DB-EnginesInfluxDB: Focus on building software with an easy-to-use serverless, scalable time series platformEnglish
Deutsch
Knowledge Base of Relational and NoSQL Database Management Systemsprovided by solid IT

DBMS > EJDB vs. Ignite vs. InfinityDB vs. Microsoft Azure Data Explorer

System Properties Comparison EJDB vs. Ignite vs. InfinityDB vs. Microsoft Azure Data Explorer

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines
NameEJDB  Xexclude from comparisonIgnite  Xexclude from comparisonInfinityDB  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparison
DescriptionEmbeddable document-store database library with JSON representation of queries (in MongoDB style)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.A Java embedded Key-Value Store which extends the Java Map interfaceFully managed big data interactive analytics platform
Primary database modelDocument storeKey-value store
Relational DBMS
Key-value storeRelational DBMS infocolumn oriented
Secondary database modelsDocument store infoIf 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 infothis 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 infosupport 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 infosee docs.microsoft.com/­en-us/­azure/­data-explorer/­time-series-analysis
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.27
Rank#297  Overall
#44  Document stores
Score3.16
Rank#96  Overall
#15  Key-value stores
#49  Relational DBMS
Score0.00
Rank#378  Overall
#57  Key-value stores
Score4.38
Rank#77  Overall
#41  Relational DBMS
Websitegithub.com/­Softmotions/­ejdbignite.apache.orgboilerbay.comazure.microsoft.com/­services/­data-explorer
Technical documentationgithub.com/­Softmotions/­ejdb/­blob/­master/­README.mdapacheignite.readme.io/­docsboilerbay.com/­infinitydb/­manualdocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperSoftmotionsApache Software FoundationBoiler Bay Inc.Microsoft
Initial release2012201520022019
Current releaseApache Ignite 2.64.0cloud service with continuous releases
License infoCommercial or Open SourceOpen Source infoGPLv2Open Source infoApache 2.0commercialcommercial
Cloud-based only infoOnly available as a cloud servicenononoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageCC++, Java, .NetJava
Server operating systemsserver-lessLinux
OS X
Solaris
Windows
All OS with a Java VMhosted
Data schemeschema-freeyesyes infonested virtual Java Maps, multi-value, logical ‘tuple space’ runtime Schema upgradeFixed schema with schema-less datatypes (dynamic)
Typing infopredefined data types such as float or dateyes infostring, integer, double, bool, date, object_idyesyes infoall Java primitives, Date, CLOB, BLOB, huge sparse arraysyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-types
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.yesnoyes
Secondary indexesnoyesno infomanual creation possible, using inversions based on multi-value capabilityall fields are automatically indexed
SQL infoSupport of SQLnoANSI-99 for query and DML statements, subset of DDLnoKusto Query Language (KQL), SQL subset
APIs and other access methodsin-process shared libraryHDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/­docs/­ItemSpaceDataStructures.htm)
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Supported programming languagesActionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby
C#
C++
Java
PHP
Python
Ruby
Scala
Java.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Server-side scripts infoStored proceduresnoyes (compute grid and cache interceptors can be used instead)noYes, possible languages: KQL, Python, R
Triggersnoyes (cache interceptors and events)noyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicy
Partitioning methods infoMethods for storing different data on different nodesnoneShardingnoneSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesnoneyes (replicated cache)noneyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes (compute grid and hadoop accelerator)noSpark connector (open source): github.com/­Azure/­azure-kusto-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency infoREAD-COMMITTED or SERIALIZEDEventual Consistency
Immediate Consistency
Foreign keys infoReferential integrityno infotypically not needed, however similar functionality with collection joins possiblenono infomanual creation possible, using inversions based on multi-value capabilityno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACID infoOptimistic locking for transactions; no isolation for bulk loadsno
Concurrency infoSupport for concurrent manipulation of datayes infoRead/Write Lockingyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnono
User concepts infoAccess controlnoSecurity Hooks for custom implementationsnoAzure Active Directory 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.

Related products and services

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

More resources
EJDBIgniteInfinityDBMicrosoft Azure Data Explorer
Recent citations in the news

GridGain Announces Call for Speakers for Virtual Apache Ignite Summit 2024
8 February 2024, PR Newswire

Apache Ignite: An Overview
6 September 2023, Open Source For You

GridGain Releases Conference Schedule for Virtual Apache Ignite Summit 2023
1 June 2023, Datanami

What is Apache Ignite? How is Apache Ignite Used?
18 July 2022, The Stack

Real-time in-memory OLTP and Analytics with Apache Ignite on AWS | Amazon Web Services
14 May 2016, AWS Blog

provided by Google News

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

Providing modern data transfer and storage service at Microsoft with Microsoft Azure - Inside Track Blog
13 July 2023, Microsoft

Introducing Microsoft Fabric: The data platform for the era of AI | Microsoft Azure Blog
23 May 2023, Microsoft

Azure Data Explorer and Stream Analytics for anomaly detection
16 January 2020, Microsoft

Controlling costs in Azure Data Explorer using down-sampling and aggregation
11 February 2019, Microsoft

provided by Google News



Share this page

Featured Products

SingleStore logo

Build AI apps with Vectors on SQL and JSON with milliseconds response times.
Try it today.

RaimaDB logo

RaimaDB, embedded database for mission-critical applications. When performance, footprint and reliability matters.
Try RaimaDB for free.

Milvus logo

Vector database designed for GenAI, fully equipped for enterprise implementation.
Try Managed Milvus for Free

Neo4j logo

See for yourself how a graph database can make your life easier.
Use Neo4j online for free.

Datastax Astra logo

Bring all your data to Generative AI applications with vector search enabled by the most scalable
vector database available.
Try for Free

Present your product here