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

DBMS > Apache Druid vs. Google Cloud Bigtable vs. Google Cloud Firestore vs. Microsoft Azure Data Explorer

System Properties Comparison Apache Druid vs. Google Cloud Bigtable vs. Google Cloud Firestore vs. Microsoft Azure Data Explorer

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonGoogle Cloud Firestore  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparison
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.Cloud 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 platform
Primary database modelRelational DBMS
Time Series DBMS
Key-value store
Wide column store
Document 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
Score3.25
Rank#90  Overall
#47  Relational DBMS
#7  Time Series DBMS
Score3.15
Rank#95  Overall
#14  Key-value stores
#8  Wide column stores
Score7.36
Rank#53  Overall
#9  Document stores
Score3.80
Rank#81  Overall
#43  Relational DBMS
Websitedruid.apache.orgcloud.google.com/­bigtablefirebase.google.com/­products/­firestoreazure.microsoft.com/­services/­data-explorer
Technical documentationdruid.apache.org/­docs/­latest/­designcloud.google.com/­bigtable/­docsfirebase.google.com/­docs/­firestoredocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperApache Software Foundation and contributorsGoogleGoogleMicrosoft
Initial release2012201520172019
Current release29.0.1, April 2024cloud service with continuous releases
License infoCommercial or Open SourceOpen Source infoApache license v2commercialcommercialcommercial
Cloud-based only infoOnly available as a cloud servicenoyesyesyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava
Server operating systemsLinux
OS X
Unix
hostedhostedhosted
Data schemeyes infoschema-less columns are supportedschema-freeschema-freeFixed schema with schema-less datatypes (dynamic)
Typing infopredefined data types such as float or dateyesnoyesyes 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.nononoyes
Secondary indexesyesnoyesall fields are automatically indexed
SQL infoSupport of SQLSQL for queryingnonoKusto Query Language (KQL), SQL subset
APIs and other access methodsJDBC
RESTful HTTP/JSON API
gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
Android
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
C#
C++
Go
Java
JavaScript (Node.js)
Python
Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Server-side scripts infoStored proceduresnonoyes, Firebase Rules & Cloud FunctionsYes, possible languages: KQL, Python, R
Triggersnonoyes, with Cloud Functionsyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicy
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedShardingShardingSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesInternal replication in Colossus, and regional replication between two clusters in different zonesMulti-source replicationyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesUsing Cloud DataflowSpark connector (open source): github.com/­Azure/­azure-kusto-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Immediate ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoAtomic single-row operationsyesno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
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.nonono
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access 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 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
Apache DruidGoogle Cloud BigtableGoogle Cloud FirestoreMicrosoft Azure Data Explorer
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

Apache Druid Wins Best Big Data Product in the 2023 BigDATAwire Readers' Choice Awards
26 January 2024, Datanami

'Lucifer' Botnet Turns Up the Heat on Apache Hadoop Servers
21 February 2024, Dark Reading

New DDoS malware Attacking Apache big-data stack, Hadoop, & Druid Servers
26 February 2024, GBHackers

Apache Druid Takes Its Place In The Pantheon Of Databases
16 June 2022, The Next Platform

How to connect DataGrip to Apache Druid | by Zisis Flokas
18 October 2021, Towards Data Science

provided by Google News

Google says it'll stop charging fees to transfer data out of Google Cloud
11 January 2024, TechCrunch

Google Introduces Autoscaling for Cloud Bigtable for Optimizing Costs
31 January 2022, InfoQ.com

Google scales up Cloud Bigtable NoSQL database
27 January 2022, TechTarget

Review: Google Bigtable scales with ease
7 September 2016, InfoWorld

Google introduces Cloud Bigtable managed NoSQL database to process data at scale
6 May 2015, VentureBeat

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, Microsoft

Update records in a Kusto Database (public preview) | Azure updates
20 February 2024, Microsoft

Public Preview: Azure Data Explorer connector for Apache Flink | Azure updates
8 January 2024, Microsoft

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

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

provided by Google News



Share this page

Featured Products

Milvus logo

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

Neo4j logo

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

Present your product here