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DBMS > Databricks vs. EXASOL vs. HugeGraph vs. Spark SQL

System Properties Comparison Databricks vs. EXASOL vs. HugeGraph vs. Spark SQL

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Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonEXASOL  Xexclude from comparisonHugeGraph  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionThe 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.High-performance, in-memory, MPP database specifically designed for in-memory analytics.A fast-speed and highly-scalable Graph DBMSSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelDocument store
Relational DBMS
Relational DBMSGraph DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score81.08
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score1.76
Rank#139  Overall
#62  Relational DBMS
Score0.17
Rank#335  Overall
#31  Graph DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websitewww.databricks.comwww.exasol.comgithub.com/­hugegraph
hugegraph.apache.org
spark.apache.org/­sql
Technical documentationdocs.databricks.comwww.exasol.com/­resourceshugegraph.apache.org/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperDatabricksExasolBaiduApache Software Foundation
Initial release2013200020182014
Current release0.93.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache Version 2.0Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaScala
Server operating systemshostedLinux
macOS
Unix
Linux
OS X
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)yesyesyes
Typing infopredefined data types such as float or dateyesyesyes
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.yesnonono
Secondary indexesyesyesyes infoalso supports composite index and range indexno
SQL infoSupport of SQLwith Databricks SQLyesnoSQL-like DML and DDL statements
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
.Net
JDBC
ODBC
WebSocket
Java API
RESTful HTTP API
TinkerPop Gremlin
JDBC
ODBC
Supported programming languagesPython
R
Scala
Java
Lua
Python
R
Groovy
Java
Python
Java
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functions and aggregatesuser defined functionsasynchronous Gremlin script jobsno
Triggersyesnono
Partitioning methods infoMethods for storing different data on different nodesShardingyes infodepending on used storage backend, e.g. Cassandra and HBaseyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesyesyes infodepending on used storage backend, e.g. Cassandra and HBasenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoHadoop integrationvia hugegraph-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integrityyesyes infoedges in graphno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDACIDno
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.noyesyesno
User concepts infoAccess controlAccess rights for users, groups and roles according to SQL-standardUsers, roles and permissionsno
More information provided by the system vendor
DatabricksEXASOLHugeGraphSpark SQL
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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DatabricksEXASOLHugeGraphSpark SQL
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