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DBMS > Databricks vs. Hypertable vs. Ignite

System Properties Comparison Databricks vs. Hypertable vs. Ignite

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Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonHypertable  Xexclude from comparisonIgnite  Xexclude from comparison
Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.
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.An open source BigTable implementation based on distributed file systems such as HadoopApache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.
Primary database modelDocument store
Relational DBMS
Wide column storeKey-value store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score76.33
Rank#17  Overall
#3  Document stores
#11  Relational DBMS
Score3.64
Rank#89  Overall
#13  Key-value stores
#48  Relational DBMS
Websitewww.databricks.comignite.apache.org
Technical documentationdocs.databricks.comapacheignite.readme.io/­docs
DeveloperDatabricksHypertable Inc.Apache Software Foundation
Initial release201320092015
Current release0.9.8.11, March 2016Apache Ignite 2.6
License infoCommercial or Open SourcecommercialOpen Source infoGNU version 3. Commercial license availableOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud serviceyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++C++, Java, .Net
Server operating systemshostedLinux
OS X
Windows infoan inofficial Windows port is available
Linux
OS X
Solaris
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)schema-freeyes
Typing infopredefined data types such as float or datenoyes
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.yesyes
Secondary indexesyesrestricted infoonly exact value or prefix value scansyes
SQL infoSupport of SQLwith Databricks SQLnoANSI-99 for query and DML statements, subset of DDL
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
C++ API
Thrift
HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
Supported programming languagesPython
R
Scala
C++
Java
Perl
PHP
Python
Ruby
C#
C++
Java
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresuser defined functions and aggregatesnoyes (compute grid and cache interceptors can be used instead)
Triggersnoyes (cache interceptors and events)
Partitioning methods infoMethods for storing different data on different nodesShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyesselectable replication factor on file system levelyes (replicated cache)
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesyes (compute grid and hadoop accelerator)
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes
User concepts infoAccess controlnoSecurity Hooks for custom implementations
More information provided by the system vendor
DatabricksHypertableIgnite
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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