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DBMS > Apache IoTDB vs. EsgynDB vs. Firebase Realtime Database vs. Google Cloud Bigtable vs. Kyligence Enterprise

System Properties Comparison Apache IoTDB vs. EsgynDB vs. Firebase Realtime Database vs. Google Cloud Bigtable vs. Kyligence Enterprise

Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonEsgynDB  Xexclude from comparisonFirebase Realtime Database  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonKyligence Enterprise  Xexclude from comparison
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 FlinkEnterprise-class SQL-on-Hadoop solution, powered by Apache TrafodionCloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.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 analytics engine for big data, built on top of Apache Kylin
Primary database modelTime Series DBMSRelational DBMSDocument storeKey-value store
Wide column store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.31
Rank#159  Overall
#14  Time Series DBMS
Score0.15
Rank#325  Overall
#144  Relational DBMS
Score13.60
Rank#39  Overall
#6  Document stores
Score2.97
Rank#92  Overall
#15  Key-value stores
#8  Wide column stores
Score0.44
Rank#259  Overall
#118  Relational DBMS
Websiteiotdb.apache.orgwww.esgyn.cnfirebase.google.com/­products/­realtime-databasecloud.google.com/­bigtablekyligence.io/­kyligence-enterprise
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlfirebase.google.com/­docs/­databasecloud.google.com/­bigtable/­docs
DeveloperApache Software FoundationEsgynGoogle infoacquired by Google 2014GoogleKyligence, Inc.
Initial release20182015201220152016
Current release1.1.0, April 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialcommercialcommercialcommercial
Cloud-based only infoOnly available as a cloud servicenonoyesyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++, JavaJava
Server operating systemsAll OS with a Java VM (>= 1.8)LinuxhostedhostedLinux
Data schemeyesyesschema-freeschema-freeyes
Typing infopredefined data types such as float or dateyesyesyesnoyes
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.nonononono
Secondary indexesyesyesyesnoyes
SQL infoSupport of SQLSQL-like query languageyesnonoANSI SQL for queries (using Apache Calcite)
APIs and other access methodsJDBC
Native API
ADO.NET
JDBC
ODBC
Android
iOS
JavaScript API
RESTful HTTP API
gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
JDBC
ODBC
RESTful HTTP API
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
All languages supporting JDBC/ODBC/ADO.NetJava
JavaScript
Objective-C
C#
C++
Go
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresyesJava Stored Procedureslimited functionality with using 'rules'no
TriggersyesnoCallbacks are triggered when data changesno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)ShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasMulti-source replication between multi datacentersInternal replication in Colossus, and regional replication between two clusters in different zones
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparkyesnoyesyes
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate ConsistencyEventual Consistency infoif the client is offline
Immediate Consistency infoif the client is online
Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Immediate Consistency
Foreign keys infoReferential integritynoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDyesAtomic single-row operations
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnonono
User concepts infoAccess controlyesfine grained access rights according to SQL-standardyes, based on authentication and database rulesAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

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More resources
Apache IoTDBEsgynDBFirebase Realtime DatabaseGoogle Cloud BigtableKyligence Enterprise
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