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DBMS > Databricks vs. Hawkular Metrics vs. Microsoft Azure Table Storage vs. Oracle Berkeley DB

System Properties Comparison Databricks vs. Hawkular Metrics vs. Microsoft Azure Table Storage vs. Oracle Berkeley DB

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Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonHawkular Metrics  Xexclude from comparisonMicrosoft Azure Table Storage  Xexclude from comparisonOracle Berkeley DB  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.Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.A Wide Column Store for rapid development using massive semi-structured datasetsWidely used in-process key-value store
Primary database modelDocument store
Relational DBMS
Time Series DBMSWide column storeKey-value store infosupports sorted and unsorted key sets
Native XML DBMS infoin the Oracle Berkeley DB XML version
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score78.61
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score0.00
Rank#379  Overall
#40  Time Series DBMS
Score4.48
Rank#75  Overall
#6  Wide column stores
Score2.21
Rank#117  Overall
#20  Key-value stores
#3  Native XML DBMS
Websitewww.databricks.comwww.hawkular.orgazure.microsoft.com/­en-us/­services/­storage/­tableswww.oracle.com/­database/­technologies/­related/­berkeleydb.html
Technical documentationdocs.databricks.comwww.hawkular.org/­hawkular-metrics/­docs/­user-guidedocs.oracle.com/­cd/­E17076_05/­html/­index.html
DeveloperDatabricksCommunity supported by Red HatMicrosoftOracle infooriginally developed by Sleepycat, which was acquired by Oracle
Initial release2013201420121994
Current release18.1.40, May 2020
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0commercialOpen Source infocommercial license available
Cloud-based only infoOnly available as a cloud serviceyesnoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC, Java, C++ (depending on the Berkeley DB edition)
Server operating systemshostedLinux
OS X
Windows
hostedAIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)schema-freeschema-freeschema-free
Typing infopredefined data types such as float or dateyesyesno
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 infoonly with the Berkeley DB XML edition
Secondary indexesyesnonoyes
SQL infoSupport of SQLwith Databricks SQLnonoyes infoSQL interfaced based on SQLite is available
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
HTTP RESTRESTful HTTP API
Supported programming languagesPython
R
Scala
Go
Java
Python
Ruby
.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net infoFigaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others infoThird-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js) info3rd party binding
Perl
Python
Tcl
Server-side scripts infoStored proceduresuser defined functions and aggregatesnonono
Triggersyes infovia Hawkular Alertingnoyes infoonly for the SQL API
Partitioning methods infoMethods for storing different data on different nodesSharding infobased on CassandraSharding infoImplicit feature of the cloud servicenone
Replication methods infoMethods for redundantly storing data on multiple nodesyesselectable replication factor infobased on Cassandrayes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency infobased on Cassandra
Immediate Consistency infobased on Cassandra
Immediate Consistency
Foreign keys infoReferential integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnooptimistic lockingACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes
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.nononoyes
User concepts infoAccess controlnoAccess rights based on private key authentication or shared access signaturesno
More information provided by the system vendor
DatabricksHawkular MetricsMicrosoft Azure Table StorageOracle Berkeley DB
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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