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DBMS > Apache Phoenix vs. Databricks vs. Splunk vs. Tarantool

System Properties Comparison Apache Phoenix vs. Databricks vs. Splunk vs. Tarantool

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Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonDatabricks  Xexclude from comparisonSplunk  Xexclude from comparisonTarantool  Xexclude from comparison
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseThe Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.Analytics Platform for Big DataIn-memory computing platform with a flexible data schema for efficiently building high-performance applications
Primary database modelRelational DBMSDocument store
Relational DBMS
Search engineDocument store
Key-value store
Relational DBMS
Secondary database modelsSpatial DBMS infowith Tarantool/GIS extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.97
Rank#126  Overall
#59  Relational DBMS
Score78.61
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score86.45
Rank#14  Overall
#2  Search engines
Score1.72
Rank#144  Overall
#25  Document stores
#25  Key-value stores
#66  Relational DBMS
Websitephoenix.apache.orgwww.databricks.comwww.splunk.comwww.tarantool.io
Technical documentationphoenix.apache.orgdocs.databricks.comdocs.splunk.com/­Documentation/­Splunkwww.tarantool.io/­en/­doc
DeveloperApache Software FoundationDatabricksSplunk Inc.VK
Initial release2014201320032008
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20192.10.0, May 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialcommercial infoLimited free edition and free developer edition availableOpen Source infoBSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC and C++
Server operating systemsLinux
Unix
Windows
hostedLinux
OS X
Solaris
Windows
BSD
Linux
macOS
Data schemeyes infolate-bound, schema-on-read capabilitiesFlexible Schema (defined schema, partial schema, schema free)yesFlexible data schema: relational definition for tables with ability to store json-like documents in columns
Typing infopredefined data types such as float or dateyesyesstring, double, decimal, uuid, integer, blob, boolean, datetime
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 indexesyesyesyesyes
SQL infoSupport of SQLyeswith Databricks SQLno infoSplunk Search Processing Language for search commandsFull-featured ANSI SQL support
APIs and other access methodsJDBCJDBC
ODBC
RESTful HTTP API
HTTP RESTOpen binary protocol
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
Python
R
Scala
C#
Java
JavaScript
PHP
Python
Ruby
C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust
Server-side scripts infoStored proceduresuser defined functionsuser defined functions and aggregatesyesLua, C and SQL stored procedures
Triggersnoyesyes, before/after data modification events, on replication events, client session events
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
yesMulti-source replicationAsynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyImmediate ConsistencyEventual ConsistencyCasual consistency across sharding partitions
Eventual consistency within replicaset partition infowhen using asyncronous replication
Immediate Consistency within single instance
Sequential consistency including linearizable read within replicaset partition infowhen using Raft
Foreign keys infoReferential integritynonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDno infoA 'Transaction' in Splunk has a different meaning: grouping related events into a single one for later searchingACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, cooperative multitasking
Durability infoSupport for making data persistentyesyesyesyes, write ahead logging
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnonoyes, full featured in-memory storage engine with persistence
User concepts infoAccess controlAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancyAccess rights for users and rolesAccess Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles
More information provided by the system vendor
Apache PhoenixDatabricksSplunkTarantool
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
Apache PhoenixDatabricksSplunkTarantool
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