DB-EnginesExtremeDB for everyone with an RTOSEnglish
Deutsch
Knowledge Base of Relational and NoSQL Database Management Systemsprovided by solid IT

DBMS > eXtremeDB vs. Google Cloud Firestore vs. Microsoft Azure Data Explorer vs. Oracle Berkeley DB

System Properties Comparison eXtremeDB vs. Google Cloud Firestore vs. Microsoft Azure Data Explorer vs. Oracle Berkeley DB

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines
NameeXtremeDB  Xexclude from comparisonGoogle Cloud Firestore  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonOracle Berkeley DB  Xexclude from comparison
DescriptionNatively in-memory DBMS with options for persistency, high-availability and clusteringCloud Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.Fully managed big data interactive analytics platformWidely used in-process key-value store
Primary database modelRelational DBMS
Time Series DBMS
Document storeRelational DBMS infocolumn orientedKey-value store infosupports sorted and unsorted key sets
Native XML DBMS infoin the Oracle Berkeley DB XML version
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
Score0.80
Rank#214  Overall
#99  Relational DBMS
#18  Time Series DBMS
Score7.36
Rank#53  Overall
#9  Document stores
Score3.80
Rank#81  Overall
#43  Relational DBMS
Score2.01
Rank#126  Overall
#21  Key-value stores
#3  Native XML DBMS
Websitewww.mcobject.comfirebase.google.com/­products/­firestoreazure.microsoft.com/­services/­data-explorerwww.oracle.com/­database/­technologies/­related/­berkeleydb.html
Technical documentationwww.mcobject.com/­docs/­extremedb.htmfirebase.google.com/­docs/­firestoredocs.microsoft.com/­en-us/­azure/­data-explorerdocs.oracle.com/­cd/­E17076_05/­html/­index.html
DeveloperMcObjectGoogleMicrosoftOracle infooriginally developed by Sleepycat, which was acquired by Oracle
Initial release2001201720191994
Current release8.2, 2021cloud service with continuous releases18.1.40, May 2020
License infoCommercial or Open SourcecommercialcommercialcommercialOpen Source infocommercial license available
Cloud-based only infoOnly available as a cloud servicenoyesyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC and C++C, Java, C++ (depending on the Berkeley DB edition)
Server operating systemsAIX
HP-UX
Linux
macOS
Solaris
Windows
hostedhostedAIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows
Data schemeyesschema-freeFixed schema with schema-less datatypes (dynamic)schema-free
Typing infopredefined data types such as float or dateyesyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesno
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.no infosupport of XML interfaces availablenoyesyes infoonly with the Berkeley DB XML edition
Secondary indexesyesyesall fields are automatically indexedyes
SQL infoSupport of SQLyes infowith the option: eXtremeSQLnoKusto Query Language (KQL), SQL subsetyes infoSQL interfaced based on SQLite is available
APIs and other access methods.NET Client API
JDBC
JNI
ODBC
Proprietary protocol
RESTful HTTP API
Android
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Supported programming languages.Net
C
C#
C++
Java
Lua
Python
Scala
Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
.Net infoFigaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others infoThird-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js) info3rd party binding
Perl
Python
Tcl
Server-side scripts infoStored proceduresyesyes, Firebase Rules & Cloud FunctionsYes, possible languages: KQL, Python, Rno
Triggersyes infoby defining eventsyes, with Cloud Functionsyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyes infoonly for the SQL API
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning / shardingShardingSharding infoImplicit feature of the cloud servicenone
Replication methods infoMethods for redundantly storing data on multiple nodesActive Replication Fabric™ for IoT
Multi-source replication infoby means of eXtremeDB Cluster option
Source-replica replication infoby means of eXtremeDB High Availability option
Multi-source replicationyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoUsing Cloud DataflowSpark connector (open source): github.com/­Azure/­azure-kusto-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integrityyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDyesnoACID
Concurrency infoSupport for concurrent manipulation of datayes infoOptimistic (MVCC) and pessimistic (locking) strategies availableyesyes
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.yesnoyes
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.Azure Active Directory Authenticationno
More information provided by the system vendor
eXtremeDBGoogle Cloud FirestoreMicrosoft Azure Data ExplorerOracle Berkeley DB
Specific characteristicseXtremeDB is an in-memory and/or persistent database system that offers an ultra-small...
» more
Competitive advantageseXtremeDB databases can be modeled relationally or as objects and can utilize SQL...
» more
Typical application scenariosIoT application across all markets: Industrial Control, Netcom, Telecom, Defense,...
» more
Key customersSchneider Electronics, F5 Networks, TNS, Boeing, Northrop Grumman, GoPro, ViaSat,...
» more
Market metricsWith hundreds of customers and over 30 million devices/applications using the product...
» more
Licensing and pricing modelsFor server use cases, there is a simple per-server license irrespective of the number...
» more

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
eXtremeDBGoogle Cloud FirestoreMicrosoft Azure Data ExplorerOracle Berkeley DB
DB-Engines blog posts

Cloud-based DBMS's popularity grows at high rates
12 December 2019, Paul Andlinger

show all

Recent citations in the news

Latest embedded DBMS supports asymmetric multiprocessing systems
24 May 2023, Embedded

McObject Delivers eXtremeDB 8.4 Improving Performance, Security, and Developer Productivity
13 May 2024, Embedded Computing Design

McObject LLC Joins STMicroelectronics Partner Program to Expand, Enhance and Accelerate Customer
6 June 2024, EIN News

The Data in Hard Real-time SCADA Systems Lets Companies Do More with Less
11 August 2023, Automation.com

McObject Announces the Release of eXtremeDB/rt 1.2
23 May 2023, Embedded Computing Design

provided by Google News

Google's AI-First Strategy Brings Vector Support To Cloud Databases
1 March 2024, Forbes

Realtime vs Cloud Firestore: Which Firebase Database to go?
8 March 2024, Appinventiv

Google launches Firebase Genkit, a new open source framework for building AI-powered apps
14 May 2024, Yahoo Canada Finance

Google's Cloud Firestore is now generally available
31 January 2019, ZDNet

Google launches Cloud Firestore, a new document database for app developers
3 October 2017, TechCrunch

provided by Google News

We’re retiring Azure Time Series Insights on 7 July 2024 – transition to Azure Data Explorer | Azure updates
31 May 2024, azure.microsoft.com

Update records in a Kusto Database (public preview) | Azure updates
20 February 2024, azure.microsoft.com

Public Preview: Azure Data Explorer connector for Apache Flink | Azure updates
8 January 2024, azure.microsoft.com

Announcing General Availability to migrate Virtual Network injected Azure Data Explorer Cluster to Private Endpoints ...
5 February 2024, azure.microsoft.com

New Features for graph-match KQL Operator: Enhanced Pattern Matching and Cycle Control | Azure updates
24 January 2024, azure.microsoft.com

provided by Google News

Margo Seltzer Named ACM Athena Lecturer for Technical and Mentoring Contributions
26 April 2023, Datanami

ACM recognizes far-reaching technical achievements with special awards
26 May 2021, EurekAlert

Oracle buys Sleepycat Software
14 February 2006, MarketWatch

Database Trends Report: SQL Beats NoSQL, MySQL Most Popular -- ADTmag
5 March 2019, ADT Magazine

Margo I. Seltzer | Berkman Klein Center
18 August 2020, Berkman Klein Center

provided by Google News



Share this page

Featured Products

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

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.

Present your product here