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DBMS > Apache Druid vs. Ignite vs. Microsoft Azure Data Explorer vs. Riak TS vs. Titan

System Properties Comparison Apache Druid vs. Ignite vs. Microsoft Azure Data Explorer vs. Riak TS vs. Titan

Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonIgnite  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonRiak TS  Xexclude from comparisonTitan  Xexclude from comparison
Titan has been decommisioned after the takeover by Datastax. It will be removed from the DB-Engines ranking. A fork has been open-sourced as JanusGraph.
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataApache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.Fully managed big data interactive analytics platformRiak TS is a distributed NoSQL database optimized for time series data and based on Riak KVTitan is a Graph DBMS optimized for distributed clusters.
Primary database modelRelational DBMS
Time Series DBMS
Key-value store
Relational DBMS
Relational DBMS infocolumn orientedTime Series DBMSGraph DBMS
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
Score3.11
Rank#96  Overall
#15  Key-value stores
#49  Relational DBMS
Score3.80
Rank#81  Overall
#43  Relational DBMS
Score0.28
Rank#307  Overall
#27  Time Series DBMS
Websitedruid.apache.orgignite.apache.orgazure.microsoft.com/­services/­data-explorergithub.com/­thinkaurelius/­titan
Technical documentationdruid.apache.org/­docs/­latest/­designapacheignite.readme.io/­docsdocs.microsoft.com/­en-us/­azure/­data-explorerwww.tiot.jp/­riak-docs/­riak/­ts/­latestgithub.com/­thinkaurelius/­titan/­wiki
DeveloperApache Software Foundation and contributorsApache Software FoundationMicrosoftOpen Source, formerly Basho TechnologiesAurelius, owned by DataStax
Initial release20122015201920152012
Current release29.0.1, April 2024Apache Ignite 2.6cloud service with continuous releases3.0.0, September 2022
License infoCommercial or Open SourceOpen Source infoApache license v2Open Source infoApache 2.0commercialOpen SourceOpen Source infoApache license, version 2.0
Cloud-based only infoOnly available as a cloud servicenonoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++, Java, .NetErlangJava
Server operating systemsLinux
OS X
Unix
Linux
OS X
Solaris
Windows
hostedLinux
OS X
Linux
OS X
Unix
Windows
Data schemeyes infoschema-less columns are supportedyesFixed schema with schema-less datatypes (dynamic)schema-freeyes
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-typesnoyes
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.noyesyesno
Secondary indexesyesyesall fields are automatically indexedrestrictedyes
SQL infoSupport of SQLSQL for queryingANSI-99 for query and DML statements, subset of DDLKusto Query Language (KQL), SQL subsetyes, limitedno
APIs and other access methodsJDBC
RESTful HTTP/JSON API
HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
HTTP API
Native Erlang Interface
Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
C#
C++
Java
PHP
Python
Ruby
Scala
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C infounofficial client library
C#
C++ infounofficial client library
Clojure infounofficial client library
Dart infounofficial client library
Erlang
Go infounofficial client library
Groovy infounofficial client library
Haskell infounofficial client library
Java
JavaScript infounofficial client library
Lisp infounofficial client library
Perl infounofficial client library
PHP
Python
Ruby
Scala infounofficial client library
Smalltalk infounofficial client library
Clojure
Java
Python
Server-side scripts infoStored proceduresnoyes (compute grid and cache interceptors can be used instead)Yes, possible languages: KQL, Python, RErlangyes
Triggersnoyes (cache interceptors and events)yes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyes infopre-commit hooks and post-commit hooksyes
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedShardingSharding infoImplicit feature of the cloud serviceShardingyes infovia pluggable storage backends
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesyes (replicated cache)yes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.selectable replication factoryes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes (compute grid and hadoop accelerator)Spark connector (open source): github.com/­Azure/­azure-kusto-sparkyesyes infovia Faunus, a graph analytics engine
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyEventual Consistency
Immediate Consistency
Eventual ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynononono infolinks between datasets can be storedyes infoRelationships in graph
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnonoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes infoSupports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesno
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemSecurity Hooks for custom implementationsAzure Active Directory AuthenticationnoUser authentification and security via Rexster Graph Server

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Apache DruidIgniteMicrosoft Azure Data ExplorerRiak TSTitan
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