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DBMS > Databricks vs. Fujitsu Enterprise Postgres vs. Microsoft Azure Synapse Analytics vs. Sphinx vs. WakandaDB

System Properties Comparison Databricks vs. Fujitsu Enterprise Postgres vs. Microsoft Azure Synapse Analytics vs. Sphinx vs. WakandaDB

Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonFujitsu Enterprise Postgres  Xexclude from comparisonMicrosoft Azure Synapse Analytics infopreviously named Azure SQL Data Warehouse  Xexclude from comparisonSphinx  Xexclude from comparisonWakandaDB  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-grade PostgreSQL-based DBMS with security enhancements such as Transparent Data Encryption and Data Masking, plus high-availability and performance improvement features.Elastic, large scale data warehouse service leveraging the broad eco-system of SQL ServerOpen source search engine for searching in data from different sources, e.g. relational databasesWakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data
Primary database modelDocument store
Relational DBMS
Relational DBMSRelational DBMSSearch engineObject oriented DBMS
Secondary database modelsDocument store
Spatial 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
Score0.37
Rank#278  Overall
#128  Relational DBMS
Score19.93
Rank#31  Overall
#19  Relational DBMS
Score5.95
Rank#55  Overall
#5  Search engines
Score0.10
Rank#356  Overall
#16  Object oriented DBMS
Websitewww.databricks.comwww.postgresql.fastware.comazure.microsoft.com/­services/­synapse-analyticssphinxsearch.comwakanda.github.io
Technical documentationdocs.databricks.comwww.postgresql.fastware.com/­product-manualsdocs.microsoft.com/­azure/­synapse-analyticssphinxsearch.com/­docswakanda.github.io/­doc
DeveloperDatabricksPostgreSQL Global Development Group, Fujitsu Australia Software TechnologyMicrosoftSphinx Technologies Inc.Wakanda SAS
Initial release2013201620012012
Current releaseFujitsu Enterprise Postgres 14, January 20223.5.1, February 20232.7.0 (April 29, 2019), April 2019
License infoCommercial or Open SourcecommercialcommercialcommercialOpen Source infoGPL version 2, commercial licence availableOpen Source infoAGPLv3, extended commercial license available
Cloud-based only infoOnly available as a cloud serviceyesnoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageCC++C++C++, JavaScript
Server operating systemshostedLinux
Windows
hostedFreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Linux
OS X
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)yesyesyesyes
Typing infopredefined data types such as float or dateyesyesnoyes
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.yesnono
Secondary indexesyesyesyesyes infofull-text index on all search fields
SQL infoSupport of SQLwith Databricks SQLyesyesSQL-like query language (SphinxQL)no
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
ADO.NET
JDBC
ODBC
Proprietary protocolRESTful HTTP API
Supported programming languagesPython
R
Scala
.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
C#
Java
PHP
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
JavaScript
Server-side scripts infoStored proceduresuser defined functions and aggregatesuser defined functionsTransact SQLnoyes
Triggersyesnonoyes
Partitioning methods infoMethods for storing different data on different nodespartitioning by range, list and by hashSharding, horizontal partitioningSharding infoPartitioning is done manually, search queries against distributed index is supportednone
Replication methods infoMethods for redundantly storing data on multiple nodesyesSource-replica replicationyesnonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyesno infodocs.microsoft.com/­en-us/­azure/­synapse-analytics/­sql-data-warehouse/­sql-data-warehouse-table-constraintsno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDACIDnoACID
Concurrency infoSupport for concurrent manipulation of datayesyes, multi-version concurrency control (MVCC)yesyesyes
Durability infoSupport for making data persistentyesyesyesyes infoThe original contents of fields are not stored in the Sphinx index.yes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nono
User concepts infoAccess controlfine grained access rights according to SQL-standardyesnoyes
More information provided by the system vendor
DatabricksFujitsu Enterprise PostgresMicrosoft Azure Synapse Analytics infopreviously named Azure SQL Data WarehouseSphinxWakandaDB
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
DatabricksFujitsu Enterprise PostgresMicrosoft Azure Synapse Analytics infopreviously named Azure SQL Data WarehouseSphinxWakandaDB
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