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DBMS > Databend vs. EXASOL vs. Hazelcast vs. Spark SQL

System Properties Comparison Databend vs. EXASOL vs. Hazelcast vs. Spark SQL

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Editorial information provided by DB-Engines
NameDatabend  Xexclude from comparisonEXASOL  Xexclude from comparisonHazelcast  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionAn open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexityHigh-performance, in-memory, MPP database specifically designed for in-memory analytics.A widely adopted in-memory data gridSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSRelational DBMSKey-value storeRelational DBMS
Secondary database modelsDocument store infoJSON support with IMDG 3.12
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.30
Rank#287  Overall
#130  Relational DBMS
Score1.99
Rank#124  Overall
#58  Relational DBMS
Score5.97
Rank#57  Overall
#6  Key-value stores
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websitegithub.com/­datafuselabs/­databend
www.databend.com
www.exasol.comhazelcast.comspark.apache.org/­sql
Technical documentationdocs.databend.comwww.exasol.com/­resourceshazelcast.org/­imdg/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperDatabend LabsExasolHazelcastApache Software Foundation
Initial release2021200020082014
Current release1.0.59, April 20235.3.6, November 20233.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Source infoApache Version 2; commercial licenses availableOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageRustJavaScala
Server operating systemshosted
Linux
macOS
All OS with a Java VMLinux
OS X
Windows
Data schemeyesyesschema-freeyes
Typing infopredefined data types such as float or dateyesyesyesyes
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.nonoyes infothe object must implement a serialization strategyno
Secondary indexesnoyesyesno
SQL infoSupport of SQLyesyesSQL-like query languageSQL-like DML and DDL statements
APIs and other access methodsCLI Client
JDBC
RESTful HTTP API
.Net
JDBC
ODBC
WebSocket
JCache
JPA
Memcached protocol
RESTful HTTP API
JDBC
ODBC
Supported programming languagesGo
Java
JavaScript (Node.js)
Python
Rust
Java
Lua
Python
R
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
Java
Python
R
Scala
Server-side scripts infoStored proceduresnouser defined functionsyes infoEvent Listeners, Executor Servicesno
Triggersnoyesyes infoEventsno
Partitioning methods infoMethods for storing different data on different nodesnoneShardingShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesnoneyes infoReplicated Mapnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infoHadoop integrationyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus Algorithm
Foreign keys infoReferential integritynoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datayesACIDone or two-phase-commit; repeatable reads; read commitedno
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.yesyesno
User concepts infoAccess controlUsers with fine-grained authorization concept, user rolesAccess rights for users, groups and roles according to SQL-standardRole-based access controlno

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DatabendEXASOLHazelcastSpark SQL
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