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DBMS > CrateDB vs. Databricks vs. GridGain vs. Microsoft Azure Data Explorer vs. Sphinx

System Properties Comparison CrateDB vs. Databricks vs. GridGain vs. Microsoft Azure Data Explorer vs. Sphinx

Editorial information provided by DB-Engines
NameCrateDB  Xexclude from comparisonDatabricks  Xexclude from comparisonGridGain  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonSphinx  Xexclude from comparison
DescriptionDistributed Database based on LuceneThe 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.GridGain is an in-memory computing platform, built on Apache IgniteFully managed big data interactive analytics platformOpen source search engine for searching in data from different sources, e.g. relational databases
Primary database modelDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Document store
Relational DBMS
Columnar
Key-value store
Object oriented DBMS
Relational DBMS
Relational DBMS infocolumn orientedSearch engine
Secondary database modelsRelational DBMSDocument store infoIf a column is of type dynamic docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-types/­dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store infothis is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine infosupport for complex search expressions docs.microsoft.com/­en-us/­azure/­kusto/­query/­parseoperator FTS, Geospatial docs.microsoft.com/­en-us/­azure/­kusto/­query/­geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS infosee docs.microsoft.com/­en-us/­azure/­data-explorer/­time-series-analysis
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.69
Rank#226  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#11  Vector DBMS
Score84.24
Rank#14  Overall
#2  Document stores
#9  Relational DBMS
Score1.48
Rank#150  Overall
#1  Columnar
#26  Key-value stores
#2  Object oriented DBMS
#69  Relational DBMS
Score3.28
Rank#83  Overall
#45  Relational DBMS
Score5.97
Rank#56  Overall
#5  Search engines
Websitecratedb.comwww.databricks.comwww.gridgain.comazure.microsoft.com/­services/­data-explorersphinxsearch.com
Technical documentationcratedb.com/­docsdocs.databricks.comwww.gridgain.com/­docs/­index.htmldocs.microsoft.com/­en-us/­azure/­data-explorersphinxsearch.com/­docs
DeveloperCrateDatabricksGridGain Systems, Inc.MicrosoftSphinx Technologies Inc.
Initial release20132013200720192001
Current release5.8.1, August 2024GridGain 8.5.1cloud service with continuous releases3.5.1, February 2023
License infoCommercial or Open SourceOpen Sourcecommercialcommercial, open sourcecommercialOpen Source infoGPL version 2, commercial licence available
Cloud-based only infoOnly available as a cloud servicenoyesnoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJava, C++, .Net, Python, REST, SQLC++
Server operating systemsAll Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supporthostedLinux
OS X
Solaris
Windows
z/OS
hostedFreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)Flexible Schema (defined schema, partial schema, schema free)yesFixed schema with schema-less datatypes (dynamic)yes
Typing infopredefined data types such as float or dateyesyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesno
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.noyesyesyes
Secondary indexesyesyesyesall fields are automatically indexedyes infofull-text index on all search fields
SQL infoSupport of SQLyes, but no triggers and constraints, and PostgreSQL compatibilitywith Databricks SQLANSI-99 for query and DML statements, subset of DDLKusto Query Language (KQL), SQL subsetSQL-like query language (SphinxQL)
APIs and other access methodsADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
JDBC
ODBC
RESTful HTTP API
HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Proprietary protocol
Supported programming languages.NET
Erlang
Go infocommunity maintained client
Java
JavaScript (Node.js) infocommunity maintained client
Perl infocommunity maintained client
PHP
Python
R
Ruby infocommunity maintained client
Scala infocommunity maintained client
Python
R
Scala
C#
C++
Java
PHP
Python
Ruby
Scala
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Server-side scripts infoStored proceduresuser defined functions (Javascript)user defined functions and aggregatesyes (compute grid and cache interceptors can be used instead)Yes, possible languages: KQL, Python, Rno
Triggersnoyes (cache interceptors and events)yes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding infoImplicit feature of the cloud serviceSharding infoPartitioning is done manually, search queries against distributed index is supported
Replication methods infoMethods for redundantly storing data on multiple nodesConfigurable replication on table/partition-levelyesyes (replicated cache)yes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.none
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes (compute grid and hadoop accelerator)Spark connector (open source): github.com/­Azure/­azure-kusto-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Read-after-write consistency on record level
Immediate ConsistencyImmediate ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datano infounique row identifiers can be used for implementing an optimistic concurrency control strategyACIDACIDnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes infoThe original contents of fields are not stored in the Sphinx index.
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyesno
User concepts infoAccess controlrights management via user accountsRole-based access control
Security Hooks for custom implementations
Azure Active Directory Authenticationno
More information provided by the system vendor
CrateDBDatabricksGridGainMicrosoft Azure Data ExplorerSphinx
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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CrateDBDatabricksGridGainMicrosoft Azure Data ExplorerSphinx
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