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DBMS > Databricks vs. EsgynDB vs. Google Cloud Bigtable vs. Hazelcast vs. Riak KV

System Properties Comparison Databricks vs. EsgynDB vs. Google Cloud Bigtable vs. Hazelcast vs. Riak KV

Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonEsgynDB  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonHazelcast  Xexclude from comparisonRiak KV  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.Enterprise-class SQL-on-Hadoop solution, powered by Apache TrafodionGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.A widely adopted in-memory data gridDistributed, fault tolerant key-value store
Primary database modelDocument store
Relational DBMS
Relational DBMSKey-value store
Wide column store
Key-value storeKey-value store infowith links between data sets and object tags for the creation of secondary indexes
Secondary database modelsDocument store infoJSON support with IMDG 3.12
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score81.08
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score0.25
Rank#312  Overall
#138  Relational DBMS
Score3.15
Rank#95  Overall
#14  Key-value stores
#8  Wide column stores
Score5.46
Rank#61  Overall
#7  Key-value stores
Score4.01
Rank#79  Overall
#9  Key-value stores
Websitewww.databricks.comwww.esgyn.cncloud.google.com/­bigtablehazelcast.com
Technical documentationdocs.databricks.comcloud.google.com/­bigtable/­docshazelcast.org/­imdg/­docswww.tiot.jp/­riak-docs/­riak/­kv/­latest
DeveloperDatabricksEsgynGoogleHazelcastOpenSource, formerly Basho Technologies
Initial release20132015201520082009
Current release5.3.6, November 20233.2.0, December 2022
License infoCommercial or Open SourcecommercialcommercialcommercialOpen Source infoApache Version 2; commercial licenses availableOpen Source infoApache version 2, commercial enterprise edition
Cloud-based only infoOnly available as a cloud serviceyesnoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++, JavaJavaErlang
Server operating systemshostedLinuxhostedAll OS with a Java VMLinux
OS X
Data schemeFlexible Schema (defined schema, partial schema, schema free)yesschema-freeschema-freeschema-free
Typing infopredefined data types such as float or dateyesnoyesno
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.yesnonoyes infothe object must implement a serialization strategyno
Secondary indexesyesyesnoyesrestricted
SQL infoSupport of SQLwith Databricks SQLyesnoSQL-like query languageno
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
ADO.NET
JDBC
ODBC
gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
JCache
JPA
Memcached protocol
RESTful HTTP API
HTTP API
Native Erlang Interface
Supported programming languagesPython
R
Scala
All languages supporting JDBC/ODBC/ADO.NetC#
C++
Go
Java
JavaScript (Node.js)
Python
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
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
Server-side scripts infoStored proceduresuser defined functions and aggregatesJava Stored Proceduresnoyes infoEvent Listeners, Executor ServicesErlang
Triggersnonoyes infoEventsyes infopre-commit hooks and post-commit hooks
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingSharding infono "single point of failure"
Replication methods infoMethods for redundantly storing data on multiple nodesyesMulti-source replication between multi datacentersInternal replication in Colossus, and regional replication between two clusters in different zonesyes infoReplicated Mapselectable replication factor
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesyesyesyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Immediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmEventual Consistency
Foreign keys infoReferential integrityyesnonono infolinks between data sets can be stored
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDAtomic single-row operationsone or two-phase-commit; repeatable reads; read commitedno
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.nononoyes
User concepts infoAccess controlfine grained access rights according to SQL-standardAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Role-based access controlyes, using Riak Security
More information provided by the system vendor
DatabricksEsgynDBGoogle Cloud BigtableHazelcastRiak KV
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
DatabricksEsgynDBGoogle Cloud BigtableHazelcastRiak KV
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