.html

apacheignite.readme.io/docs

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

docs.oracle.com/cd/E17076_05/html/index.html

docs.snowflake.net/manuals/index.html

Developer

Elastic

Apache Software Foundation

Microsoft

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Snowflake Computing Inc.

Initial release

2010

2015

2019

1994

2014

Current release

8.6, January 2023

Apache Ignite 2.6

cloud service with continuous releases

18.1.40, May 2020

License

Commercial or Open Source

Open Source

Elastic License

Open Source

Apache 2.0

commercial

Open Source

commercial license available

commercial

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

Java

C++, Java, .Net

C, Java, C++ (depending on the Berkeley DB edition)

Server operating systems

All OS with a Java VM

Linux
OS X
Solaris
Windows

hosted

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

hosted

Data scheme

schema-free

Flexible type definitions. Once a type is defined, it is persistent

yes

Fixed schema with schema-less datatypes (dynamic)

schema-free

yes

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

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

only with the Berkeley DB XML edition

yes

Secondary indexes

yes

All search fields are automatically indexed

yes

all fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

ANSI-99 for query and DML statements, subset of DDL

Kusto Query Language (KQL), SQL subset

yes

SQL interfaced based on SQLite is available

yes

APIs and other access methods

Java API
RESTful HTTP/JSON API

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

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

CLI Client
JDBC
ODBC

Supported programming languages

.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

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

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

yes (compute grid and cache interceptors can be used instead)

Yes, possible languages: KQL, Python, R

user defined functions

Triggers

yes

by using the 'percolation' feature

yes (cache interceptors and events)

yes

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

yes

only for the SQL API

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes (replicated cache)

yes

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

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

ES-Hadoop Connector

yes (compute grid and hadoop accelerator)

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

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

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.

Memcached and Redis integration

yes

User concepts

Access control

Security Hooks for custom implementations

Azure Active Directory Authentication

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

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.