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DBMS > Apache IoTDB vs. FoundationDB vs. Google Cloud Bigtable vs. Graph Engine vs. Microsoft Azure Data Explorer

System Properties Comparison Apache IoTDB vs. FoundationDB vs. Google Cloud Bigtable vs. Graph Engine vs. Microsoft Azure Data Explorer

Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonFoundationDB  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonGraph Engine infoformer name: Trinity  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparison
Created as commercial project in 2013, FoundationDB has been acquired by Apple in March 2015 and was withdrawn from the market. As a consequence, the product was removed from the DB-Engines ranking. In April 2018, Apple open-sourced FoundationDB and it therefore reappears in the ranking.
DescriptionAn IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and FlinkOrdered key-value store. Core features are complimented by layers.Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engineFully managed big data interactive analytics platform
Primary database modelTime Series DBMSDocument store infosupported via specific layer
Key-value store
Relational DBMS infosupported via specific SQL-layer
Key-value store
Wide column store
Graph DBMS
Key-value store
Relational 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
Score1.18
Rank#173  Overall
#15  Time Series DBMS
Score1.03
Rank#190  Overall
#31  Document stores
#28  Key-value stores
#89  Relational DBMS
Score3.26
Rank#92  Overall
#13  Key-value stores
#8  Wide column stores
Score0.61
Rank#240  Overall
#21  Graph DBMS
#35  Key-value stores
Score4.38
Rank#77  Overall
#41  Relational DBMS
Websiteiotdb.apache.orggithub.com/­apple/­foundationdbcloud.google.com/­bigtablewww.graphengine.ioazure.microsoft.com/­services/­data-explorer
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlapple.github.io/­foundationdbcloud.google.com/­bigtable/­docswww.graphengine.io/­docs/­manualdocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperApache Software FoundationFoundationDBGoogleMicrosoftMicrosoft
Initial release20182013201520102019
Current release1.1.0, April 20236.2.28, November 2020cloud service with continuous releases
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoApache 2.0commercialOpen Source infoMIT Licensecommercial
Cloud-based only infoOnly available as a cloud servicenonoyesnoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC++.NET and C
Server operating systemsAll OS with a Java VM (>= 1.8)Linux
OS X
Windows
hosted.NEThosted
Data schemeyesschema-free infosome layers support schemasschema-freeyesFixed schema with schema-less datatypes (dynamic)
Typing infopredefined data types such as float or dateyesno infosome layers support typingnoyesyes 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 indexesyesnonoall fields are automatically indexed
SQL infoSupport of SQLSQL-like query languagesupported in specific SQL layer onlynonoKusto Query Language (KQL), SQL subset
APIs and other access methodsJDBC
Native API
gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
RESTful HTTP APIMicrosoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
.Net
C
C++
Go
Java
JavaScript infoNode.js
PHP
Python
Ruby
Swift
C#
C++
Go
Java
JavaScript (Node.js)
Python
C#
C++
F#
Visual Basic
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Server-side scripts infoStored proceduresyesin SQL-layer onlynoyesYes, possible languages: KQL, Python, R
Triggersyesnononoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicy
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)ShardingShardinghorizontal partitioningSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasyesInternal replication in Colossus, and regional replication between two clusters in different zonesyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and SparknoyesSpark connector (open source): github.com/­Azure/­azure-kusto-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Linearizable consistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Eventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynoin SQL-layer onlynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDAtomic single-row operationsnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesoptional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storageyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyesno
User concepts infoAccess controlyesnoAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Azure Active Directory Authentication

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More resources
Apache IoTDBFoundationDBGoogle Cloud BigtableGraph Engine infoformer name: TrinityMicrosoft Azure Data Explorer
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